Journal articles: 'Spectrogram analysis'

1

Johnson, Alexander. "An integrated approach for teaching speech spectrogram analysis to engineering students." Journal of the Acoustical Society of America 152, no. 3 (September 2022): 1962–69. http://dx.doi.org/10.1121/10.0014172.

Full text

Abstract:

Spectrogram analysis is a vital skill for learning speech acoustics. Spectrograms are necessary for visualizing cause-effect relationships between speech articulator movements and the resulting sound produced. However, many interpretation techniques needed to read spectrograms are counterintuitive to engineering students who have been taught to use more rigid mathematical formulas. As a result, spectrogram reading is often challenging for these students who do not have prior background in acoustic phonetics. In this paper, a structured, inclusive framework for teaching spectrogram reading to students of engineering backgrounds is presented. Findings from the implementation of these teaching methods in undergraduate and graduate engineering courses at University of California, Los Angeles are also unveiled.

APA, Harvard, Vancouver, ISO, and other styles

2

Li, Hong Ping, and Hong Li. "Establish an Artificial Neural Networks Model to Make Quantitative Analysis about the Capillary Electrophoresis Spectrum." Advanced Materials Research 452-453 (January 2012): 1116–20. http://dx.doi.org/10.4028/www.scientific.net/amr.452-453.1116.

Full text

Abstract:

Simulating the overlapping capillary electrophoresis spectrogram under the dissimilar conditions by the computer system , Choosing the overlapping capillary electrophoresis spectrogram simulated under the different conditions , processing the data to compose a neural network training regulations, Applying the artificial neural networks method to make a quantitative analysis about the multi-component in the overlapping capillary electrophoresis spectrogram,Using: Radial direction primary function neural network model and multi-layered perceptron neural network model. The findings indicated that, along with the increasing of the capillary electrophoresis spectrogram noise level, the related components’ ability of the two kinds of the overlapping capillary electrophoresis spectrogram by neural network model quantitative analysis drop down. Along with the increasing of the capillary electrophoresis spectrogram’s total dissociation degree, the multi-layered perceptron neural network model to the related components’ ability of the overlapping capillary electrophoresis spectum by quantitative analysis raise up.

APA, Harvard, Vancouver, ISO, and other styles

3

Ferreira, Diogo R., Tiago A. Martins, and Paulo Rodrigues. "Explainable deep learning for the analysis of MHD spectrograms in nuclear fusion." Machine Learning: Science and Technology 3, no. 1 (December 30, 2021): 015015. http://dx.doi.org/10.1088/2632-2153/ac44aa.

Full text

Abstract:

Abstract In the nuclear fusion community, there are many specialized techniques to analyze the data coming from a variety of diagnostics. One of such techniques is the use of spectrograms to analyze the magnetohydrodynamic (MHD) behavior of fusion plasmas. Physicists look at the spectrogram to identify the oscillation modes of the plasma, and to study instabilities that may lead to plasma disruptions. One of the major causes of disruptions occurs when an oscillation mode interacts with the wall, stops rotating, and becomes a locked mode. In this work, we use deep learning to predict the occurrence of locked modes from MHD spectrograms. In particular, we use a convolutional neural network with class activation mapping to pinpoint the exact behavior that the model thinks is responsible for the locked mode. Surprisingly, we find that, in general, the model explanation agrees quite well with the physical interpretation of the behavior observed in the spectrogram.

APA, Harvard, Vancouver, ISO, and other styles

4

Liao, Ying. "Analysis of Rehabilitation Occupational Therapy Techniques Based on Instrumental Music Chinese Tonal Language Spectrogram Analysis." Occupational Therapy International 2022 (October 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/1064441.

Full text

Abstract:

This paper provides an in-depth analysis of timbre-speech spectrograms in instrumental music, designs a model analysis of rehabilitation occupational therapy techniques based on the analysis of timbre-speech spectrograms in instrumental music, and tests the models for comparison. Starting from the mechanism of human articulation, this paper models the process of human expression as a time-varying linear system consisting of excitation, vocal tract, and radiation models. The system’s overall architecture is designed according to the characteristics of Chinese speech and everyday speech rehabilitation theory (HSL theory). The dual judgment of temporal threshold and short-time average energy realized the phonetic length training. Tone and clear tone training were achieved by linear predictive coding technique (LPC) and autocorrelation function. Using the DTW technique, isolated word speech recognition was achieved by extracting Mel-scale Frequency Cepstral Coefficients (MFCC) parameters of speech signals. The system designs corresponding training scenes for each training module according to the extracted speech parameters, combines the multimedia speech spectrogram motion situation with the speech parameters, and finally presents the training content as a speech spectrogram, and evaluates the training results through human-machine interaction to stimulate the interest of rehabilitation therapy and realize the speech rehabilitation training of patients. After analyzing the pre- and post-test data, it was found that the p -values of all three groups were <0.05, which was judged to be significantly different. Also, all subjects changed their behavioral data during the treatment. Therefore, it was concluded that the music therapy technique could improve the patients’ active gaze communication ability, verbal command ability, and active question-answering ability after summarizing the data, i.e., the hypothesis of this experiment is valid. Therefore, it is believed that the technique of timbre-speech spectrogram analysis in instrumental music can achieve the effect of rehabilitation therapy to a certain extent.

APA, Harvard, Vancouver, ISO, and other styles

5

Pethiyagoda, Ravindra, Scott W. McCue, and Timothy J. Moroney. "Spectrograms of ship wakes: identifying linear and nonlinear wave signals." Journal of Fluid Mechanics 811 (December 6, 2016): 189–209. http://dx.doi.org/10.1017/jfm.2016.753.

Full text

Abstract:

A spectrogram is a useful way of using short-time discrete Fourier transforms to visualise surface height measurements taken of ship wakes in real-world conditions. For a steadily moving ship that leaves behind small-amplitude waves, the spectrogram is known to have two clear linear components, a sliding-frequency mode caused by the divergent waves and a constant-frequency mode for the transverse waves. However, recent observations of high-speed ferry data have identified additional components of the spectrograms that are not yet explained. We use computer simulations of linear and nonlinear ship wave patterns and apply time–frequency analysis to generate spectrograms for an idealised ship. We clarify the role of the linear dispersion relation and ship speed on the two linear components. We use a simple weakly nonlinear theory to identify higher-order effects in a spectrogram and, while the high-speed ferry data are very noisy, we propose that certain additional features in the experimental data are caused by nonlinearity. Finally, we provide a possible explanation for a further discrepancy between the high-speed ferry spectrograms and linear theory by accounting for ship acceleration.

APA, Harvard, Vancouver, ISO, and other styles

6

Oh, Myeonggeun, and Yong-Hoon Kim. "Statistical Approach to Spectrogram Analysis for Radio-Frequency Interference Detection and Mitigation in an L-Band Microwave Radiometer." Sensors 19, no. 2 (January 14, 2019): 306. http://dx.doi.org/10.3390/s19020306.

Full text

Abstract:

For the elimination of radio-frequency interference (RFI) in a passive microwave radiometer, the threshold level is generally calculated from the mean value and standard deviation. However, a serious problem that can arise is an error in the retrieved brightness temperature from a higher threshold level owing to the presence of RFI. In this paper, we propose a method to detect and mitigate RFI contamination using the threshold level from statistical criteria based on a spectrogram technique. Mean and skewness spectrograms are created from a brightness temperature spectrogram by shifting the 2-D window to discriminate the form of the symmetric distribution as a natural thermal emission signal. From the remaining bins of the mean spectrogram eliminated by RFI-flagged bins in the skewness spectrogram for data captured at 0.1-s intervals, two distribution sides are identically created from the left side of the distribution by changing the standard position of the distribution. Simultaneously, kurtosis calculations from these bins for each symmetric distribution are repeatedly performed to determine the retrieved brightness temperature corresponding to the closest kurtosis value of three. The performance is evaluated using experimental data, and the maximum error and root-mean-square error (RMSE) in the retrieved brightness temperature are served to be less than approximately 3 K and 1.7 K, respectively, from a window with a size of 100 × 100 time–frequency bins according to the RFI levels and cases.

APA, Harvard, Vancouver, ISO, and other styles

7

Pizetta, Igor Henrique Beloti, Alexandre Santos Brandão, and Mário Sarcinelli-Filho. "UAV Thrust Model Identification Using Spectrogram Analysis." Automation 2, no. 3 (August 1, 2021): 141–52. http://dx.doi.org/10.3390/automation2030009.

Full text

Abstract:

This paper deals with a non-contact method to identify the aerodynamic propeller constants of the Parrot AR.Drone quadrotor. The experimental setup consists of a microphone installed in the flight arena to record audio data. In terms of methodology, a spectrogram analysis is adopted to estimate the propeller velocity based on the filtered sound signal. It is known that, in a hovering maneuver, when the UAV mass increases, the propellers rotate faster to produce the necessary thrust increment. In this work, the rotorcraft takes off with its factory settings, first with no hull, corresponding to a mass of 413 g, and after with a small hull, corresponding to a mass of 444 g, and a bigger hull, corresponding to a mass of 462 g. In the sequence, the velocity of the propellers are estimated for each of these three cases using spectrograms of audio recorded by a microphone, corresponding to the sound generated by the four rotors. Finally, the estimated velocity is used to identify the aerodynamic parameters, thus validating the proposal.

APA, Harvard, Vancouver, ISO, and other styles

8

Franzoni, Valentina, Giulio Biondi, and Alfredo Milani. "Emotional sounds of crowds: spectrogram-based analysis using deep learning." Multimedia Tools and Applications 79, no. 47-48 (August 17, 2020): 36063–75. http://dx.doi.org/10.1007/s11042-020-09428-x.

Full text

Abstract:

AbstractCrowds express emotions as a collective individual, which is evident from the sounds that a crowd produces in particular events, e.g., collective booing, laughing or cheering in sports matches, movies, theaters, concerts, political demonstrations, and riots. A critical question concerning the innovative concept of crowd emotions is whether the emotional content of crowd sounds can be characterized by frequency-amplitude features, using analysis techniques similar to those applied on individual voices, where deep learning classification is applied to spectrogram images derived by sound transformations. In this work, we present a technique based on the generation of sound spectrograms from fragments of fixed length, extracted from original audio clips recorded in high-attendance events, where the crowd acts as a collective individual. Transfer learning techniques are used on a convolutional neural network, pre-trained on low-level features using the well-known ImageNet extensive dataset of visual knowledge. The original sound clips are filtered and normalized in amplitude for a correct spectrogram generation, on which we fine-tune the domain-specific features. Experiments held on the finally trained Convolutional Neural Network show promising performances of the proposed model to classify the emotions of the crowd.

APA, Harvard, Vancouver, ISO, and other styles

9

Sun, Hong-ying, Hong-wu Yang, and Zhong-xing Tao. "Realization of Spectrogram Analysis with FPGA." Journal of Electronics & Information Technology 33, no. 5 (May 12, 2011): 1238–42. http://dx.doi.org/10.3724/sp.j.1146.2010.01005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

ELEMANS, COEN P. H., KIER HEECK, and MEES MULLER. "SPECTROGRAM ANALYSIS OF ANIMAL SOUND PRODUCTION." Bioacoustics 18, no. 2 (January 2008): 183–212. http://dx.doi.org/10.1080/09524622.2008.9753599.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Król, Andrzej, and Tomasz Szymczyk. "Comparative analysis of the quality of recorded sound in the function of different recording formats." Journal of Computer Sciences Institute 24 (September 30, 2022): 189–94. http://dx.doi.org/10.35784/jcsi.2934.

Full text

Abstract:

In article, the quality of the following encoders was analyzed: mp3, AAC, wma and OGG Vorbis. An original graphic method was used to carry out the quantitative research. It consists in comparing the number of pixels (representing data) between the spectrogram of a wav file and the spectrograms of files compressed with different codecs and bit rates. It has been shown that the Ogg Vorbis encoder retains the most data from the uncompressed wav sample in all tested bit rates (128KBit / s, 160KBit / s, 320KBit / s).

APA, Harvard, Vancouver, ISO, and other styles

12

Choi, Byung-Moon, Ji Yeon Yim, Hangsik Shin, and Gyujeong Noh. "Novel Analgesic Index for Postoperative Pain Assessment Based on a Photoplethysmographic Spectrogram and Convolutional Neural Network: Observational Study." Journal of Medical Internet Research 23, no. 2 (February 3, 2021): e23920. http://dx.doi.org/10.2196/23920.

Full text

Abstract:

Background Although commercially available analgesic indices based on biosignal processing have been used to quantify nociception during general anesthesia, their performance is low in conscious patients. Therefore, there is a need to develop a new analgesic index with improved performance to quantify postoperative pain in conscious patients. Objective This study aimed to develop a new analgesic index using photoplethysmogram (PPG) spectrograms and a convolutional neural network (CNN) to objectively assess pain in conscious patients. Methods PPGs were obtained from a group of surgical patients for 6 minutes both in the absence (preoperatively) and in the presence (postoperatively) of pain. Then, the PPG data of the latter 5 minutes were used for analysis. Based on the PPGs and a CNN, we developed a spectrogram–CNN index for pain assessment. The area under the curve (AUC) of the receiver-operating characteristic curve was measured to evaluate the performance of the 2 indices. Results PPGs from 100 patients were used to develop the spectrogram–CNN index. When there was pain, the mean (95% CI) spectrogram–CNN index value increased significantly—baseline: 28.5 (24.2-30.7) versus recovery area: 65.7 (60.5-68.3); P<.01. The AUC and balanced accuracy were 0.76 and 71.4%, respectively. The spectrogram–CNN index cutoff value for detecting pain was 48, with a sensitivity of 68.3% and specificity of 73.8%. Conclusions Although there were limitations to the study design, we confirmed that the spectrogram–CNN index can efficiently detect postoperative pain in conscious patients. Further studies are required to assess the spectrogram–CNN index’s feasibility and prevent overfitting to various populations, including patients under general anesthesia. Trial Registration Clinical Research Information Service KCT0002080; https://cris.nih.go.kr/cris/search/search_result_st01.jsp?seq=6638

APA, Harvard, Vancouver, ISO, and other styles

13

Bruni, Vittoria, Michela Tartaglione, and Domenico Vitulano. "A Fast and Robust Spectrogram Reassignment Method." Mathematics 7, no. 4 (April 19, 2019): 358. http://dx.doi.org/10.3390/math7040358.

Full text

Abstract:

The improvement of the readability of time-frequency transforms is an important topic in the field of fast-oscillating signal processing. The reassignment method is often used due to its adaptivity to different transforms and nice formal properties. However, it strongly depends on the selection of the analysis window and it requires the computation of the same transform using three different but well-defined windows. The aim of this work is to provide a simple method for spectrogram reassignment, named FIRST (Fast Iterative and Robust Reassignment Thinning), with comparable or better precision than classical reassignment method, a reduced computational effort, and a near independence of the adopted analysis window. To this aim, the time-frequency evolution of a multicomponent signal is formally provided and, based on this law, only a subset of time-frequency points is used to improve spectrogram readability. Those points are the ones less influenced by interfering components. Preliminary results show that the proposed method can efficiently reassign spectrograms more accurately than the classical method in the case of interfering signal components, with a significant gain in terms of required computational effort.

APA, Harvard, Vancouver, ISO, and other styles

14

Palupi, Indiati Retno, and Wiji Raharjo. "The Utilization of Signal Analysis by Using Short Time Fourier Transform." RSF Conference Series: Engineering and Technology 1, no. 1 (December 23, 2021): 30–36. http://dx.doi.org/10.31098/cset.v1i1.445.

Full text

Abstract:

Signal Analysis is a part of geophysics work. It is important in analyse the character of signal or waveform in geophysics. In this paper the earthquake waveform is used as the example. One method to do this is used Short Time Fourier Transform. It adopts the basic concept of Fast Fourier Transform in the short period of time in waveform and at the same moment there is a convolutional process between the waveform and the mother wavelet and then resulting the spectrogram. Finally, the spectrogram will show the power spectrum or the magnitude of the amplitude in each time in the waveform. It relates with the energy of the earthquake. The result including three parameters, they are time, frequency and the spectrogram. It makes easier for the geophysicist to analyse the frequency changing in each time based on the spectrogram colour. Besides that, it can be used to identify the arrival time of P and S wave as the important information in calculate the hypocentre location of the earthquake.

APA, Harvard, Vancouver, ISO, and other styles

15

Rao, G. Manmadha, and N. Srinivasa Rao. "Speech Signal Analysis Using Concentrated Spectrogram Method." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 5 (May 31, 2015): 127–32. http://dx.doi.org/10.14257/ijsip.2015.8.5.13.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Mekaoui, S., A. Houacine, and T. Gharbi. "Reassigned Spectrogram Representation for Mechanomyogram Signal Analysis." Sensor Letters 6, no. 4 (August 1, 2008): 465–76. http://dx.doi.org/10.1166/sl.2008.401.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Pinkowski, Ben. "Principal component analysis of speech spectrogram images." Pattern Recognition 30, no. 5 (May 1997): 777–87. http://dx.doi.org/10.1016/s0031-3203(96)00103-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Kuo, Ho‐Chang, Chi‐Chun Lo, Yan‐Di Wang, Jheng‐Dao Wu, and Bor‐Shyh Lin. "Spectrogram for childhood asthma detection and analysis." Allergy 74, no. 9 (April 15, 2019): 1783–86. http://dx.doi.org/10.1111/all.13768.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Rees, Catherine J., P. David Blalock, Shannon E. Kemp, Stacey L. Halum, and Jamie A. Koufman. "Differentiation of adductor-type spasmodic dysphonia from muscle tension dysphonia by spectral analysis." Otolaryngology–Head and Neck Surgery 137, no. 4 (October 2007): 576–81. http://dx.doi.org/10.1016/j.otohns.2007.03.040.

Full text

Abstract:

Objectives To determine the utility of spectral analysis in the differentiation of adductor-type spasmodic dysphonia (AdSD) from muscle tension dysphonia (MTD). Study Design Prospective blinded study. Methods Forty-seven samples of AdSD-connected speech spectrograms from 27 subjects and 17 samples of MTD-connected speech spectrograms from 15 subjects were selected from clinical charts and de-identified. These spectrograms were reviewed independently and blindly by two speech language pathologists experienced in spectrography. The speech language pathologists designated the spectrogram as consistent with AdSD and MTD, and these designations were compared with actual clinical diagnoses. Results The ability to differentiate AdSD from MTD with spectral analysis was 94% for rater #1 and 98% for rater #2. No MTD subjects were incorrectly diagnosed as having SD (100% specificity). Conclusions This study suggests that experienced speech language pathologists can distinguish AdSD from MTD with a high degree of sensitivity and specificity based on spectral analysis. Spectral analysis is especially useful in cases where perceptual analysis and clinical evaluation alone are insufficient.

APA, Harvard, Vancouver, ISO, and other styles

20

Vega, Giovany, Carlos J. Corrada-Bravo, and T. Mitchell Aide. "Audio segmentation using Flattened Local Trimmed Range for ecological acoustic space analysis." PeerJ Computer Science 2 (June 27, 2016): e70. http://dx.doi.org/10.7717/peerj-cs.70.

Full text

Abstract:

The acoustic space in a given environment is filled with footprints arising from three processes: biophony, geophony and anthrophony. Bioacoustic research using passive acoustic sensors can result in thousands of recordings. An important component of processing these recordings is to automate signal detection. In this paper, we describe a new spectrogram-based approach for extracting individual audio events. Spectrogram-based audio event detection (AED) relies on separating the spectrogram into background (i.e., noise) and foreground (i.e., signal) classes using a threshold such as a global threshold, a per-band threshold, or one given by a classifier. These methods are either too sensitive to noise, designed for an individual species, or require prior training data. Our goal is to develop an algorithm that is not sensitive to noise, does not need any prior training data and works with any type of audio event. To do this, we propose: (1) a spectrogram filtering method, the Flattened Local Trimmed Range (FLTR) method, which models the spectrogram as a mixture of stationary and non-stationary energy processes and mitigates the effect of the stationary processes, and (2) an unsupervised algorithm that uses the filter to detect audio events. We measured the performance of the algorithm using a set of six thoroughly validated audio recordings and obtained a sensitivity of 94% and a positive predictive value of 89%. These sensitivity and positive predictive values are very high, given that the validated recordings are diverse and obtained from field conditions. The algorithm was then used to extract audio events in three datasets. Features of these audio events were plotted and showed the unique aspects of the three acoustic communities.

APA, Harvard, Vancouver, ISO, and other styles

21

Burriel-Valencia, Jordi, Ruben Puche-Panadero, Javier Martinez-Roman, Angel Sapena-Baño, Martin Riera-Guasp, and Manuel Pineda-Sánchez. "Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines." Energies 12, no. 17 (August 31, 2019): 3361. http://dx.doi.org/10.3390/en12173361.

Full text

Abstract:

Induction machines drive many industrial processes and their unexpected failure can cause heavy production losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, and so forth. In these cases, an analysis in the time-frequency domain—such as a spectrogram—is required for detecting faults signatures. The spectrogram is built using the short time Fourier transform, but its resolution depends critically on the time window used to generate it—short windows provide good time resolution but poor frequency resolution, just the opposite than long windows. Therefore, the window must be adapted at each time to the shape of the expected fault harmonics, by highly skilled maintenance personnel. In this paper this problem is solved with the design of a new multi-band window, which generates simultaneously many different narrow-band current spectrograms and combines them into as single, high resolution one, without the need of manual adjustments. The proposed method is validated with the diagnosis of bar breakages during the start-up of a commercial induction motor.

APA, Harvard, Vancouver, ISO, and other styles

22

Varachitchai, Napol, and Chowarit Mitsantisuk. "An Analysis of Force Response by Using Spectrogram." Applied Mechanics and Materials 781 (August 2015): 466–70. http://dx.doi.org/10.4028/www.scientific.net/amm.781.466.

Full text

Abstract:

In haptic technology, robot has to contact with objects and human operator. The robot systems must be flexible systems, high accuracy and precision during the operation for safe the human operator. So, analysis of the force response is a necessary ability to design the controller of the robot systems. This paper proposes a methodology to analyze force response by mean of spectrogram analysis. The master-slave robot based on bilateral control is used by human operator. Disturbance Observe (DOB) is used to estimate the force response instead of force sensor to improve the performance of the whole system. Finally, force response is analyzed by spectrogram. The availability of the proposed method is shown in an experiment.

APA, Harvard, Vancouver, ISO, and other styles

23

AL-MANIE, M. A., and W. J. WANG. "TIME-FREQUENCY ANALYSIS BY EVOLUTIONARY PERIODOGRAM WITH APPLICATION IN GEAR FAULT DIAGNOSIS." International Journal of Wavelets, Multiresolution and Information Processing 08, no. 05 (September 2010): 679–93. http://dx.doi.org/10.1142/s0219691310003742.

Full text

Abstract:

The evolutionary periodogram has been introduced to mechanical fault diagnosis and relationship between the evolutionary periodogram and time-frequency spectrogram has been investigated. The evolutionary periodogram is unveiled as an especially windowed spectrogram, and is applied to gearbox fault diagnosis. It has been shown that the window used in the evolutionary periodogram is not a single function but a combination of a set of functions. Two cases of gearbox diagnosis are presented as examples of application. Vibration signals and a synchronous signal are collected for the analysis. The time synchronous averaging is used to reduce background noise or random transients to enhance the periodicity of a specific gear rotation. The performance of the evolutionary periodogram has been compared with the spectrogram for gear diagnosis, showing that the evolutionary periodogram is an alternative technique in time-frequency analysis for fault detection and better resolution can be obtained as more choices are offered by the way of constructing the window.

APA, Harvard, Vancouver, ISO, and other styles

24

Jiaa, Chi L. "Spectrogram analysis of random laser texture pattern media." Surface and Coatings Technology 123, no. 2-3 (January 2000): 140–46. http://dx.doi.org/10.1016/s0257-8972(99)00476-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

LIN, BOR-SHING, BOR-SHYH LIN, HUEY-DONG WU, FOK-CHING CHONG, and SAO-JIE CHEN. "WHEEZE RECOGNITION BASED ON 2D BILATERAL FILTERING OF SPECTROGRAM." Biomedical Engineering: Applications, Basis and Communications 18, no. 03 (June 25, 2006): 128–37. http://dx.doi.org/10.4015/s1016237206000221.

Full text

Abstract:

This paper describes the design of a low-cost and high performance wheeze recognition system. First, respiratory sounds are captured, amplified and filtered by an analog circuit; then digitized through a PC soundcard, and recorded in accordance with the Computerized Respiratory Sound Analysis (CORSA) standards. Since the proposed wheeze detection algorithm is based on the spectrogram processing of respiratory sounds, spectrograms generated from recorded sounds have to pass through a 2D bilateral filter for edge-preserving smoothing. Finally, the processed spectra go through an edge detection procedure to recognize wheeze sounds.Experiment results show a high sensitivity of 0.967 and a specificity of 0.909 in qualitative analysis of wheeze recognition. Due to its high efficiency, great performance and easy-to-implement features, this wheeze recognition system could be of interest in the clinical monitoring of asthma patients and the study of physiological mechanisms in the respiratory airways.

APA, Harvard, Vancouver, ISO, and other styles

26

H. Jopri, M., A. R. Abdullah, M. Manap, T. Sutikno, and M. R. Ab Ghani. "Harmonic Contribution Analysis of Electric Arc Furnace by Using Spectrogram." Bulletin of Electrical Engineering and Informatics 7, no. 2 (June 1, 2018): 236–43. http://dx.doi.org/10.11591/eei.v7i2.1187.

Full text

Abstract:

In this paper, spectrogram, a fast and accurate technique is introduced for the analysis of the contribution. Based on a rule-based classifier and the threshold settings that referred to the IEEE Standard 1159 2009, the analysis of the harmonic and interharmonic contribution of EAF are carried out successfully. Moreover, the impact of contribution is measured using total harmonic distortion (THD) and total non-harmonic distortion (TnHD). In addition, spectrogram also gives 100 percent correct detection and able to analyze the contribution impact. It is proven that the proposed method is accurate, fast and cost efficient for analyzing the impact of harmonic and interharmonic of EAF.

APA, Harvard, Vancouver, ISO, and other styles

27

Liu, Lin-li, Yi Pang, and Zhuang-li Hu. "Application of Spectrogram Analysis in Traditional Vocal Music Teaching and Multimedia Animation Vocal Music Teaching." International Journal of Emerging Technologies in Learning (iJET) 11, no. 11 (November 29, 2016): 64. http://dx.doi.org/10.3991/ijet.v11i11.6242.

Full text

Abstract:

The spectrogram analysis technology via computer application makes voice visualized, realizes the integrated teaching mode of "mouth to ear- nose", and improves the teaching quality of vocal music.The technology gives a dynamic map of singing respiration and singing organs, and the singing process is specified by the harmony search algorithm. This technology solves problems of shallow breathing, slurred speech and incorrect use of resonant cavity. The spectrogram analysis visualizes vocal music teaching, improves students' abilities of singing, practice and innovation.

APA, Harvard, Vancouver, ISO, and other styles

28

Neralla, Manikanta. "Design and Performance Analysis of Short Time Fourier Transform Processor." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 3205–15. http://dx.doi.org/10.22214/ijraset.2022.41917.

Full text

Abstract:

Abstract: Time-frequency domain characterization of signals have always been focused on variants of Short time Fourier transform (STFT). The selection of transform kernel plays an important role in preserving the signal support which provides a cross-term free time-frequency distribution. Time-Bandwidth product has been taken as a measure of signal support preservation criteria thereby developing an optimal kernel for STFT based on linear canonical decomposition. In the development of kernel , Fractional Fourier Transform (FrFT) is used which provides noise free frequency domain representation .With the help of developed transform kernel , the magnitude-wise shift invariance property is verified and timefrequency content is analyzed by plotting spectrogram. Keywords: STFT, FrFT, kernel, Time-Bandwidth product, spectrogram.

APA, Harvard, Vancouver, ISO, and other styles

29

Premoli, Marika, Daniele Baggi, Marco Bianchetti, Alessandro Gnutti, Marco Bondaschi, Andrea Mastinu, Pierangelo Migliorati, et al. "Automatic classification of mice vocalizations using Machine Learning techniques and Convolutional Neural Networks." PLOS ONE 16, no. 1 (January 19, 2021): e0244636. http://dx.doi.org/10.1371/journal.pone.0244636.

Full text

Abstract:

Ultrasonic vocalizations (USVs) analysis is a well-recognized tool to investigate animal communication. It can be used for behavioral phenotyping of murine models of different disorders. The USVs are usually recorded with a microphone sensitive to ultrasound frequencies and they are analyzed by specific software. Different calls typologies exist, and each ultrasonic call can be manually classified, but the qualitative analysis is highly time-consuming. Considering this framework, in this work we proposed and evaluated a set of supervised learning methods for automatic USVs classification. This could represent a sustainable procedure to deeply analyze the ultrasonic communication, other than a standardized analysis. We used manually built datasets obtained by segmenting the USVs audio tracks analyzed with the Avisoft software, and then by labelling each of them into 10 representative classes. For the automatic classification task, we designed a Convolutional Neural Network that was trained receiving as input the spectrogram images associated to the segmented audio files. In addition, we also tested some other supervised learning algorithms, such as Support Vector Machine, Random Forest and Multilayer Perceptrons, exploiting informative numerical features extracted from the spectrograms. The performance showed how considering the whole time/frequency information of the spectrogram leads to significantly higher performance than considering a subset of numerical features. In the authors’ opinion, the experimental results may represent a valuable benchmark for future work in this research field.

APA, Harvard, Vancouver, ISO, and other styles

30

Reddy, A. Pramod, and Vijayarajan V. "Fusion Based AER System Using Deep Learning Approach for Amplitude and Frequency Analysis." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 3 (May 31, 2022): 1–19. http://dx.doi.org/10.1145/3488369.

Full text

Abstract:

Automatic emotion recognition from Speech (AERS) systems based on acoustical analysis reveal that some emotional classes persist with ambiguity. This study employed an alternative method aimed at providing deep understanding into the amplitude–frequency, impacts of various emotions in order to aid in the advancement of near term, more effectively in classifying AER approaches. The study was undertaken by converting narrow 20 ms frames of speech into RGB or grey-scale spectrogram images. The features have been used to fine-tune a feature selection system that had previously been trained to recognise emotions. Two different Linear and Mel spectral scales are used to demonstrate a spectrogram. An inductive approach for in sighting the amplitude and frequency features of various emotional classes. We propose a two-channel profound combination of deep fusion network model for the efficient categorization of images. Linear and Mel- spectrogram is acquired from Speech-signal, which is prepared in the recurrence area to input Deep Neural Network. The proposed model Alex-Net with five convolutional layers and two fully connected layers acquire most vital features form spectrogram images plotted on the amplitude-frequency scale. The state-of-the-art is compared with benchmark dataset (EMO-DB). RGB and saliency images are fed to pre-trained Alex-Net tested both EMO-DB and Telugu dataset with an accuracy of 72.18% and fused image features less computations reaching to an accuracy 75.12%. The proposed model show that Transfer learning predict efficiently than Fine-tune network. When tested on Emo-DB dataset, the propȯsed system adequately learns discriminant features from speech spectrȯgrams and outperforms many stȧte-of-the-art techniques.

APA, Harvard, Vancouver, ISO, and other styles

31

Njirjak, Marko, Erik Otović, Dario Jozinović, Jonatan Lerga, Goran Mauša, Alberto Michelini, and Ivan Štajduhar. "The Choice of Time–Frequency Representations of Non-Stationary Signals Affects Machine Learning Model Accuracy: A Case Study on Earthquake Detection from LEN-DB Data." Mathematics 10, no. 6 (March 17, 2022): 965. http://dx.doi.org/10.3390/math10060965.

Full text

Abstract:

Non-stationary signals are often analyzed using raw waveform data or spectrograms of those data; however, the possibility of alternative time–frequency representations being more informative than the original data or spectrograms is yet to be investigated. This paper tested whether alternative time–frequency representations could be more informative for machine learning classification of seismological data. The mentioned hypothesis was evaluated by training three well-established convolutional neural networks using nine time–frequency representations. The results were compared to the base model, which was trained on the raw waveform data. The signals that were used in the experiment are three-component seismogram instances from the Local Earthquakes and Noise DataBase (LEN-DB). The results demonstrate that Pseudo Wigner–Ville and Wigner–Ville time–frequency representations yield significantly better results than the base model, while spectrogram and Margenau–Hill perform significantly worse (p < 0.01). Interestingly, the spectrogram, which is often used in signal analysis, had inferior performance when compared to the base model. The findings presented in this research could have notable impacts in the fields of geophysics and seismology as the phenomena that were previously hidden in the seismic noise are now more easily identified. Furthermore, the results indicate that applying Pseudo Wigner–Ville or Wigner–Ville time–frequency representations could result in a large increase in earthquakes in the catalogs and lessen the need to add new stations with an overall reduction in the costs. Finally, the proposed approach of extracting valuable information through time–frequency representations could be applied in other domains as well, such as electroencephalogram and electrocardiogram signal analysis, speech recognition, gravitational waves investigation, and so on.

APA, Harvard, Vancouver, ISO, and other styles

32

CHO, Hyunjin, Junseok LIM, Bonhwa KU, Myoungjun CHEONG, Iksu SEO, Hanseok KO, and Wooyoung HONG. "Underwater Radiated Signal Analysis in the Modulation Spectrogram Domain." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E98.A, no. 8 (2015): 1751–59. http://dx.doi.org/10.1587/transfun.e98.a.1751.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Konatham, Saikrishna Reddy, Hugues Guillet de Chatellus, and Jose Azana. "Photonics-Based Real-Time Spectrogram Analysis of Broadband Waveforms." Journal of Lightwave Technology 38, no. 19 (October 1, 2020): 5356–67. http://dx.doi.org/10.1109/jlt.2020.3010516.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Verdaguer, José M., Carmen Górriz, María P. Prim, Antonio J. del Palacio, Javier Gavilán, and Juan I. de Diego. "Analysis of Changes in the Spectrogram Following Endotracheal Intubation." Acta Otorrinolaringologica (English Edition) 59, no. 5 (January 2008): 217–22. http://dx.doi.org/10.1016/s2173-5735(08)70226-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Sheikh-Zade, Yu R., G. Kh Mukhambetaliev, I. L. Cherednik, and A. P. Galenko-Yaroshevskii. "Structural Analysis of Heart Rhythm Spectrogram in Narcotized Cats." Bulletin of Experimental Biology and Medicine 140, no. 4 (October 2005): 375–77. http://dx.doi.org/10.1007/s10517-005-0493-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Yang, Yuan, Jian Gang Bi, Hong Jie Wang, and Nan Meng. "Study of the Ultrasonic Characteristics of Typical Partial Discharge on GIS." Advanced Materials Research 753-755 (August 2013): 2200–2207. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2200.

Full text

Abstract:

This Paper studied the typical PD in GIS by ultrasonic method. A GIS PD simulating and testing system is established, including a PD simulating system, an ultrasonic detecting system and a conventional pulse current method measurement system. The thesis focuses on four typical kinds of PD signals in GIS, including metal particles, bad contacts on high and grounding potential conductor, the protrusion on the high potential conductor. The statistics characteristics are obtained which include N-Φ spectrogram (relationship between discharge frequency and discharge phase Spectrogram) and Umax-Φ spectrogram (relationship between discharge amplitude and discharge phase Spectrogram). Experimental results show that low-frequency ultrasonic sensors can detect this four PD signals， signal spectra analysis shows that ultrasonic signals of different statistical model are with different characteristics. The results provide test data for the pattern recognition of Partial Discharge in GIS.

APA, Harvard, Vancouver, ISO, and other styles

37

Cairong, Zou, Zhang Xinran, Zha Cheng, and Zhao Li. "A Novel DBN Feature Fusion Model for Cross-Corpus Speech Emotion Recognition." Journal of Electrical and Computer Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/7437860.

Full text

Abstract:

The feature fusion from separate source is the current technical difficulties of cross-corpus speech emotion recognition. The purpose of this paper is to, based on Deep Belief Nets (DBN) in Deep Learning, use the emotional information hiding in speech spectrum diagram (spectrogram) as image features and then implement feature fusion with the traditional emotion features. First, based on the spectrogram analysis by STB/Itti model, the new spectrogram features are extracted from the color, the brightness, and the orientation, respectively; then using two alternative DBN models they fuse the traditional and the spectrogram features, which increase the scale of the feature subset and the characterization ability of emotion. Through the experiment on ABC database and Chinese corpora, the new feature subset compared with traditional speech emotion features, the recognition result on cross-corpus, distinctly advances by 8.8%. The method proposed provides a new idea for feature fusion of emotion recognition.

APA, Harvard, Vancouver, ISO, and other styles

38

Boro, Jogen. "SPECTROGRAM STUDY OF BODO VOWELS." International Journal of Research -GRANTHAALAYAH 3, no. 9 (September 30, 2015): 122–28. http://dx.doi.org/10.29121/granthaalayah.v3.i9.2015.2952.

Full text

Abstract:

Bodo Language is phonetically rich language but it is not analysed systematically and scientifically still now. So each and every parts of language is to be studied with the help of modern equipments for find out the accurate results. In the age of Information Technology where the Information Technology (IT) is threading the entire into a Global Village with knowledge as the prime currency and sole differentiator, development of appropriate access technology take part vital role for gaining the information. Especially for India, with its multi-lingual requirements and not so fortunate achievements in terms of overall literacy, development of speech technology in each of its recognized language demands utmost attention. Moreover, development of such Speech Technologies in Indian Languages, with their core dependence on linguistic and cultural ethos, need to be developed largely in India. From the present analysis and study of Bodo vowels spectrograms, it is seen that the lower frequency regions for almost all vowels are very clear. It is a very uncommon characteristic observed in case of the Bodo vowel utterances in comparison with other local languages of Assam. So this will help the speech researcher’s on Bodo Language in various directions in future.

APA, Harvard, Vancouver, ISO, and other styles

39

Tretiakov, Igor Aleksandrovich. "Structural-linguistic approach to the description of spectrogram of electromagnetic radiation spectrograms generated by computing technology." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2023, no. 1 (January 31, 2023): 64–70. http://dx.doi.org/10.24143/2072-9502-2023-1-64-70.

Full text

Abstract:

In this paper, the solution of an urgent scientific and technical problem in the field of information security is considered – the problem of detecting electromagnetic radiation generated by computer technology, within the framework of a structural and linguistic approach to the analysis of experimental data. The implementation of the stage of assigning segmented sections of spectrograms of symbols of some alphabet corresponding to certain types of behavior is considered in detail. As well as the implementation of the analysis stage of the received sequences of symbols. A model of morphological grammar construction and a mechanism for generating texts that differ little from the correct ones are described. The grammar formation stage was implemented in the Python 3.7 programming language. The choice of this programming language is justified by its cross-platform nature, low entry threshold, as well as wide application: from automation of mathematical calculations and machine learning, to the development of web applications. Also, one of the advantages of this language is the availability of many high-quality libraries, including those used for current development. A statistical experiment was conducted to evaluate the effectiveness and accuracy of the developed algorithms. An estimate of the probability of the correct classification of linguistic chains of experimental spectrogram curves into the desired groups and a general assessment of the accuracy of the correct determination of all curves into the desired groups is given. As a result of the application of a structural-linguistic approach to the problem of detecting electromagnetic radiation generated by computer technology, it can be concluded that the obtained linguistic descriptions of the studied spectrograms represent short and reliable rules for their analysis and allow for high accuracy in automated mode to determine deviations in the observed spectra from the specified standards.

APA, Harvard, Vancouver, ISO, and other styles

40

Lambamo, Wondimu, Ramasamy Srinivasagan, and Worku Jifara. "Analyzing Noise Robustness of Cochleogram and Mel Spectrogram Features in Deep Learning Based Speaker Recognition." Applied Sciences 13, no. 1 (December 31, 2022): 569. http://dx.doi.org/10.3390/app13010569.

Full text

Abstract:

The performance of speaker recognition systems is very well on the datasets without noise and mismatch. However, the performance gets degraded with the environmental noises, channel variation, physical and behavioral changes in speaker. The types of Speaker related feature play crucial role in improving the performance of speaker recognition systems. Gammatone Frequency Cepstral Coefficient (GFCC) features has been widely used to develop robust speaker recognition systems with the conventional machine learning, it achieved better performance compared to Mel Frequency Cepstral Coefficient (MFCC) features in the noisy condition. Recently, deep learning models showed better performance in the speaker recognition compared to conventional machine learning. Most of the previous deep learning-based speaker recognition models has used Mel Spectrogram and similar inputs rather than a handcrafted features like MFCC and GFCC features. However, the performance of the Mel Spectrogram features gets degraded in the high noise ratio and mismatch in the utterances. Similar to Mel Spectrogram, Cochleogram is another important feature for deep learning speaker recognition models. Like GFCC features, Cochleogram represents utterances in Equal Rectangular Band (ERB) scale which is important in noisy condition. However, none of the studies have conducted analysis for noise robustness of Cochleogram and Mel Spectrogram in speaker recognition. In addition, only limited studies have used Cochleogram to develop speech-based models in noisy and mismatch condition using deep learning. In this study, analysis of noise robustness of Cochleogram and Mel Spectrogram features in speaker recognition using deep learning model is conducted at the Signal to Noise Ratio (SNR) level from −5 dB to 20 dB. Experiments are conducted on the VoxCeleb1 and Noise added VoxCeleb1 dataset by using basic 2DCNN, ResNet-50, VGG-16, ECAPA-TDNN and TitaNet Models architectures. The Speaker identification and verification performance of both Cochleogram and Mel Spectrogram is evaluated. The results show that Cochleogram have better performance than Mel Spectrogram in both speaker identification and verification at the noisy and mismatch condition.

APA, Harvard, Vancouver, ISO, and other styles

41

Kasim, Rizanaliah, Abdul Rahim Abdullah, Nur Asmiza Selamat, N. A. Abidullah, and Tengku Nor Shuhadah Tengku Zawawi. "Lead Acid Battery Analysis Using Spectogram ." Applied Mechanics and Materials 785 (August 2015): 692–96. http://dx.doi.org/10.4028/www.scientific.net/amm.785.692.

Full text

Abstract:

Renewable energy is an alternative option that can be substituted for future energy demand. Many type of battery are used in commerce to propel portable power and this makes the task of selecting the right battery type is crucial. This paper presents the analysis of voltage charging and discharging for lead acid battery using time-frequency distribution (TFD) which is spectrogram. Spectogram technique is used to represent the signals in the time-frequency representation (TFR). The parameter of a signal such as instantaneous root mean square (RMS) voltage, direct current voltage (VDC) and alternating current voltage (VAC) are estimated from the TFR to identify the signal characteristics. This analysis, focus on lead-acid battery with nominal battery voltage of 6 and 12V and storage capacity from 5 until 50Ah. The battery is a model using MATLAB/SIMULINK and the results show that spectrogram technique is capable to identify and determine the signal characteristic of Lead Acid battery.

APA, Harvard, Vancouver, ISO, and other styles

42

Byeon, Yeong-Hyeon, and Keun-Chang Kwak. "Pre-Configured Deep Convolutional Neural Networks with Various Time-Frequency Representations for Biometrics from ECG Signals." Applied Sciences 9, no. 22 (November 10, 2019): 4810. http://dx.doi.org/10.3390/app9224810.

Full text

Abstract:

We evaluated electrocardiogram (ECG) biometrics using pre-configured models of convolutional neural networks (CNNs) with various time-frequency representations. Biometrics technology records a person’s physical or behavioral characteristics in a digital signal via a sensor and analyzes it to identify the person. An ECG signal is obtained by detecting and amplifying a minute electrical signal flowing on the skin using a noninvasive electrode when the heart muscle depolarizes at each heartbeat. In biometrics, the ECG is especially advantageous in security applications because the heart is located within the body and moves while the subject is alive. However, a few body states generate noisy biometrics. The analysis of signals in the frequency domain has a robust effect on the noise. As the ECG is noise-sensitive, various studies have applied time-frequency transformations that are robust to noise, with CNNs achieving a good performance in image classification. Studies have applied time-frequency representations of the 1D ECG signals to 2D CNNs using transforms like MFCC (mel frequency cepstrum coefficient), spectrogram, log spectrogram, mel spectrogram, and scalogram. CNNs have various pre-configured models such as VGGNet, GoogLeNet, ResNet, and DenseNet. Combinations of the time-frequency representations and pre-configured CNN models have not been investigated. In this study, we employed the PTB (Physikalisch-Technische Bundesanstalt)-ECG and CU (Chosun University)-ECG databases. The MFCC accuracies were 0.45%, 2.60%, 3.90%, and 0.25% higher than the spectrogram, log spectrogram, mel spectrogram, and scalogram accuracies, respectively. The Xception accuracies were 3.91%, 0.84%, and 1.14% higher than the VGGNet-19, ResNet-101, and DenseNet-201 accuracies, respectively.

APA, Harvard, Vancouver, ISO, and other styles

43

Babusiak, Branko, Marian Hostovecky, Maros Smondrk, and Ladislav Huraj. "Spectral Analysis of Electroencephalographic Data in Serious Games." Applied Sciences 11, no. 6 (March 10, 2021): 2480. http://dx.doi.org/10.3390/app11062480.

Full text

Abstract:

In this paper, we describe an investigation of brain activity while playing a serious game (SG). A SG is focused on improving logical thinking, specifically on cognitive training of students in the field of basic logic gates, and we summarize SG description, design, and development. A method based on various signal processing techniques for evaluating electroencephalographic (EEG) data was implemented in the MATLAB. This assessment was based on the analysis of the spectrogram of particular brain activity. Changes in brain activity power at a characteristic frequency band during the gameplay were calculated from the spectrogram. The EEG of 21 respondents was measured. Based on the results, the respondents can be divided into three groups according to specific EEG activity changes during the gameplay compared to a relaxed state. The beta/alpha ratio, an indicator of brain employment to a mental task, was increased during gameplay in 18 of the 21 subjects. Our results reflected the sex of respondents, time of the game and the indicator, and whether the game was successfully completed.

APA, Harvard, Vancouver, ISO, and other styles

44

DEBBAL, S. M., and F. BEREKSI-REGUIG. "SECOND CARDIAC SOUND ANALYSIS TECHNIQUES AND PERFORMANCE COMPARISON." Journal of Mechanics in Medicine and Biology 05, no. 03 (September 2005): 429–42. http://dx.doi.org/10.1142/s021951940500162x.

Full text

Abstract:

This paper presents the applications of the spectrogram, Wigner distribution and wavelet transform analysis methods to the second cardiac sound S2 of the phonocardiogram signal (PCG). A comparison between these methods has shown the resolution differences between them. It is found that the spectrogram Short-Time Fourier Transform (STFT) cannot detect the two internals components of the second sound S2 (A2 and P2, atrial and pulmonary components respectively). The Wigner Distribution (WD) can provide time-frequency characteristics of the sound S2, but with insufficient diagnostic information as the two components (A2 and P2) are not accurately detected, appearing to be one component only. It is found that the wavelet transform (WT) is capable of detecting the two components, the aortic valve component A2 and pulmonary valve component P2, of the second cardiac sound S2. However, the standard Fourier transform can display these components in frequency but not the time delay between them. Furthermore, the wavelet transform provides more features and characteristics of the second sound S2 that will hemp physicians to obtain qualitative and quantitative measurements of the time-frequency characteristics.

APA, Harvard, Vancouver, ISO, and other styles

45

GUO, Jiantao, and Lin WANG. "Spectrogram analysis of frequency-hopping signals based on entropy measure." Journal of Computer Applications 33, no. 5 (October 18, 2013): 1230–32. http://dx.doi.org/10.3724/sp.j.1087.2013.01230.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Kim, Bong-Hyun, and Dong-Uk Cho. "Variation Analysis of Spectrogram for Indicators Design of Musicality Evaluation." Journal of the Korea Academia-Industrial cooperation Society 10, no. 8 (August 31, 2009): 2110–16. http://dx.doi.org/10.5762/kais.2009.10.8.2110.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Ghosh, Subhroshekhar, Meixia Lin, and Dongfang Sun. "Signal Analysis via the Stochastic Geometry of Spectrogram Level Sets." IEEE Transactions on Signal Processing 70 (2022): 1104–17. http://dx.doi.org/10.1109/tsp.2022.3153596.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Du, L., J. Li, P. Stoica, H. Ling, and S. S. Ram. "Doppler spectrogram analysis of human gait via iterative adaptive approach." Electronics Letters 45, no. 3 (2009): 186. http://dx.doi.org/10.1049/el:20092769.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Runefors, P., E. Arnbjörnsson, G. Elander, and K. Michelsson. "Newborn infants' cry after heel-prick: analysis with sound spectrogram." Acta Paediatrica 89, no. 1 (January 2, 2007): 68–72. http://dx.doi.org/10.1111/j.1651-2227.2000.tb01190.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Kim, Jeakwan, Yunseon Choi, and Young-Sup Lee. "Spectrogram Image Analysis of Error Signals for Minimizing Impulse Noise." Journal of Sensors 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/4935694.

Full text

Abstract:

This paper presents the theoretical and experimental study on the spectrogram image analysis of error signals for minimizing the impulse input noises in the active suppression of noise. Impulse inputs of some specific wave patterns as primary noises to a one-dimensional duct with the length of 1800 mm are shown. The convergence speed of the adaptive feedforward algorithm based on the least mean square approach was controlled by a normalized step size which was incorporated into the algorithm. The variations of the step size govern the stability as well as the convergence speed. Because of this reason, a normalized step size is introduced as a new method for the control of impulse noise. The spectrogram images which indicate the degree of the attenuation of the impulse input noises are considered to represent the attenuation with the new method. The algorithm is extensively investigated in both simulation and real-time control experiment. It is demonstrated that the suggested algorithm worked with a nice stability and performance against impulse noises. The results in this study can be used for practical active noise control systems.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Spectrogram analysis'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles