Journal articles on the topic 'Music Performance Classification Data processing'
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1
Sudarma, Made, and I. Gede Harsemadi. "Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 1 (February 1, 2017): 486. http://dx.doi.org/10.11591/ijece.v7i1.pp486-495.
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Each of music which has been created, has its own mood which is emitted, therefore, there has been many researches in Music Information Retrieval (MIR) field that has been done for recognition of mood to music. This research produced software to classify music to the mood by using K-Nearest Neighbor and ID3 algorithm. In this research accuracy performance comparison and measurement of average classification time is carried out which is obtained based on the value produced from music feature extraction process. For music feature extraction process it uses 9 types of spectral analysis, consists of 400 practicing data and 400 testing data. The system produced outcome as classification label of mood type those are contentment, exuberance, depression and anxious. Classification by using algorithm of KNN is good enough that is 86.55% at k value = 3 and average processing time is 0.01021. Whereas by using ID3 it results accuracy of 59.33% and average of processing time is 0.05091 second.
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Manoharan, J. Samuel. "Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing." December 2021 3, no. 4 (December 24, 2021): 365–74. http://dx.doi.org/10.36548/jaicn.2021.4.008.
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Sound event detection, speech emotion classification, music classification, acoustic scene classification, audio tagging and several other audio pattern recognition applications are largely dependent on the growing machine learning technology. The audio pattern recognition issues are also addressed by neural networks in recent days. The existing systems operate within limited durations on specific datasets. Pretrained systems with large datasets in natural language processing and computer vision applications over the recent years perform well in several tasks. However, audio pattern recognition research with large-scale datasets is limited in the current scenario. In this paper, a large-scale audio dataset is used for training a pre-trained audio neural network. Several audio related tasks are performed by transferring this audio neural network. Several convolution neural networks are used for modeling the proposed audio neural network. The computational complexity and performance of this system are analyzed. The waveform and leg-mel spectrogram are used as input features in this architecture. During audio tagging, the proposed system outperforms the existing systems with a mean average of 0.45. The performance of the proposed model is demonstrated by applying the audio neural network to five specific audio pattern recognition tasks.
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Yang, Daniel, Kevin Ji, and TJ Tsai. "A Deeper Look at Sheet Music Composer Classification Using Self-Supervised Pretraining." Applied Sciences 11, no. 4 (February 4, 2021): 1387. http://dx.doi.org/10.3390/app11041387.
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This article studies a composer style classification task based on raw sheet music images. While previous works on composer recognition have relied exclusively on supervised learning, we explore the use of self-supervised pretraining methods that have been recently developed for natural language processing. We first convert sheet music images to sequences of musical words, train a language model on a large set of unlabeled musical “sentences”, initialize a classifier with the pretrained language model weights, and then finetune the classifier on a small set of labeled data. We conduct extensive experiments on International Music Score Library Project (IMSLP) piano data using a range of modern language model architectures. We show that pretraining substantially improves classification performance and that Transformer-based architectures perform best. We also introduce two data augmentation strategies and present evidence that the model learns generalizable and semantically meaningful information.
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Singhal, Rahul, Shruti Srivatsan, and Priyabrata Panda. "Classification of Music Genres using Feature Selection and Hyperparameter Tuning." September 2022 4, no. 3 (August 25, 2022): 167–78. http://dx.doi.org/10.36548/jaicn.2022.3.003.
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The ability of music to spread joy and excitement across lives, makes it widely acknowledged as the human race's universal language. The phrase "music genre" is frequently used to group several musical styles together as following a shared custom or set of guidelines. According to their unique preferences, people now make playlists based on particular musical genres. Due to the determination and extraction of appropriate audio elements, music genre identification is regarded as a challenging task. Music information retrieval, which extracts meaningful information from music, is one of several real - world applications of machine learning. The objective of this paper is to efficiently categorise songs into various genres based on their attributes using various machine learning approaches. To enhance the outcomes, appropriate feature engineering and data pre-processing techniques have been performed. Finally, using suitable performance assessment measures, the output from each model has been compared. Compared to other machine learning algorithms, Random Forest along with efficient feature selection and hyperparameter tuning has produced better results in classifying music genres.
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Vani Vivekanand, Chettiyar. "Performance Analysis of Emotion Classification Using Multimodal Fusion Technique." Journal of Computational Science and Intelligent Technologies 2, no. 1 (April 16, 2021): 14–20. http://dx.doi.org/10.53409/mnaa/jcsit/2103.
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As the central processing unit of the human body, the human brain is in charge of several activities, including cognition, perception, emotion, attention, action, and memory. Emotions have a significant impact on human well-being in their life. Methodologies for accessing emotions of human could be essential for good user-machine interactions. Comprehending BCI (Brain-Computer Interface) strategies for identifying emotions can also help people connect with the world more naturally. Many approaches for identifying human emotions have been developed using signals of EEG for classifying happy, neutral, sad, and angry emotions, discovered to be effective. The emotions are elicited by various methods, including displaying participants visuals of happy and sad facial expressions, listening to emotionally linked music, visuals, and, sometimes, both of these. In this research, a multi-model fusion approach for emotion classification utilizing BCI and EEG data with various classifiers was proposed. The 10-20 electrode setup was used to gather the EEG data. The emotions were classified using the sentimental analysis technique based on user ratings. Simultaneously, Natural Language Processing (NLP) is implemented for increasing accuracy. This analysis classified the assessment parameters as happy, neutral, sad, and angry emotions. Based on these emotions, the proposed model’s performance was assessed in terms of accuracy and overall accuracy. The proposed model has a 93.33 percent overall accuracy and increased performance in all emotions identified.
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Grollmisch, Sascha, and Estefanía Cano. "Improving Semi-Supervised Learning for Audio Classification with FixMatch." Electronics 10, no. 15 (July 28, 2021): 1807. http://dx.doi.org/10.3390/electronics10151807.
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Including unlabeled data in the training process of neural networks using Semi-Supervised Learning (SSL) has shown impressive results in the image domain, where state-of-the-art results were obtained with only a fraction of the labeled data. The commonality between recent SSL methods is that they strongly rely on the augmentation of unannotated data. This is vastly unexplored for audio data. In this work, SSL using the state-of-the-art FixMatch approach is evaluated on three audio classification tasks, including music, industrial sounds, and acoustic scenes. The performance of FixMatch is compared to Convolutional Neural Networks (CNN) trained from scratch, Transfer Learning, and SSL using the Mean Teacher approach. Additionally, a simple yet effective approach for selecting suitable augmentation methods for FixMatch is introduced. FixMatch with the proposed modifications always outperformed Mean Teacher and the CNNs trained from scratch. For the industrial sounds and music datasets, the CNN baseline performance using the full dataset was reached with less than 5% of the initial training data, demonstrating the potential of recent SSL methods for audio data. Transfer Learning outperformed FixMatch only for the most challenging dataset from acoustic scene classification, showing that there is still room for improvement.
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Ma, Bo Le, Jing Fang Cheng, and Chao Ran Zhang. "Research on a New Array-Manifold of Single Vector Hydrophone." Advanced Materials Research 955-959 (June 2014): 899–910. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.899.
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For the purpose of improving the signal processing of single vector hydrophone, this paper combined two velocity signals as two complex data, so as to change array-manifold of single vector hydrophone. Taking two-dimension single vector hydrophone as an example, this paper compared the capability of signal processing of new array-manifold single vector hydrophone with old one from conventional beam-forming(CBF) ,minimum variance distortionless response (MVDR) and multiple signal classification (MUSIC). As for CBF, the analysis indicates, the capability of spatial filtering of new array-manifold could improve 0.51db and the HPBW of new array-manifold will be smaller than old array-manifold. When the noise power is 0, the HPBW of new array-manifold will be narrower than old array-manifold 19.26°. As for MVDR, the capability of signal processing of new array-manifold is the same as old array-manifold. In MUSIC algorithm, the value measuring angle resolution shows the superiority of the new array-manifold- angle resolution. Simulation and measured data proved the better performance of the method presented by this paper.
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Dwisaputra, Indra, and Ocsirendi Ocsirendi. "Teknik Pengenalan Suara Musik Pada Robot Seni Tari." Manutech : Jurnal Teknologi Manufaktur 10, no. 02 (May 20, 2019): 35–39. http://dx.doi.org/10.33504/manutech.v10i02.66.
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The dancing robot has become an annual competition in Indonesia that needs to be developed to improve robot performance. The dancing robot is a humanoid robot that has 24 degrees of freedom. For 2018 the theme raised was "Remo Dancer Robot". Sound processing provides a very important role in dance robots. This robot moves dancing to adjust to the rhythm of the music. The robot will stop dancing when the music is mute. The resulting sound signal is still analogous. Voice signals must be changed to digital data to access the signal. Convert analog to digital signals using Analog Digital Converter (ADC). ADC data is taken by sampling time 254 data per second. The sampling data is stored and grouped per 1 second to classify the parts of Remo Dance music. The results of data classification are in the form of digital numbers which then become a reference to determine the movement of the robot. Robots can recognize conditions when music is in a mute or a play condition.
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Wang, Guoxuan, Guimei Zheng, Hongzhen Wang, and Chen Chen. "Meter Wave Polarization-Sensitive Array Radar for Height Measurement Based on MUSIC Algorithm." Sensors 22, no. 19 (September 26, 2022): 7298. http://dx.doi.org/10.3390/s22197298.
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Obtaining good measurement performance with meter wave radar has always been a difficult problem. Especially in low-elevation areas, the multipath effect seriously affects the measurement accuracy of meter wave radar. The generalized multiple signal classification (MUSIC) algorithm is a well-known measurement method that dose not require decorrelation processing. The polarization-sensitive array (PSA) has the advantage of polarization diversity, and the polarization smoothing generalized MUSIC algorithm demonstrates good angle estimation performance in low-elevation areas when based on a PSA. Nevertheless, its computational complexity is still high, and the estimation accuracy and discrimination success probability need to be further improved. In addition, it cannot estimate the polarization parameters. To solve these problems, a polarization synthesis steering vector MUSIC algorithm is proposed in this paper. First, the MUSIC algorithm is used to obtain the spatial spectrum of the meter wave PSA. Second, the received data are properly deformed and classified. The Rayleigh–Ritz method is used to decompose the angle to realize the decoupling of polarization and the direction of the arrival angle. Third, the geometric relationship and prior information of the direct wave and the reflected wave are used to continue dimension reduction processing to reduce the computational complexity of the algorithm. Finally, the geometric relationship is used to obtain the target height measurement results. Extensive simulation results illustrate the accuracy and superiority of the proposed algorithm.
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Liang, Yan, Zhou Meng, Yu Chen, Yichi Zhang, Mingyang Wang, and Xin Zhou. "A Data Fusion Orientation Algorithm Based on the Weighted Histogram Statistics for Vector Hydrophone Vertical Array." Sensors 20, no. 19 (October 1, 2020): 5619. http://dx.doi.org/10.3390/s20195619.
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In this paper, we propose a data fusion algorithm based on the weighted histogram statistics (DF-WHS) to improve the performance of direction-of-arrival (DOA) estimation for the vector hydrophone vertical array (VHVA). The processing frequency band is firstly divided into multiple sub-bands, and the high-resolution multiple signal classification (MUSIC) algorithm is applied to estimate the azimuth of each sub-band for each vector hydrophone. Then, the weighted least square (WLS) data fusion technique is used to fuse the sub-band estimation results of multiple sensors. Finally, the weighted histogram statistics method is employed to obtain the synthesis results in the frequency domain. We carried out a simulation and sea trial of the 16-element VHVA to evaluate the performance of the proposed algorithm. Compared to several traditional processing algorithms, the beam width of the proposed approach is significantly narrower, the side lobes are considerably lower, and the mean square error (MSE) is effectively smaller. In addition, the DF-WHS method is more suitable to accurately estimate the target azimuth with a low signal-to-noise ratio (SNR) because the noise sub-band is suppressed in the weighted histogram statistics step. The DF-WHS method in this article provides a new approach to improve the performance of deep-sea target detection for the VHVA.
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Ramachandran, Naveen, Sassan Saatchi, Stefano Tebaldini, Mauro Mariotti d’Alessandro, and Onkar Dikshit. "Evaluation of P-Band SAR Tomography for Mapping Tropical Forest Vertical Backscatter and Tree Height." Remote Sensing 13, no. 8 (April 13, 2021): 1485. http://dx.doi.org/10.3390/rs13081485.
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Low-frequency tomographic synthetic aperture radar (TomoSAR) techniques provide an opportunity for quantifying the dynamics of dense tropical forest vertical structures. Here, we compare the performance of different TomoSAR processing, Back-projection (BP), Capon beamforming (CB), and MUltiple SIgnal Classification (MUSIC), and compensation techniques for estimating forest height (FH) and forest vertical profile from the backscattered echoes. The study also examines how polarimetric measurements in linear, compact, hybrid, and dual circular modes influence parameter estimation. The tomographic analysis was carried out using P-band data acquired over the Paracou study site in French Guiana, and the quantitative evaluation was performed using LiDAR-based canopy height measurements taken during the 2009 TropiSAR campaign. Our results show that the relative root mean squared error (RMSE) of height was less than 10%, with negligible systematic errors across the range, with Capon and MUSIC performing better for height estimates. Radiometric compensation, such as slope correction, does not improve tree height estimation. Further, we compare and analyze the impact of the compensation approach on forest vertical profiles and tomographic metrics and the integrated backscattered power. It is observed that radiometric compensation increases the backscatter values of the vertical profile with a slight shift in local maxima of the canopy layer for both the Capon and the MUSIC estimators. Our results suggest that applying the proper processing and compensation techniques on P-band TomoSAR observations from space will allow the monitoring of forest vertical structure and biomass dynamics.
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Rajadnya, Vibhavari, and Kalyani R. Joshi. "Raga classification based on pitch co-occurrence based features." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (October 1, 2021): 157. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp157-166.
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<p><span>Analysis and classification of raga is the need of time especially in music industry. With the presence of abundance of multimedia data on internet, it is imperative to develop appropriate tools to classify ragas. In this work, an attempt has been made to use occurrence pattern of pitch based svara (note) for classification. Sequence of notes is an important cue in the raga classification. Pitch based svara (note) profile is formed. This pattern presents in the signal along with its statistical distribution can be characterized using co-occurrence matrix. Proposed note co-occurrence matrix summarizes this aspect. This matrix captures both tonal and temporal aspects of melody. Ragas differ in terms of distribution of spectral power. K-nearest neighbor (KNN) has been used as the classifier. Publicly available database consisting of 300 recordings of 30 Hindustani ragas consisting of 130 hours of audio recordings stored as 160 kbps mp3 fileswhich is part of CompMusic project is used. Leave one out validation strategy is used to evaluate the performance. Experimental result indicates the effectiveness of the proposed scheme which is giving accuracy of 93.7%.</span></p>
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Mishra, Sudipan, and Xumin Liu. "Optimizing Concurrency Performance of Complex Services in Mobile Environment." International Journal of Web Services Research 11, no. 1 (January 2014): 94–110. http://dx.doi.org/10.4018/ijwsr.2014010105.
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Hosting services on mobile devices has been considered as the key solution for domains that have special requirement on portability, timeliness, and flexibility on service deployment. Typical examples include, among many others, military, music, healthcare, gaming, and data sharing. Despite the recent boom of mobile computing makes service deployment in mobile environment possible, significant challenges arise due to the limitations in existing mobile hardware/software capable of managing resource intensive applications. The situation gets worse when managing complex services that allow concurrent clients and requests. This paper addresses the issue related specifically to concurrency control improvement in mobile web servers to support the mobile deployment of complex services. The authors identify key factors that affect a system to respond a request, including request related factors, system resource related factors, and context. Based on this, the authors propose a dynamic heavy request classification model (DHRC) to estimate the heaviness of an incoming request using machine-learning methods. The heavy request will be detected, which requires relatively heavy system resources of the mobile server to generate a response. The authors design a dynamic request management strategy (DRMS), which reduces the number of discarded requests by adding heavy requests to a queue and processing them asynchronously. The proposed solution is implemented on Android-based mobile devices as an extension of I-Jetty web server. Experimental studies are conducted and the result indicates the effectiveness of our solution.
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Ali, Yaseen Hadi, Rozeha A. Rashid, and Siti Zaleha Abdul Hamid. "A machine learning for environmental noise classification in smart cities." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (March 1, 2022): 1777. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1777-1786.
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<a name="OLE_LINK39"></a><span>The sound at the same decibel (dB) level may be perceived either as annoying noise or as pleasant music. Therefore, it is necessary to go beyond the state-of-the-art approaches that measure only the dB level and also identify the type of the sound especially when the sound is recorded using a microphone. This paper presented a case study that considers the ability of machine learning models to identify sources of environmental noise in urban areas and compares the sound levels with the recommended levels by the World Health Organization (WHO). The approach was evaluated with a </span><a name="OLE_LINK3"></a><span>dataset </span><span>of 44 sound samples grouped in four sound classes that are highway, railway, lawnmowers, and birds. We used mel-frequency cepstral coefficients for feature extraction and supervised algorithms that are Support vector machine (SVM), k-nearest neighbors (KNN), <a name="OLE_LINK22"></a>bootstrap aggregation (Bagging), and random forest (RF) for noise classification. We evaluated performance of the four algorithms to determine the best one for the classification of sound samples in the data set under consideration. The findings showed that the noise classification accuracy is in the range of 95%</span><span>-100%. Furthermore, all the captured data exceeded the recommended levels by WHO which can cause adverse health effects.</span>
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Famoriji, Oluwole John, and Thokozani Shongwe. "Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array." Electronics 11, no. 2 (January 10, 2022): 208. http://dx.doi.org/10.3390/electronics11020208.
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Direction-of-arrival (DoA) estimation of electromagnetic (EM) waves impinging on a spherical antenna array in short time windows is examined in this paper. Reflected EM signals due to non-line-of-sight propagation measured with a spherical antenna array can be coherent and/or highly correlated in a snapshot. This makes spectral-based methods inefficient. Spectral methods, such as maximum likelihood (ML) methods, multiple signal classification (MUSIC), and beamforming methods, are theoretically and systematically investigated in this study. MUSIC is an approach used for frequency estimation and radio direction finding, ML is a technique used for estimating the parameters of an assumed probability distribution for given observed data, and PWD applies a Fourier transform to the capture response and produces them in the frequency domain. Although they have been previously adapted and used to estimate DoA of EM signals impinging on linear and planar antenna array configurations, this paper investigates their suitability and effectiveness for a spherical antenna array. Various computer simulations were conducted, and plots of root-mean-square error (RMSE) against the square root of the Cramér–Rao lower bound (CRLB) were generated and used to evaluate the performance of each method. Numerical experiments and results from measured data show the degree of appropriateness and efficiency of each method. For instance, the techniques exhibit identical performance to that in the wideband scenario when the frequency f = 8 GHz, f = 16 GHz, and f = 32 GHz, but f = 16 GHz performs best. This indicates that the difference between the covariance matrix of the signal is coherent and that the steering vectors of signals impinging from that angle are small. MUSIC and PWD share the same problems in the single-frequency scenario as in the wideband scenario when the delay sample d = 0. Consequently, the DoA estimation obtained with ML techniques is more suitable, less biased, and more robust against noise than beamforming and MUSIC techniques. In addition, deterministic ML (DML) and weighted subspace fitting (WSF) techniques show better DoA estimation performance than the stochastic ML (SML) technique. For a large number of snapshots, WSF is a better choice because it is more computationally efficient than DML. Finally, the results obtained indicate that WSF and ML methods perform better than MUSIC and PWD for the coherent or partially correlated signals studied.
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Kolinsky, Réégine, Héélééne Cuvelier, Vincent Goetry, Isabelle Peretz, and Joséé Morais. "Music Training Facilitates Lexical Stress Processing." Music Perception 26, no. 3 (February 1, 2009): 235–46. http://dx.doi.org/10.1525/mp.2009.26.3.235.
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WE INVESTIGATED WHETHER MUSIC TRAINING facilitates the processing of lexical stress in natives of a language that does not use lexical stress contrasts. Musically trained (musicians) or untrained (nonmusicians) French natives were presented with two tasks: speeded classification that required them to focus on a segmental contrast and ignore irrelevant stress variations, and sequence repetition involving either segmental or stress contrasts. In the latter situation, French natives are usually "deaf" to lexical stress, but this was less the case for musicians, demonstrating that music expertise enhances sensitivity to stress contrasts. This increased sensitivity does not seem, however, to unavoidably bias musicians' attention to stress contrasts: in segmental-based speeded classification, musicians were not more affected than nonmusicians by irrelevant stress variations when overall performance was controlled for. Implications regarding both the notion of modularity of processing and the advantage that musicianship may afford for second language learning are discussed.
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Düzenli, Timur, and Nalan Özkurt. "Discrete and Dual Tree Wavelet Features for Real-Time Speech/Music Discrimination." ISRN Signal Processing 2011 (May 2, 2011): 1–10. http://dx.doi.org/10.5402/2011/269361.
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The performance of wavelet transform-based features for the speech/music discrimination task has been investigated. In order to extract wavelet domain features, discrete and complex orthogonal wavelet transforms have been used. The performance of the proposed feature set has been compared with a feature set constructed from the most common time, frequency and cepstral domain features such as number of zero crossings, spectral centroid, spectral flux, and Mel cepstral coefficients. The artificial neural networks have been used as classification tool. The principal component analysis has been applied to eliminate the correlated features before the classification stage. For discrete wavelet transform, considering the number of vanishing moments and orthogonality, the best performance is obtained with Daubechies8 wavelet among the other members of the Daubechies family. The dual tree wavelet transform has also demonstrated a successful performance both in terms of accuracy and time consumption. Finally, a real-time discrimination system has been implemented using the Daubhecies8 wavelet which has the best accuracy.
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Kumar, Arvind, Sandeep Solanki, and Mahesh Chandra. "Hilbert spectrum based features for speech/music classification." Serbian Journal of Electrical Engineering 19, no. 2 (2022): 239–59. http://dx.doi.org/10.2298/sjee2202239k.
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Automatic Speech/Music classification uses different signal processing techniques to categorize multimedia content into different classes. The proposed work explores Hilbert Spectrum (HS) obtained from different AM-FM components of an audio signal, also called Intrinsic Mode Functions (IMFs) to classify an incoming audio signal into speech/music signal. The HS is a twodimensional representation of instantaneous energies (IE) and instantaneous frequencies (IF) obtained using Hilbert Transform of the IMFs. This HS is further processed using Mel-filter bank and Discrete Cosine Transform (DCT) to generate novel IF and Instantaneous Amplitude (IA) based cepstral features. Validations of the results were done using three databases-Slaney Database, GTZAN and MUSAN database. To evaluate the general applicability of the proposed features, extensive experiments were conducted on different combination of audio files from S&S, GTZAN and MUSAN database and promising results are achieved. Finally, performance of the system is compared with performance of existing cepstral features and previous works in this domain.
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Qiu, Lvyang, Shuyu Li, and Yunsick Sung. "3D-DCDAE: Unsupervised Music Latent Representations Learning Method Based on a Deep 3D Convolutional Denoising Autoencoder for Music Genre Classification." Mathematics 9, no. 18 (September 16, 2021): 2274. http://dx.doi.org/10.3390/math9182274.
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With unlabeled music data widely available, it is necessary to build an unsupervised latent music representation extractor to improve the performance of classification models. This paper proposes an unsupervised latent music representation learning method based on a deep 3D convolutional denoising autoencoder (3D-DCDAE) for music genre classification, which aims to learn common representations from a large amount of unlabeled data to improve the performance of music genre classification. Specifically, unlabeled MIDI files are applied to 3D-DCDAE to extract latent representations by denoising and reconstructing input data. Next, a decoder is utilized to assist the 3D-DCDAE in training. After 3D-DCDAE training, the decoder is replaced by a multilayer perceptron (MLP) classifier for music genre classification. Through the unsupervised latent representations learning method, unlabeled data can be applied to classification tasks so that the problem of limiting classification performance due to insufficient labeled data can be solved. In addition, the unsupervised 3D-DCDAE can consider the musicological structure to expand the understanding of the music field and improve performance in music genre classification. In the experiments, which utilized the Lakh MIDI dataset, a large amount of unlabeled data was utilized to train the 3D-DCDAE, obtaining a denoising and reconstruction accuracy of approximately 98%. A small amount of labeled data was utilized for training a classification model consisting of the trained 3D-DCDAE and the MLP classifier, which achieved a classification accuracy of approximately 88%. The experimental results show that the model achieves state-of-the-art performance and significantly outperforms other methods for music genre classification with only a small amount of labeled data.
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Wei, Bo, Kai Li, Chengwen Luo, Weitao Xu, Jin Zhang, and Kuan Zhang. "No Need of Data Pre-processing." ACM Transactions on Internet of Things 2, no. 4 (November 30, 2021): 1–26. http://dx.doi.org/10.1145/3467980.
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Device-free context awareness is important to many applications. There are two broadly used approaches for device-free context awareness, i.e., video-based and radio-based. Video-based approaches can deliver good performance, but privacy is a serious concern. Radio-based context awareness applications have drawn researchers' attention instead, because it does not violate privacy and radio signal can penetrate obstacles. The existing works design explicit methods for each radio-based application. Furthermore, they use one additional step to extract features before conducting classification and exploit deep learning as a classification tool. Although this feature extraction step helps explore patterns of raw signals, it generates unnecessary noise and information loss. The use of raw CSI signal without initial data processing was, however, considered as no usable patterns. In this article, we are the first to propose an innovative deep learning–based general framework for both signal processing and classification. The key novelty of this article is that the framework can be generalised for all the radio-based context awareness applications with the use of raw CSI. We also eliminate the extra work to extract features from raw radio signals. We conduct extensive evaluations to show the superior performance of our proposed method and its generalisation.
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Ozcift, Akin, and Arif Gulten. "Assessing Effects of Pre-Processing Mass Spectrometry Data on Classification Performance." European Journal of Mass Spectrometry 14, no. 5 (April 1, 2008): 267–73. http://dx.doi.org/10.1255/ejms.938.
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Disease prediction through mass spectrometry (MS) data is gaining importance in medical diagnosis. Particularly in cancerous diseases, early prediction is one of the most life saving stages. High dimension and the noisy nature of MS data requires a two-phase study for successful disease prediction; first, MS data must be pre-processed with stages such as baseline correction, normalizing, de-noising and peak detection. Second, a dimension reduction based classifier design is the main objective. Having the data pre-processed, the prediction accuracy of the classifier algorithm becomes the most significant factor in the medical diagnosis phase. As health is the main concern, the accuracy of the classifier is clearly very important. In this study, the effects of the pre-processing stages of MS data on classifier performances are addressed. Three pre-processing stages—baseline correction, normalization and de-noising—are applied to three MS data samples, namely, high-resolution ovarian cancer, low-resolution prostate cancer and a low-resolution ovarian cancer. To measure the effects of the pre-processing stages quantitatively, four diverse classifiers, genetic algorithm wrapped K-nearest neighbor (GA-KNN), principal component analysis-based least discriminant analysis (PCA-LDA), a neural network (NN) and a support vector machine (SVM) are applied to the data sets. Calculated classifier performances have demonstrated the effects of pre-processing stages quantitatively and the importance of pre-processing stages on the prediction accuracy of classifiers. Results of computations have been shown clearly.
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Tayyari, Fariborz, and James L. Smith. "Effect of Music on Performance in Human-Computer Interface." Proceedings of the Human Factors Society Annual Meeting 31, no. 12 (September 1987): 1321–25. http://dx.doi.org/10.1177/154193128703101205.
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The effect of music at two levels (60–65 dB and 80–85 dB), vs. no music (silent), on the performance of 40 subjects engaged in a data processing task was studied. It was found that, while the music did not disturb the overall accuracy of the task output, it increased the subjects' speed in data processing and overall productivity. The subjects showed a favorable attitude toward music being introduced at workstations.
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Heijink, Hank, Luke Windsor, and Peter Desain. "Data processing in music performance research: Using structural information to improve score-performance matching." Behavior Research Methods, Instruments, & Computers 32, no. 4 (December 2000): 546–54. http://dx.doi.org/10.3758/bf03200827.
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Andrighetti, Milena, Giovanna Turvani, Giulia Santoro, Marco Vacca, Andrea Marchesin, Fabrizio Ottati, Massimo Ruo Roch, Mariagrazia Graziano, and Maurizio Zamboni. "Data Processing and Information Classification—An In-Memory Approach." Sensors 20, no. 6 (March 18, 2020): 1681. http://dx.doi.org/10.3390/s20061681.
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To live in the information society means to be surrounded by billions of electronic devices full of sensors that constantly acquire data. This enormous amount of data must be processed and classified. A solution commonly adopted is to send these data to server farms to be remotely elaborated. The drawback is a huge battery drain due to high amount of information that must be exchanged. To compensate this problem data must be processed locally, near the sensor itself. But this solution requires huge computational capabilities. While microprocessors, even mobile ones, nowadays have enough computational power, their performance are severely limited by the Memory Wall problem. Memories are too slow, so microprocessors cannot fetch enough data from them, greatly limiting their performance. A solution is the Processing-In-Memory (PIM) approach. New memories are designed that can elaborate data inside them eliminating the Memory Wall problem. In this work we present an example of such a system, using as a case of study the Bitmap Indexing algorithm. Such algorithm is used to classify data coming from many sources in parallel. We propose a hardware accelerator designed around the Processing-In-Memory approach, that is capable of implementing this algorithm and that can also be reconfigured to do other tasks or to work as standard memory. The architecture has been synthesized using CMOS technology. The results that we have obtained highlights that, not only it is possible to process and classify huge amount of data locally, but also that it is possible to obtain this result with a very low power consumption.
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Zhang, Yuyu. "An Empirical Analysis of Piano Performance Skill Evaluation Based on Big Data." Mobile Information Systems 2022 (August 27, 2022): 1–9. http://dx.doi.org/10.1155/2022/8566721.
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Teachers often guide the rhythm and coherence of piano performance in the teaching process. It is of great significance to use computer technology to automatically evaluate piano performance skills. In this paper, computer technology is used to automatically evaluate the accuracy of piano music classification based on the high-dimensional data collaborative filtering recommendation algorithm, and the K-means model algorithm is used for comparative testing. By comparing the classification results of the high-dimensional data collaborative filtering recommendation algorithm with the piano music classification results of the K-means algorithm, the piano learning burden can be reduced and the piano learning effect can be improved. The research results of this paper show that the accuracy rate of the automatic piano performance evaluation system based on the high-dimensional data collaborative filtering recommendation algorithm reaches 95%, which has a good evaluation effect.
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Kang, Sang-Ick, and Sangmin Lee. "Improvement of Speech/Music Classification for 3GPP EVS Based on LSTM." Symmetry 10, no. 11 (November 7, 2018): 605. http://dx.doi.org/10.3390/sym10110605.
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The competition of speech recognition technology related to smartphones is now getting into full swing with the widespread internet of thing (IoT) devices. For robust speech recognition, it is necessary to detect speech signals in various acoustic environments. Speech/music classification that facilitates optimized signal processing from classification results has been extensively adapted as an essential part of various electronics applications, such as multi-rate audio codecs, automatic speech recognition, and multimedia document indexing. In this paper, we propose a new technique to improve robustness of a speech/music classifier for an enhanced voice service (EVS) codec adopted as a voice-over-LTE (VoLTE) speech codec using long short-term memory (LSTM). For effective speech/music classification, feature vectors implemented with the LSTM are chosen from the features of the EVS. To overcome the diversity of music data, a large scale of data is used for learning. Experiments show that LSTM-based speech/music classification provides better results than the conventional EVS speech/music classification algorithm in various conditions and types of speech/music data, especially at lower signal-to-noise ratio (SNR) than conventional EVS algorithm.
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Li, Li, Rui Zhang, and Zhenyu Wang. "Melodic Phrase Attention Network for Symbolic Data-based Music Genre Classification (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15825–26. http://dx.doi.org/10.1609/aaai.v35i18.17909.
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Compared with audio data-based music genre classification, researches on symbolic data-based music are scarce. Existing methods generally utilize manually extracted features, which is very time-consuming and laborious, and use traditional classifiers for label prediction without considering specific music features. To tackle this issue, we propose the Melodic Phrase Attention Network (MPAN) for symbolic data-based music genre classification. Our model is trained in three steps: First, we adopt representation learning, instead of the traditional musical feature extraction method, to obtain a vectorized representation of the music pieces. Second, the music pieces are divided into several melodic phrases through melody segmentation. Finally, the Melodic Phrase Attention Network is designed according to music characteristics, to identify the reflection of each melodic phrase on the music genre, thereby generating more accurate predictions. Experimental results show that our proposed method is superior to baseline symbolic data-based music genre classification approaches, and has achieved significant performance improvements on two large datasets.
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Wang, Xiaohua, Lei Cheng, Ding Cheng, and Qinlin Zhou. "Theater Music Data Acquisition and Genre Recognition Using Edge Computing and Deep Brief Network." Scientific Programming 2022 (August 25, 2022): 1–6. http://dx.doi.org/10.1155/2022/8543443.
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Artificial intelligence (AI) and the Internet of Things (IoT) make it urgent to push the frontier of AI to the network edge and release the potential of edge big data. The model’s accuracy in data acquisition and music genre classification (MGC) is further improved based on theater music data acquisition. First, machine learning and AI algorithms are used to collect data on various devices and automatically identify music genres. The data collected by edge devices are safe and private, which shortens the time delay of data processing and response. In addition, the deep belief network (DBN)-based MGC algorithm has better overall recognition and classification effect on music genres. The MGC accuracy of the proposed improved DBN algorithm is nearly 80%, compared to 30%–40% of the traditional algorithms. The DBN algorithm is more accurate than the traditional classical algorithm in MGC. The research has an important reference value for developing Internet technology and establishing a music recognition model.
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Hu, Yifeng, and Gabriela Mogos. "Music genres classification by deep learning." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (February 1, 2022): 1186. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp1186-1198.
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<p>Since musical genre is one of the most common ways used by people for managing digital music databases, music-genre-classification is a crucial task. There are many scenarios for its use, and the main one explored here is eventually being placed on Spotify, or Netease music, as an external component to recommend songs to users. This paper provides various deep neural networks developed based on python, together with the effect of these models on music genres classification. In addition, the paper illustrates the technologies for audio feature extraction in industrial environment by mel frequency cepstral coefficients (MFCC), audio data augmentation in</p>
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Chincholkar, Bhushan R. "Implementation Analysis of Data Classification Approach for Sentiment Classification." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 1509–12. http://dx.doi.org/10.22214/ijraset.2021.36613.
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Sentiment analysis is one of the fastest growing fields with its demand and potential benefits that are increasing every day. Sentiment analysis aims to classify the polarity of a document through natural language processing, text analysis. With the help of internet and modern technology, there has bee n a tremendous growth in the amount of data. Each individual is in position to precise his/her own ideas freely on social media. All of this data can be analyzed and used in order to draw benefits and quality information. In this paper, the focus is on cyber-hate classification based on for public opinion or views, since the spread of hate speech using social media can have disruptive impacts on social sentiment analysis. In particular, here proposing a modified approach with two stage training for dealing with text ambiguity and classifying three type approach positive, negative and neutral sentiment, and compare its performance with those popular methods also as well as some existing fuzzy approaches. Afterword comparing the performance of proposed approach with commonly used sentiment classifiers which are known to perform well in this task. The experimental results indicate that our modified approach performs marginally better than the other algorithms.
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Xin, Feng, Li Shaohui, Feng Qiang, and Liu Shugui. "Vibration-based Processing and Classification Method for Oil Well-testing Data from Downhole Pressure Gauges." E3S Web of Conferences 206 (2020): 01024. http://dx.doi.org/10.1051/e3sconf/202020601024.
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During petroleum exploration and exploitation, the oil well-testing data collected by pressure gauges are used for monitoring the well condition and recording the reservoir performance. However, due to the large number of the collected data, the classification of this large volume of data requires a previous processing for the removal of noise and outliers. It is impractical to partition and process these data manually. Vibration-based features reflect geological properties and offer a promising option to fulfil such requirements. Based on the 75 on-site measured samples, the time-frequency-domain features are extracted and the classification performance of three classical classifiers are investigated. Then the downhole data processing and classification method is present by analysing the cross interaction of different types of data features and different classification mechanism. Several feature combinations are tested to establish a processing flow that can efficiently remove the noise and preserve the shape of curves, high signal to noise ratio rates, with minimum absolute errors. The results show that optimal multi-feature combination can achieve the highest working stage identification rate of 72%, the parameters optimized support vector machine can achieve the better classification performance than other listed classifiers. This paper provides a theoretical study for the data denoising and processing to enhance the working stage classification accuracy.
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Zhang, Kedong. "Music Style Classification Algorithm Based on Music Feature Extraction and Deep Neural Network." Wireless Communications and Mobile Computing 2021 (September 4, 2021): 1–7. http://dx.doi.org/10.1155/2021/9298654.
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The music style classification technology can add style tags to music based on the content. When it comes to researching and implementing aspects like efficient organization, recruitment, and music resource recommendations, it is critical. Traditional music style classification methods use a wide range of acoustic characteristics. The design of characteristics necessitates musical knowledge and the characteristics of various classification tasks are not always consistent. The rapid development of neural networks and big data technology has provided a new way to better solve the problem of music-style classification. This paper proposes a novel method based on music extraction and deep neural networks to address the problem of low accuracy in traditional methods. The music style classification algorithm extracts two types of features as classification characteristics for music styles: timbre and melody features. Because the classification method based on a convolutional neural network ignores the audio’s timing. As a result, we proposed a music classification module based on the one-dimensional convolution of a recurring neuronal network, which we combined with single-dimensional convolution and a two-way, recurrent neural network. To better represent the music style properties, different weights are applied to the output. The GTZAN data set was also subjected to comparison and ablation experiments. The test results outperformed a number of other well-known methods, and the rating performance was competitive.
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Woehrle, Hendrik, Johannes Teiwes, Elsa Kirchner, and Frank Kirchner. "A Framework for High Performance Embedded Signal Processing and Classification of Psychophysiological Data." APCBEE Procedia 7 (2013): 60–66. http://dx.doi.org/10.1016/j.apcbee.2013.08.013.
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Chen, Yue E., and Bai Li Ren. "Research on Large Scale Data Set Processing Based on SVM." Advanced Materials Research 216 (March 2011): 738–41. http://dx.doi.org/10.4028/www.scientific.net/amr.216.738.
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SVM has got very good results in the area of solving the classification, regression and density estimation problem in machine learning, has been successfully applied to practical problems of text recognition, speech classification, but the training time is too long is a big drawback. A new reduction strategy is proposed for training support vector machines. This method is fast in convergence without learning machine’s generalization performance, the results of simulation experiments show the feasibility and effectiveness of that method through this method.
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Gong, Tianzhuo. "Deep Belief Network-Based Multifeature Fusion Music Classification Algorithm and Simulation." Complexity 2021 (January 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/8861896.
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In this paper, the multifeature fusion music classification algorithm and its simulation results are studied by deep confidence networks, the multifeature fusion music database is established and preprocessed, and then features are extracted. The simulation is carried out using multifeature fusion music data. The multifeature fusion music preprocessing includes endpoint detection, framing, windowing, and pre-emphasis. In this paper, we extracted the rhythm features, sound quality features, and spectral features, including energy, cross-zero rate, fundamental frequency, harmonic noise ratio, and 12 statistical features, including maximum value, mean value, and linear slope. A total of 384-dimensional statistical features was extracted and compared with the classification ability of different emotional features. The deficiencies of the traditional classification algorithm are first studied, and then by introducing confusion, constructing multilevel classifiers, and tuning each level of the classifier, better recognition rates than traditional primary classification are obtained. This paper introduces label information for supervised training to further improve the features of multifunctional fusion music. Experiments show that this information has excellent performance in multifunctional fusion music recognition. The experiments compare the multilevel classifier with primary classification, and the multilevel classification with the primary classification and the classification performance is improved, and the recognition rate of the multilevel classification algorithm is also improved over the multilevel classification algorithm, proving that the excellent performance with multiple levels of classification.
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Yang, Gao. "Research on Music Content Recognition and Recommendation Technology Based on Deep Learning." Security and Communication Networks 2022 (March 14, 2022): 1–8. http://dx.doi.org/10.1155/2022/7696840.
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With the development of information technology, various cloud music services are gradually emerging, which has fully changed and enriched people’s music life. How to propose the songs that consumers anticipate from the enormous song data is one of the key goals of the music recommendation system. This research aims to create a better music algorithm that incorporates user data for deep learning, a candidate matrix compression technique for suggestion improvement, accuracy, recall rate, and other metrics as evaluation criteria. In terms of recommendation methods, the music-music recommendation method based on predicting user behavior data and the recommendation method based on automatic tag generation are proposed. The music features obtained by audio processing are fully utilized, and the depth content information in music audio data is combined with other data for recommendation, which improves the tag quality and avoids the problem of low coverage. The results show that this model can extract the effective feature representation of songs in different classification criteria and achieve a good classification effect simultaneously.
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Dabas, Chetna, Aditya Agarwal, Naman Gupta, Vaibhav Jain, and Siddhant Pathak. "Machine Learning Evaluation for Music Genre Classification of Audio Signals." International Journal of Grid and High Performance Computing 12, no. 3 (July 2020): 57–67. http://dx.doi.org/10.4018/ijghpc.2020070104.
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Music genre classification has its own popularity index in the present times. Machine learning can play an important role in the music streaming task. This research article proposes a machine learning based model for the classification of music genre. The evaluation of the proposed model is carried out while considering different music genres as in blues, metal, pop, country, classical, disco, jazz and hip-hop. Different audio features utilized in this study include MFCC (Mel Frequency Spectral Coefficients), Delta, Delta-Delta and temporal aspects for processing the data. The implementation of the proposed model has been done in the Python language. The results of the proposed model reveal an accuracy SVM accuracy of 95%. The proposed algorithm has been compared with existing algorithms and the proposed algorithm performs better than the existing ones in terms of accuracy.
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Woody, Robert H. "Musicians' Cognitive Processing of Imagery-Based Instructions for Expressive Performance." Journal of Research in Music Education 54, no. 2 (July 2006): 125–37. http://dx.doi.org/10.1177/002242940605400204.
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This study addressed the cognitive processes of musicians using imagery to improve expressive performance. Specifically, it was an examination of the extent to which musicians translate imagery into explicit plans for the sound properties of music. Eighty four undergraduate and graduate music majors completed a research packet during individual practice sessions. Subjects worked with three melodies, each accompanied by an imagery example presented as a teacher's instructions for performing more expressively. The research packet guided subjects in considering the imagery-based instruction, practicing in light of it, and giving a final performance. The subjects wrote down their thoughts during the process. Results indicated that some musicians used a cognitive translation process, but others chose to develop and personalize the provided imagery. A curvilinear pattern in the data suggested an inverted-U relationship between the variables of private instruction received and cognitive translation usage. An interpretation of this result in light of previous research is offered.
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Chaudhury, Mousumi, Amin Karami, and Mustansar Ali Ghazanfar. "Large-Scale Music Genre Analysis and Classification Using Machine Learning with Apache Spark." Electronics 11, no. 16 (August 17, 2022): 2567. http://dx.doi.org/10.3390/electronics11162567.
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The trend for listening to music online has greatly increased over the past decade due to the number of online musical tracks. The large music databases of music libraries that are provided by online music content distribution vendors make music streaming and downloading services more accessible to the end-user. It is essential to classify similar types of songs with an appropriate tag or index (genre) to present similar songs in a convenient way to the end-user. As the trend of online music listening continues to increase, developing multiple machine learning models to classify music genres has become a main area of research. In this research paper, a popular music dataset GTZAN which contains ten music genres is analysed to study various types of music features and audio signals. Multiple scalable machine learning algorithms supported by Apache Spark, including naïve Bayes, decision tree, logistic regression, and random forest, are investigated for the classification of music genres. The performance of these classifiers is compared, and the random forest performs as the best classifier for the classification of music genres. Apache Spark is used in this paper to reduce the computation time for machine learning predictions with no computational cost, as it focuses on parallel computation. The present work also demonstrates that the perfect combination of Apache Spark and machine learning algorithms reduces the scalability problem of the computation of machine learning predictions. Moreover, different hyperparameters of the random forest classifier are optimized to increase the performance efficiency of the classifier in the domain of music genre classification. The experimental outcome shows that the developed random forest classifier can establish a high level of performance accuracy, especially for the mislabelled, distorted GTZAN dataset. This classifier has outperformed other machine learning classifiers supported by Apache Spark in the present work. The random forest classifier manages to achieve 90% accuracy for music genre classification compared to other work in the same domain.
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Li, Tianjiao. "Visual Classification of Music Style Transfer Based on PSO-BP Rating Prediction Model." Complexity 2021 (May 13, 2021): 1–9. http://dx.doi.org/10.1155/2021/9959082.
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In this paper, based on computer reading and processing of music frequency, amplitude, timbre, image pixel, color filling, and so forth, a method of image style transfer guided by music feature data is implemented in real-time playback, using existing music files and image files, processing and trying to reconstruct the fluent relationship between the two in terms of auditory and visual, generating dynamic, musical sound visualization with real-time changes in the visualization. Although recommendation systems have been well developed in real applications, the limitations of CF algorithms are slowly coming to light as the number of people increases day by day, such as the data sparsity problem caused by the scarcity of rated items, the cold start problem caused by new items and new users. The work is dynamic, with real-time changes in music and sound. Taking portraits as an experimental case, but allowing users to customize the input of both music and image files, this new visualization can provide users with a personalized service of mass customization and generate personalized portraits according to personal preferences. At the same time, we take advantage of the BP neural network’s ability to handle complex nonlinear problems and construct a rating prediction model between the user and item attribute features, referred to as the PSO-BP rating prediction model, by combining the features of global optimization of particle swarm optimization algorithm, and make further improvements based on the traditional collaborative filtering algorithm.
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Qiu, Lvyang, Shuyu Li, and Yunsick Sung. "DBTMPE: Deep Bidirectional Transformers-Based Masked Predictive Encoder Approach for Music Genre Classification." Mathematics 9, no. 5 (March 3, 2021): 530. http://dx.doi.org/10.3390/math9050530.
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Music is a type of time-series data. As the size of the data increases, it is a challenge to build robust music genre classification systems from massive amounts of music data. Robust systems require large amounts of labeled music data, which necessitates time- and labor-intensive data-labeling efforts and expert knowledge. This paper proposes a musical instrument digital interface (MIDI) preprocessing method, Pitch to Vector (Pitch2vec), and a deep bidirectional transformers-based masked predictive encoder (MPE) method for music genre classification. The MIDI files are considered as input. MIDI files are converted to the vector sequence by Pitch2vec before being input into the MPE. By unsupervised learning, the MPE based on deep bidirectional transformers is designed to extract bidirectional representations automatically, which are musicological insight. In contrast to other deep-learning models, such as recurrent neural network (RNN)-based models, the MPE method enables parallelization over time-steps, leading to faster training. To evaluate the performance of the proposed method, experiments were conducted on the Lakh MIDI music dataset. During MPE training, approximately 400,000 MIDI segments were utilized for the MPE, for which the recovery accuracy rate reached 97%. In the music genre classification task, the accuracy rate and other indicators of the proposed method were more than 94%. The experimental results indicate that the proposed method improves classification performance compared with state-of-the-art models.
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Kompalli, Prasanna Lakshmi, and Ramesh Kumar Cherku. "Efficient Mining of Data Streams Using Associative Classification Approach." International Journal of Software Engineering and Knowledge Engineering 25, no. 03 (April 2015): 605–31. http://dx.doi.org/10.1142/s0218194015500059.
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Data stream associative classification poses many challenges to the data mining community. In this paper, we address four major challenges posed, namely, infinite length, extraction of knowledge with single scan, processing time, and accuracy. Since data streams are infinite in length, it is impractical to store and use all the historical data for training. Mining such streaming data for knowledge acquisition is a unique opportunity and even a tough task. A streaming algorithm must scan data once and extract knowledge. While mining data streams, processing time, and accuracy have become two important aspects. In this paper, we propose PSTMiner which considers the nature of data streams and provides an efficient classifier for predicting the class label of real data streams. It has greater potential when compared with many existing classification techniques. Additionally, we propose a compact novel tree structure called PSTree (Prefix Streaming Tree) for storing data. Extensive experiments conducted on 24 real datasets from UCI repository and synthetic datasets from MOA (Massive Online Analysis) show that PSTMiner is consistent. Empirical results show that performance of PSTMiner is highly competitive in terms of accuracy and performance time when compared with other approaches under windowed streaming model.
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Joffe, Erel, Emily J. Pettigrew, Jorge R. Herskovic, Charles F. Bearden, and Elmer V. Bernstam. "Expert guided natural language processing using one-class classification." Journal of the American Medical Informatics Association 22, no. 5 (June 10, 2015): 962–66. http://dx.doi.org/10.1093/jamia/ocv010.
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Abstract Introduction Automatically identifying specific phenotypes in free-text clinical notes is critically important for the reuse of clinical data. In this study, the authors combine expert-guided feature (text) selection with one-class classification for text processing. Objectives To compare the performance of one-class classification to traditional binary classification; to evaluate the utility of feature selection based on expert-selected salient text (snippets); and to determine the robustness of these models with respects to irrelevant surrounding text. Methods The authors trained one-class support vector machines (1C-SVMs) and two-class SVMs (2C-SVMs) to identify notes discussing breast cancer. Manually annotated visit summary notes (88 positive and 88 negative for breast cancer) were used to compare the performance of models trained on whole notes labeled as positive or negative to models trained on expert-selected text sections (snippets) relevant to breast cancer status. Model performance was evaluated using a 70:30 split for 20 iterations and on a realistic dataset of 10 000 records with a breast cancer prevalence of 1.4%. Results When tested on a balanced experimental dataset, 1C-SVMs trained on snippets had comparable results to 2C-SVMs trained on whole notes (F = 0.92 for both approaches). When evaluated on a realistic imbalanced dataset, 1C-SVMs had a considerably superior performance (F = 0.61 vs. F = 0.17 for the best performing model) attributable mainly to improved precision (p = .88 vs. p = .09 for the best performing model). Conclusions 1C-SVMs trained on expert-selected relevant text sections perform better than 2C-SVMs classifiers trained on either snippets or whole notes when applied to realistically imbalanced data with low prevalence of the positive class.
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Hadj Irid, Sidi Mohamed, Samir Kameche, and Said Assous. "A Novel Algorithm to Estimate Closely Spaced Source DOA." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (August 1, 2017): 2109. http://dx.doi.org/10.11591/ijece.v7i4.pp2109-2115.
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<p>In order to improve resolution and direction of arrival (DOA) estimation of two closely spaced sources, in context of array processing, a new algorithm is presented. However, the proposed algorithm combines both spatial sampling technic to widen the resolution and a high resolution method which is the Multiple Signal Classification (MUSIC) to estimate the DOA of two closely spaced sources impinging on the far-field of Uniform Linear Array (ULA). Simulations examples are discussed to demonstrate the performance and the effectiveness of the proposed approach (referred as Spatial sampling MUSIC SS-MUSIC) compared to the classical MUSIC method when it’s used alone in this context.</p>
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Wang, Kun-Ching. "Robust Audio Content Classification Using Hybrid-Based SMD and Entropy-Based VAD." Entropy 22, no. 2 (February 6, 2020): 183. http://dx.doi.org/10.3390/e22020183.
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A robust approach for the application of audio content classification (ACC) is proposed in this paper, especially in variable noise-level conditions. We know that speech, music, and background noise (also called silence) are usually mixed in the noisy audio signal. Based on the findings, we propose a hierarchical ACC approach consisting of three parts: voice activity detection (VAD), speech/music discrimination (SMD), and post-processing. First, entropy-based VAD is successfully used to segment input signal into noisy audio and noise even if variable-noise level is happening. The determinations of one-dimensional (1D)-subband energy information (1D-SEI) and 2D-textural image information (2D-TII) are then formed as a hybrid feature set. The hybrid-based SMD is achieved because the hybrid feature set is input into the classification of the support vector machine (SVM). Finally, a rule-based post-processing of segments is utilized to smoothly determine the output of the ACC system. The noisy audio is successfully classified into noise, speech, and music. Experimental results show that the hierarchical ACC system using hybrid feature-based SMD and entropy-based VAD is successfully evaluated against three available datasets and is comparable with existing methods even in a variable noise-level environment. In addition, our test results with the VAD scheme and hybrid features also shows that the proposed architecture increases the performance of audio content discrimination.
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Ponnaganti, Naga Deepti, and Raju Anitha. "A Novel Ensemble Bagging Classification Method for Breast Cancer Classification Using Machine Learning Techniques." Traitement du Signal 39, no. 1 (February 28, 2022): 229–37. http://dx.doi.org/10.18280/ts.390123.
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Breast cancer is observed as a dangerous disease type for women in the world. The clinical experts stated that early detection of cancer helps in saving lives. To detect cancer in the early stage, medical image processing is observed as an effective field. Medical Image processing with an appropriate classification mechanism improves accuracy and image resource with minimal processing time. To detect breast cancer several machine learning techniques are evolved for cancer classification. However, those machine learning techniques are subjected to increased time consumption and limitation in the accuracy of classification. This paper proposed an Ensemble Bagging Weighted Voting Classification (EBWvc) for the classification of breast cancer. Initially, to resolve to overfit in machine learning bagging is applied for collected data. The ensemble bagging classification provides effective training to machine learning for reduced computational time and improved performance characteristics. The weighted voting is adopted for the classification of cancer in the breast. The performance of proposed EBWvc is analyzed comparatively with consideration accuracy, precision, recall, and F1 -Score. The comparative analysis of results exhibited that proposed EBWvc exhibits improved performance than existing classification techniques.
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Sedona, Rocco, Gabriele Cavallaro, Jenia Jitsev, Alexandre Strube, Morris Riedel, and Jón Benediktsson. "Remote Sensing Big Data Classification with High Performance Distributed Deep Learning." Remote Sensing 11, no. 24 (December 17, 2019): 3056. http://dx.doi.org/10.3390/rs11243056.
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High-Performance Computing (HPC) has recently been attracting more attention in remote sensing applications due to the challenges posed by the increased amount of open data that are produced daily by Earth Observation (EO) programs. The unique parallel computing environments and programming techniques that are integrated in HPC systems are able to solve large-scale problems such as the training of classification algorithms with large amounts of Remote Sensing (RS) data. This paper shows that the training of state-of-the-art deep Convolutional Neural Networks (CNNs) can be efficiently performed in distributed fashion using parallel implementation techniques on HPC machines containing a large number of Graphics Processing Units (GPUs). The experimental results confirm that distributed training can drastically reduce the amount of time needed to perform full training, resulting in near linear scaling without loss of test accuracy.
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Omar, Hoger Khayrolla, and Alaa Khalil Jumaa. "Distributed big data analysis using spark parallel data processing." Bulletin of Electrical Engineering and Informatics 11, no. 3 (June 1, 2022): 1505–15. http://dx.doi.org/10.11591/eei.v11i3.3187.
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Nowadays, the big data marketplace is rising rapidly. The big challenge is finding a system that can store and handle a huge size of data and then processing that huge data for mining the hidden knowledge. This paper proposed a comprehensive system that is used for improving big data analysis performance. It contains a fast big data processing engine using Apache Spark and a big data storage environment using Apache Hadoop. The system tests about 11 Gigabytes of text data which are collected from multiple sources for sentiment analysis. Three different machine learning (ML) algorithms are used in this system which is already supported by the Spark ML package. The system programs were written in Java and Scala programming languages and the constructed model consists of the classification algorithms as well as the pre-processing steps in a figure of ML pipeline. The proposed system was implemented in both central and distributed data processing. Moreover, some datasets manipulation manners have been applied in the system tests to check which manner provides the best accuracy and time performance. The results showed that the system works efficiently for treating big data, it gains excellent accuracy with fast execution time especially in the distributed data nodes.
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Mullick, Sankha Subhra, Shounak Datta, Sourish Gunesh Dhekane, and Swagatam Das. "Appropriateness of performance indices for imbalanced data classification: An analysis." Pattern Recognition 102 (June 2020): 107197. http://dx.doi.org/10.1016/j.patcog.2020.107197.
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Romanchuk, Vitaliy. "Mathematical support and software for data processing in robotic neurocomputer systems." MATEC Web of Conferences 161 (2018): 03004. http://dx.doi.org/10.1051/matecconf/201816103004.
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The paper addresses classification and formal definition of neurocomputer systems for robotic complexes, based on the types of associations among their elements. We suggest analytical expressions for performance evaluation in neural computer information processing, aimed at development of methods, algorithms and software that optimize such systems.