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Journal articles on the topic 'Mel-Frequency Cepstral coefficients'

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Author: Grafiati

Published: 4 June 2021

Last updated: 31 May 2022

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1

Sato, Nobuo, and Yasunari Obuchi. "Emotion Recognition using Mel-Frequency Cepstral Coefficients." Journal of Natural Language Processing 14, no. 4 (2007): 83–96. http://dx.doi.org/10.5715/jnlp.14.4_83.

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2

Hashad, F. G., T. M. Halim, S. M. Diab, B. M. Sallam, and F. E. Abd El-Samie. "Fingerprint recognition using mel-frequency cepstral coefficients." Pattern Recognition and Image Analysis 20, no. 3 (September 2010): 360–69. http://dx.doi.org/10.1134/s1054661810030120.

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3

Park, Won Gyeong, Young Bae Lim, Dong Woo Kim, Ho Kyoung Lee, and Sdeongwon Cho. "Prediction Method of Electrical Abnormal States Using Simplified Mel-Frequency Cepstral Coefficients." Journal of Korean Institute of Intelligent Systems 28, no. 5 (October 31, 2018): 514–22. http://dx.doi.org/10.5391/jkiis.2018.28.5.514.

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4

INDRAWATY, YOULLIA, IRMA AMELIA DEWI, and RIZKI LUKMAN. "Ekstraksi Ciri Pelafalan Huruf Hijaiyyah Dengan Metode Mel-Frequency Cepstral Coefficients." MIND Journal 4, no. 1 (June 1, 2019): 49–64. http://dx.doi.org/10.26760/mindjournal.v4i1.49-64.

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Huruf hijaiyyah merupakan huruf penyusun ayat dalam Al Qur’an. Setiap hurufhijaiyyah memiliki karakteristik pelafalan yang berbeda. Tetapi dalam praktiknya,ketika membaca huruf hijaiyyah terkadang tidak memperhatikan kaidah bacaanmakhorijul huruf. Makhrorijul huruf adalah cara melafalkan atau tempatkeluarnya huruf hijaiyyah. Dengan adanya teknologi pengenalan suara, dalammelafalkan huruf hijaiyyah dapat dilihat perbedaannya secara kuantitatif melaluisistem. Terdapat dua tahapan agar suara dapat dikenali, dengan terlebih dahulumelakukan ekstraksi sinyal suara selanjutnya melakukan identifikasi suara ataubacaan. MFCC (Mel Frequency Cepstral Coefficients) merupakan sebuah metodeuntuk melakukan ektraksi ciri yang menghasilkan nilai cepstral dari sinyal suara.Penelitian ini bertujuan untuk mengetahui nilai cepstral pada setiap hurufhijaiyyah. Hasil pengujian yang telah dilakukan, setiap huruf hijaiyyah memilikinilai cepstral yang berbeda.

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5

ARORA, SHRUTI, SUSHMA JAIN, and INDERVEER CHANA. "A FUSION FRAMEWORK BASED ON CEPSTRAL DOMAIN FEATURES FROM PHONOCARDIOGRAM TO PREDICT HEART HEALTH STATUS." Journal of Mechanics in Medicine and Biology 21, no. 04 (April 22, 2021): 2150034. http://dx.doi.org/10.1142/s0219519421500342.

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A great increase in the number of cardiovascular cases has been a cause of serious concern for the medical experts all over the world today. In order to achieve valuable risk stratification for patients, early prediction of heart health can benefit specialists to make effective decisions. Heart sound signals help to know about the condition of heart of a patient. Motivated by the success of cepstral features in speech signal classification, authors have used here three different cepstral features, viz. Mel-frequency cepstral coefficients (MFCCs), gammatone frequency cepstral coefficients (GFCCs), and Mel-spectrogram for classifying phonocardiogram into normal and abnormal. Existing research has explored only MFCCs and Mel-feature set extensively for classifying the phonocardiogram. However, in this work, the authors have used a fusion of GFCCs with MFCCs and Mel-spectrogram, and achieved a better accuracy score of 0.96 with sensitivity and specificity scores as 0.91 and 0.98, respectively. The proposed model has been validated on the publicly available benchmark dataset PhysioNet 2016.

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6

Sheu, Jia-Shing, and Ching-Wen Chen. "Voice Recognition and Marking Using Mel-frequency Cepstral Coefficients." Sensors and Materials 32, no. 10 (October 9, 2020): 3209. http://dx.doi.org/10.18494/sam.2020.2860.

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7

Koolagudi, Shashidhar G., Deepika Rastogi, and K. Sreenivasa Rao. "Identification of Language using Mel-Frequency Cepstral Coefficients (MFCC)." Procedia Engineering 38 (2012): 3391–98. http://dx.doi.org/10.1016/j.proeng.2012.06.392.

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8

Saldanha, Jennifer C., T. Ananthakrishna, and Rohan Pinto. "Vocal Fold Pathology Assessment Using Mel-Frequency Cepstral Coefficients and Linear Predictive Cepstral Coefficients Features." Journal of Medical Imaging and Health Informatics 4, no. 2 (April 1, 2014): 168–73. http://dx.doi.org/10.1166/jmihi.2014.1253.

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9

Pedalanka, P. S. Subhashini, M. SatyaSai Ram, and Duggirala Sreenivasa Rao. "Mel Frequency Cepstral Coefficients based Bacterial Foraging Optimization with DNN-RBF for Speaker Recognition." Indian Journal of Science and Technology 14, no. 41 (November 3, 2021): 3082–92. http://dx.doi.org/10.17485/ijst/v14i41.1858.

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10

Fahmy, Maged M. M. "Palmprint recognition based on Mel frequency Cepstral coefficients feature extraction." Ain Shams Engineering Journal 1, no. 1 (September 2010): 39–47. http://dx.doi.org/10.1016/j.asej.2010.09.005.

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11

Rasyid, Muhammad Fahim, Herlina Jayadianti, and Herry Sofyan. "APLIKASI PENGENALAN PENUTUR PADA IDENTIFIKASI SUARA PENELEPON MENGGUNAKAN MEL-FREQUENCY CEPSTRAL COEFFICIENT DAN VECTOR QUANTIZATION (Studi Kasus : Layanan Hotline Universitas Pembangunan Nasional “Veteran” Yogyakarta)." Telematika 17, no. 2 (October 31, 2020): 68. http://dx.doi.org/10.31315/telematika.v1i1.3380.

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Layanan hotline Universitas Pembangunan Nasional “Veteran” Yogyakarta merupakan layanan yang dapat digunakan oleh semua orang. Layanan tersebut digunakan dosen dan pegawai untuk berbagi informasi dengan bagian-bagian yang berlokasi di gedung rektorat. Penelepon dapat berkomunikasi dengan bagian yang dituju apabila telah teridentifikasi oleh petugas layanan hotline. Terminologi identitas yang terdiri dari nama, jabatan serta asal jurusan atau bagian ditanyakan saat proses identifikasi. Tidak terdapat catatan hasil identifikasi penelepon baik dalam bentuk fisik maupun basis data yang terekam pada komputer. Hal tersebut mengakibatkan tidak adanya dokumentasi yang dapat dijadikan barang bukti untuk menindak lanjuti kasus kesalahan identifikasi. Penelitian ini fokus untuk mengurangi resiko kesalahan identifikasi penelepon menggunakan teknologi speaker recognition. Frekuensi suara diekstraksi menggunakan metode Mel-Frequency Cepstral Coefficient (MFCC) sehingga dihasilkan nilai Mel Frequency Cepstrum Coefficients. Nilai Mel Frequency Cepstrum Coefficients dari semua data latih suara pegawai Universitas Pembangunan Nasional “Veteran” Yogyakarta kemudian dibandingkan dengan sinyal suara penelpon menggunakan metode Vector Quantization (VQ). Aplikasi pengenalan penutur mampu mengidentifikasi suara penelepon dengan tingkat akurasi 80% pada nilai ambang (threshold) 25.

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12

Ramashini, Murugaiya, P. Emeroylariffion Abas, Kusuma Mohanchandra, and Liyanage C. De Silva. "Robust cepstral feature for bird sound classification." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (April 1, 2022): 1477. http://dx.doi.org/10.11591/ijece.v12i2.pp1477-1487.

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Birds are excellent environmental indicators and may indicate sustainability of the ecosystem; birds may be used to provide provisioning, regulating, and supporting services. Therefore, birdlife conservation-related researches always receive centre stage. Due to the airborne nature of birds and the dense nature of the tropical forest, bird identifications through audio may be a better solution than visual identification. The goal of this study is to find the most appropriate cepstral features that can be used to classify bird sounds more accurately. Fifteen (15) endemic Bornean bird sounds have been selected and segmented using an automated energy-based algorithm. Three (3) types of cepstral features are extracted; linear prediction cepstrum coefficients (LPCC), mel frequency cepstral coefficients (MFCC), gammatone frequency cepstral coefficients (GTCC), and used separately for classification purposes using support vector machine (SVM). Through comparison between their prediction results, it has been demonstrated that model utilising GTCC features, with 93.3% accuracy, outperforms models utilising MFCC and LPCC features. This demonstrates the robustness of GTCC for bird sounds classification. The result is significant for the advancement of bird sound classification research, which has been shown to have many applications such as in eco-tourism and wildlife management.

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13

Mahalakshmi, P. "A REVIEW ON VOICE ACTIVITY DETECTION AND MEL-FREQUENCY CEPSTRAL COEFFICIENTS FOR SPEAKER RECOGNITION (TREND ANALYSIS)." Asian Journal of Pharmaceutical and Clinical Research 9, no. 9 (December 1, 2016): 360. http://dx.doi.org/10.22159/ajpcr.2016.v9s3.14352.

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ABSTRACTObjective: The objective of this review article is to give a complete review of various techniques that are used for speech recognition purposes overtwo decades.Methods: VAD-Voice Activity Detection, SAD-Speech Activity Detection techniques are discussed that are used to distinguish voiced from unvoicedsignals and MFCC- Mel Frequency Cepstral Coefficient technique is discussed which detects specific features.Results: The review results show that research in MFCC has been dominant in signal processing in comparison to VAD and other existing techniques.Conclusion: A comparison of different speaker recognition techniques that were used previously were discussed and those in current research werealso discussed and a clear idea of the better technique was identified through the review of multiple literature for over two decades.Keywords: Cepstral analysis, Mel-frequency cepstral coefficients, signal processing, speaker recognition, voice activity detection.

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14

Shao, Xu, and Ben Milner. "Predicting fundamental frequency from mel-frequency cepstral coefficients to enable speech reconstruction." Journal of the Acoustical Society of America 118, no. 2 (August 2005): 1134–43. http://dx.doi.org/10.1121/1.1953269.

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15

Nasr, Marwa A., Mohammed Abd-Elnaby, Adel S. El-Fishawy, S. El-Rabaie, and Fathi E. Abd El-Samie. "Speaker identification based on normalized pitch frequency and Mel Frequency Cepstral Coefficients." International Journal of Speech Technology 21, no. 4 (September 17, 2018): 941–51. http://dx.doi.org/10.1007/s10772-018-9524-7.

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16

Лавриненко, Александр Юрьевич, Юрий Анатольевич Кочергин, and Георгий Филимонович Конахович. "СИСТЕМА РАСПОЗНАВАНИЯ СТЕГАНОГРАФИЧЕСКИ-ПРЕОБРАЗОВАННЫХ ГОЛОСОВЫХ КОМАНД УПРАВЛЕНИЯ БПЛА." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 3 (October 30, 2018): 20–28. http://dx.doi.org/10.32620/reks.2018.3.03.

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It is created the system of recognition the steganographic-transformed voice commands of unmanned aerial vehicle control based on a cepstral analysis. It provides effective recognition and hidden commands transmission of to the board of an unmanned aerial vehicle, by converting voice control commands into a kind of steganographic characteristics vector, which implies the concealment of voice control information of an unmanned aerial vehicle. The mathematical model of the algorithm for calculating the mel-frequency cepstral coefficients and the recognition classifier of voice control commands for the solution of the problem of semantic identification and securing the control information of the unmanned aerial vehicle in the communication channel is synthesized. A software package has been developed that includes tools for compiling the base of reference voice images of subjects of management for training and testing the system for recognizing steganographic-transformed voice commands of the unmanned aerial vehicle control based on the cepstral analysis and computer models of the proposed methods and algorithms for recognition voice control commands in the MATLAB environment. The expediency of applying the proposed system for recognizing steganographic-transformed voice commands of the unmanned aerial vehicle control based on a cepstral analysis is substantiated and experimentally proved. An algorithm is presented for calculating the mel-frequency cepstral coefficients that appear in the role of the main features of recognition and the result of the steganographic transformation of speech, where for the evaluation of automatic recognition of voice commands using the results of classifier constructed by the criterion of minimum distance in the role which acts as the variance of the difference of the expectation of a mel-frequency cepstral coefficients. The obtained results of the experimental research allow to draw a conclusion about the expediency of further practical application of the developed system of recognition the steganographic-transformed voice commands of the unmanned aerial vehicle control based on the cepstral analysis

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17

Kapoor, Tripti, and R. K. Sharma. "Parkinsons disease Diagnosis using Mel frequency Cepstral Coefficients and Vector Quantization." International Journal of Computer Applications 14, no. 3 (January 12, 2011): 43–46. http://dx.doi.org/10.5120/1821-2393.

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18

Wulandari Siagian, Thasya Nurul, Hilal Hudan Nuha, and Rahmat Yasirandi. "Footstep Recognition Using Mel Frequency Cepstral Coefficients and Artificial Neural Network." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 3 (June 20, 2020): 497–503. http://dx.doi.org/10.29207/resti.v4i3.1964.

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Footstep recognition is relatively new biometrics and based on the learning of footsteps signals captured from people walking on the sensing area. The footstep signals classification process for security systems still has a low level of accuracy. Therefore, we need a classification system that has a high accuracy for security systems. Most systems are generally developed using geometric and holistic features but still provide high error rates. In this research, a new system is proposed by using the Mel Frequency Cepstral Coefficients (MFCCs) feature extraction, because it has a good linear frequency as a copycat of the human hearing system and Artificial Neural Network (ANN) as a classification algorithm because it has a good level of accuracy with a dataset of 500 recording footsteps. The classification results show that the proposed system can achieve the highest accuracy of validation loss value 57.3, Accuracy testing 92.0%, loss value 193.8, and accuracy training 100%, the accuracy results are an evaluation of the system in improving the foot signal recognition system for security systems in the smart home environment.

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19

., Rachna. "FEATURE EXTRACTION FROM ASTHMA PATIENT’S VOICE USING MEL-FREQUENCY CEPSTRAL COEFFICIENTS." International Journal of Research in Engineering and Technology 03, no. 06 (June 25, 2014): 273–76. http://dx.doi.org/10.15623/ijret.2014.0306050.

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20

Liu, Delian, Xiaorui Wang, Jianqi Zhang, and Xi Huang. "Feature extraction using Mel frequency cepstral coefficients for hyperspectral image classification." Applied Optics 49, no. 14 (May 6, 2010): 2670. http://dx.doi.org/10.1364/ao.49.002670.

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21

K, Sureshkumar, and Thatchinamoorthy P. "Speech and Spectral Landscapes using Mel-Frequency Cepstral Coefficients Signal Processing." International Journal of VLSI & Signal Processing 3, no. 1 (April 25, 2016): 5–8. http://dx.doi.org/10.14445/23942584/ijvsp-v3i1p102.

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22

Milner, Ben, and Jonathan Darch. "Robust Acoustic Speech Feature Prediction From Noisy Mel-Frequency Cepstral Coefficients." IEEE Transactions on Audio, Speech, and Language Processing 19, no. 2 (February 2011): 338–47. http://dx.doi.org/10.1109/tasl.2010.2047811.

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23

Astuti, Dwi. "Aplikasi Identifikasi Suara Hewan Menggunakan Metode Mel-Frequency Cepstral Coefficients (MFCC)." Journal of Informatics, Information System, Software Engineering and Applications (INISTA) 1, no. 2 (May 30, 2019): 26–34. http://dx.doi.org/10.20895/inista.v1i2.50.

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Pengenalan suara berada dibawah bidang komputasi linguistik. Hal ini mencakup identifikasi, pengakuan, dan terjemahan ucapan yang terdeteksi ke dalam teks oleh komputer. Penelitian ini menggunakan handphone dan sistem yang dirancang menggunakan suara. Tujuan utama dari penelitian ini adalah menggunakan teknik pengenalan suara untuk mendeteksi, mengidentifikasi dan menerjemahkan suara binatang. Sistem ini terdiri dari dua tahap yaitu pelatihan dan pengujian. Pelatihan melibatkan pengajaran sistem dengan membangun kamus, model akustik untuk setiap kata yang perlu dikenali oleh sistem (analisis offline). Tahap pengujian menggunakan model akustik untuk mengenali kata-kata terisolasi menggunakan algoritma klasifikasi. Aplikasi penyimpanan audio untuk mengidentifikasi berbagai suara binatang dapat dilakukan dengan lebih akurat dimasa depan.

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24

Eskidere, Ömer, and Ahmet Gürhanlı. "Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients Features." Computational and Mathematical Methods in Medicine 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/956249.

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The Mel Frequency Cepstral Coefficients (MFCCs) are widely used in order to extract essential information from a voice signal and became a popular feature extractor used in audio processing. However, MFCC features are usually calculated from a single window (taper) characterized by large variance. This study shows investigations on reducing variance for the classification of two different voice qualities (normal voice and disordered voice) using multitaper MFCC features. We also compare their performance by newly proposed windowing techniques and conventional single-taper technique. The results demonstrate that adapted weighted Thomson multitaper method could distinguish between normal voice and disordered voice better than the results done by the conventional single-taper (Hamming window) technique and two newly proposed windowing methods. The multitaper MFCC features may be helpful in identifying voices at risk for a real pathology that has to be proven later.

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25

Ayvaz, Uğur, Hüseyin Gürüler, Faheem Khan, Naveed Ahmed, Taegkeun Whangbo, and Abdusalomov Akmalbek Bobomirzaevich. "Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning." Computers, Materials & Continua 71, no. 3 (2022): 5511–21. http://dx.doi.org/10.32604/cmc.2022.023278.

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26

Deng, Lei, and Yong Gao. "Gammachirp Filter Banks Applied in Roust Speaker Recognition Based GMM-UBM Classifier." International Arab Journal of Information Technology 17, no. 2 (February 28, 2019): 170–77. http://dx.doi.org/10.34028/iajit/17/2/4.

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In this paper, authors propose an auditory feature extraction algorithm in order to improve the performance of the speaker recognition system in noisy environments. In this auditory feature extraction algorithm, the Gammachirp filter bank is adapted to simulate the auditory model of human cochlea. In addition, the following three techniques are applied: cube-root compression method, Relative Spectral Filtering Technique (RASTA), and Cepstral Mean and Variance Normalization algorithm (CMVN).Subsequently, based on the theory of Gaussian Mixes Model-Universal Background Model (GMM-UBM), the simulated experiment was conducted. The experimental results implied that speaker recognition systems with the new auditory feature has better robustness and recognition performance compared to Mel-Frequency Cepstral Coefficients(MFCC), Relative Spectral-Perceptual Linear Predictive (RASTA-PLP),Cochlear Filter Cepstral Coefficients (CFCC) and gammatone Frequency Cepstral Coefficeints (GFCC)

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Noda, Juan J., Carlos M. Travieso-González, David Sánchez-Rodríguez, and Jesús B. Alonso-Hernández. "Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion." Applied Sciences 9, no. 19 (October 1, 2019): 4097. http://dx.doi.org/10.3390/app9194097.

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This work introduces a new approach for automatic identification of crickets, katydids and cicadas analyzing their acoustic signals. We propose the building of a tool to identify this biodiversity. The study proposes a sound parameterization technique designed specifically for identification and classification of acoustic signals of insects using Mel Frequency Cepstral Coefficients (MFCC) and Linear Frequency Cepstral Coefficients (LFCC). These two sets of coefficients are evaluated individually as has been done in previous studies and have been compared with the fusion proposed in this work, showing an outstanding increase in identification and classification at species level reaching a success rate of 98.07% on 343 insect species.

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Li, Guan Yu, Hong Zhi Yu, Yong Hong Li, and Ning Ma. "Features Extraction for Lhasa Tibetan Speech Recognition." Applied Mechanics and Materials 571-572 (June 2014): 205–8. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.205.

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Speech feature extraction is discussed. Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction coefficient (PLP) method is analyzed. These two types of features are extracted in Lhasa large vocabulary continuous speech recognition system. Then the recognition results are compared.

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H.Mansour, Abdelmajid, Gafar Zen Alabdeen Salh, and Khalid A. Mohammed. "Voice Recognition using Dynamic Time Warping and Mel-Frequency Cepstral Coefficients Algorithms." International Journal of Computer Applications 116, no. 2 (April 22, 2015): 34–41. http://dx.doi.org/10.5120/20312-2362.

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Almanfaluti, Istian Kriya, and Judi Prajetno Sugiono. "Identifikasi Pola Suara Pada Bahasa Jawa Meggunakan Mel Frequency Cepstral Coefficients (MFCC)." JURNAL MEDIA INFORMATIKA BUDIDARMA 4, no. 1 (January 29, 2020): 22. http://dx.doi.org/10.30865/mib.v4i1.1793.

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Voice Recognition is a process of developing systems used between computer and human. The purpose of this study is to find out the sound pattern of a person based on the spoken Javanese language. This study used the Mel Frequency Cepstral Coefficients (MFCC) method to solve the problem of feature extraction from human voices. Tests were carried out on 4 users consisting of 2 women and 2 men, each saying 1 word "KUTHO", the word pronounced 5 times. The results of the testing are to get a sound pattern from the characteristics of 1 person with another person so that research using the MFCC method can produce different sound patterns

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Prajapati, Pooja, and Miral Patel. "Feature Extraction of Isolated Gujarati Digits with Mel Frequency Cepstral Coefficients (MFCCs)." International Journal of Computer Applications 163, no. 6 (April 17, 2017): 29–33. http://dx.doi.org/10.5120/ijca2017913551.

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32

Maliki, I., and Sofiyanudin. "Musical Instrument Recognition using Mel-Frequency Cepstral Coefficients and Learning Vector Quantization." IOP Conference Series: Materials Science and Engineering 407 (September 26, 2018): 012118. http://dx.doi.org/10.1088/1757-899x/407/1/012118.

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Mak, B. "A mathematical relationship between full-band and multiband mel-frequency cepstral coefficients." IEEE Signal Processing Letters 9, no. 8 (August 2002): 241–44. http://dx.doi.org/10.1109/lsp.2002.803007.

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34

Patil, Adwait. "Covid Classification Using Audio Data." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 1633–37. http://dx.doi.org/10.22214/ijraset.2021.38675.

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Abstract: Coronavirus outbreak has affected the entire world adversely this project has been developed in order to help common masses diagnose their chances of been covid positive just by using coughing sound and basic patient data. Audio classification is one of the most interesting applications of deep learning. Similar to image data audio data is also stored in form of bits and to understand and analyze this audio data we have used Mel frequency cepstral coefficients (MFCCs) which makes it possible to feed the audio to our neural network. In this project we have used Coughvid a crowdsource dataset consisting of 27000 audio files and metadata of same amount of patients. In this project we have used a 1D Convolutional Neural Network (CNN) to process the audio and metadata. Future scope for this project will be a model that rates how likely it is that a person is infected instead of binary classification. Keywords: Audio classification, Mel frequency cepstral coefficients, Convolutional neural network, deep learning, Coughvid

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35

Singh, Moirangthem Tiken. "Automatic Speech Recognition System: A Survey Report." Science & Technology Journal 4, no. 2 (July 1, 2016): 152–55. http://dx.doi.org/10.22232/stj.2016.04.02.10.

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This paper presents a report on an Automatic Speech Recognition System (ASR) for different Indian language under different accent. The paper is a comparative study of the performance of system developed which uses Hidden Markov Model (HMM) as the classifier and Mel-Frequency Cepstral Coefficients (MFCC) as speech features.

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H. Mohd Johari, N., Noreha Abdul Malik, and K. A. Sidek. "Distinctive features for normal and crackles respiratory sounds using cepstral coefficients." Bulletin of Electrical Engineering and Informatics 8, no. 3 (September 1, 2019): 875–81. http://dx.doi.org/10.11591/eei.v8i3.1517.

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Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detected and therefore diagnostic test can be performed earlier. In this paper, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are used to extract features from normal and crackles respiratory sounds. By using statistical computation such as mean and standard deviation (SD) of cepstral based coefficients it can differentiate between crackles and normal sounds. The statistical computations of the cepstral coefficient of LPCC and MFCC show that the mean LPCC except for the third coefficient and first three statistical coefficient values of MFCC’s SD provide distinctive feature between normal and crackles respiratory sounds. Hence, LPCCs and MFCCs can be used as feature extraction method of respiratory sounds to classify between normal and crackles as screening and diagnostic tool.

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Thakur, Surendra, Emmanuel Adetiba, Oludayo O. Olugbara, and Richard Millham. "Experimentation Using Short-Term Spectral Features for Secure Mobile Internet Voting Authentication." Mathematical Problems in Engineering 2015 (2015): 1–21. http://dx.doi.org/10.1155/2015/564904.

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We propose a secure mobile Internet voting architecture based on the Sensus reference architecture and report the experiments carried out using short-term spectral features for realizing the voice biometric based authentication module of the architecture being proposed. The short-term spectral features investigated are Mel-Frequency Cepstral Coefficients (MFCCs), Mel-Frequency Discrete Wavelet Coefficients (MFDWC), Linear Predictive Cepstral Coefficients (LPCC), and Spectral Histogram of Oriented Gradients (SHOGs). The MFCC, MFDWC, and LPCC usually have higher dimensions that oftentimes lead to high computational complexity of the pattern matching algorithms in automatic speaker recognition systems. In this study, higher dimensions of each of the short-term features were reduced to an 81-element feature vector per Speaker using Histogram of Oriented Gradients (HOG) algorithm while neural network ensemble was utilized as the pattern matching algorithm. Out of the four short-term spectral features investigated, the LPCC-HOG gave the best statistical results withRstatistic of 0.9127 and mean square error of 0.0407. These compact LPCC-HOG features are highly promising for implementing the authentication module of the secure mobile Internet voting architecture we are proposing in this paper.

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Naveena, V., Susmitha Vekkot, and K. Jeeva Priya. "Voice Conversion System Based on Deep Neural Networks." Journal of Computational and Theoretical Nanoscience 17, no. 1 (January 1, 2020): 316–21. http://dx.doi.org/10.1166/jctn.2020.8668.

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The paper focuses on usage of deep neural networks for converting a person’s voice to another person’s voice, analogous to a mimic. The work in this paper introduces the concept of neural networks and deploys multi-layer deep neural networks for building a framework for voice conversion. The spectral Mel-Frequency Cepstral Coefficients (MFCCs) are converted using a 10-layer deep network while fundamental frequency (F0) conversion is accomplished by logarithmic Gaussian normalized transformation. MFCCs are subjected to inverse cepstral filtering while changes in F0 are incorporated using Pitch Synchronous OverLap Add (PSOLA) algorithm for re-synthesis. The results obtained are compared using Mel Cepstral Distortion (MCD) for objective evaluation while ABX-listening test is conducted for subjective assessment. Maximum improvement in MCD of 13.87% is obtained for female-to-male conversion while ABX-listening test indicates that female-to-male is closest to target with an agreement of 76.2%. The method achieves reasonably good performance compared to state-of-the-art using optimal resources and avoids requirement of highly complex computations.

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Alasadi, A. A., T. H. Aldhayni, R. R. Deshmukh, A. H. Alahmadi, and A. S. Alshebami. "Efficient Feature Extraction Algorithms to Develop an Arabic Speech Recognition System." Engineering, Technology & Applied Science Research 10, no. 2 (April 4, 2020): 5547–53. http://dx.doi.org/10.48084/etasr.3465.

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This paper studies three feature extraction methods, Mel-Frequency Cepstral Coefficients (MFCC), Power-Normalized Cepstral Coefficients (PNCC), and Modified Group Delay Function (ModGDF) for the development of an Automated Speech Recognition System (ASR) in Arabic. The Support Vector Machine (SVM) algorithm processed the obtained features. These feature extraction algorithms extract speech or voice characteristics and process the group delay functionality calculated straight from the voice signal. These algorithms were deployed to extract audio forms from Arabic speakers. PNCC provided the best recognition results in Arabic speech in comparison with the other methods. Simulation results showed that PNCC and ModGDF were more accurate than MFCC in Arabic speech recognition.

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Mięsikowska, Marzena. "Discriminant Analysis of Voice Commands in the Presence of an Unmanned Aerial Vehicle." Information 12, no. 1 (January 8, 2021): 23. http://dx.doi.org/10.3390/info12010023.

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The aim of this study was to perform discriminant analysis of voice commands in the presence of an unmanned aerial vehicle equipped with four rotating propellers, as well as to obtain background sound levels and speech intelligibility. The measurements were taken in laboratory conditions in the absence of the unmanned aerial vehicle and the presence of the unmanned aerial vehicle. Discriminant analysis of speech commands (left, right, up, down, forward, backward, start, and stop) was performed based on mel-frequency cepstral coefficients. Ten male speakers took part in this experiment. The unmanned aerial vehicle hovered at a height of 1.8 m during the recordings at a distance of 2 m from the speaker and 0.3 m above the measuring equipment. Discriminant analysis based on mel-frequency cepstral coefficients showed promising classification of speech commands equal to 76.2% for male speakers. Evaluated speech intelligibility during recordings and obtained sound levels in the presence of the unmanned aerial vehicle during recordings did not exclude verbal communication with the unmanned aerial vehicle for male speakers.

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Mięsikowska, Marzena. "Discriminant Analysis of Voice Commands in the Presence of an Unmanned Aerial Vehicle." Information 12, no. 1 (January 8, 2021): 23. http://dx.doi.org/10.3390/info12010023.

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The aim of this study was to perform discriminant analysis of voice commands in the presence of an unmanned aerial vehicle equipped with four rotating propellers, as well as to obtain background sound levels and speech intelligibility. The measurements were taken in laboratory conditions in the absence of the unmanned aerial vehicle and the presence of the unmanned aerial vehicle. Discriminant analysis of speech commands (left, right, up, down, forward, backward, start, and stop) was performed based on mel-frequency cepstral coefficients. Ten male speakers took part in this experiment. The unmanned aerial vehicle hovered at a height of 1.8 m during the recordings at a distance of 2 m from the speaker and 0.3 m above the measuring equipment. Discriminant analysis based on mel-frequency cepstral coefficients showed promising classification of speech commands equal to 76.2% for male speakers. Evaluated speech intelligibility during recordings and obtained sound levels in the presence of the unmanned aerial vehicle during recordings did not exclude verbal communication with the unmanned aerial vehicle for male speakers.

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Milner, Ben, and Xu Shao. "Prediction of Fundamental Frequency and Voicing From Mel-Frequency Cepstral Coefficients for Unconstrained Speech Reconstruction." IEEE Transactions on Audio, Speech and Language Processing 15, no. 1 (January 2007): 24–33. http://dx.doi.org/10.1109/tasl.2006.876880.

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Elsharkawy, Rania, S. El-Rabaie, M. Hindy, R. S. Ghoname, and Moawad Ibrahim Dessouky. "FET SMALL SIGNAL MODELLING BASED ON THE DST AND MEL FREQUENCY CEPSTRAL COEFFICIENTS." Progress In Electromagnetics Research B 18 (2009): 185–204. http://dx.doi.org/10.2528/pierb09082001.

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Gongora, Leonardo, Olga Ramos, and Joao Mauricio. "Comparative Analysis of the Mel Frequency Cepstral Coefficients for Voiced and Silent Speech." International Journal on Communications Antenna and Propagation (IRECAP) 6, no. 5 (October 31, 2016): 255. http://dx.doi.org/10.15866/irecap.v6i5.10271.

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Dadula, Cristina P., and Elmer P. Dadios. "Fuzzy Logic System for Abnormal Audio Event Detection Using Mel Frequency Cepstral Coefficients." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 2 (March 15, 2017): 205–10. http://dx.doi.org/10.20965/jaciii.2017.p0205.

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This paper presents a fuzzy logic system for audio event detection using mel frequency cepstral coefficients (MFCC). Twelve MFCC of audio samples were analyzed. The range of values of MFCC were obtained including its histogram. These values were normalized so that its minimum and maximum values lie between 0 and 1. Rules were formulated based on the histogram to classify audio samples as normal, gunshot, or crowd panic. Five MFCC were chosen as input to the fuzzy logic system. The membership functions and rules of the fuzzy logic system are defined based on the normalized histograms of MFCC. The system was tested with a total of 150 minutes of normal sounds from different buses and 72 seconds audio clips abnormal sounds. The designed fuzzy logic system was able to classify audio events with an average accuracy of 99.4%.

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Hanafi, Dirman, and Abdul Syafiq Abdul Sukor. "Speaker Identification Using K-means Method Based on Mel Frequency Cepstral Coefficients(MFCC)." i-manager's Journal on Embedded Systems 1, no. 1 (April 15, 2012): 19–28. http://dx.doi.org/10.26634/jes.1.1.1729.

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Vergin, R., D. O'Shaughnessy, and A. Farhat. "Generalized mel frequency cepstral coefficients for large-vocabulary speaker-independent continuous-speech recognition." IEEE Transactions on Speech and Audio Processing 7, no. 5 (1999): 525–32. http://dx.doi.org/10.1109/89.784104.

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Okubo, Azumi, and Takehito Kikuchi. "Cough Classification Method with Mel-Frequency Cepstral Coefficients and Gaussian-type Evaluation Function." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2021 (2021): 1A1—C02. http://dx.doi.org/10.1299/jsmermd.2021.1a1-c02.

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IsmailaAdeniyi, Kamil, and Oyeyiola Abdulhamid K. "Comparative Study on the Performance of Mel-Frequency Cepstral Coefficients and Linear Prediction Cepstral Coefficients under different Speaker's Conditions." International Journal of Computer Applications 90, no. 11 (March 26, 2014): 38–42. http://dx.doi.org/10.5120/15767-4460.

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Mengistu, Abrham Debasu, and Dagnachew Melesew Alemayehu. "Text Independent Amharic Language Speaker Identification in Noisy Environments using Speech Processing Techniques." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 1 (January 1, 2017): 109. http://dx.doi.org/10.11591/ijeecs.v5.i1.pp109-114.

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<p>In Ethiopia, the largest ethnic and linguistic groups are the Oromos, Amharas and Tigrayans. This paper presents the performance analysis of text-independent speaker identification system for the Amharic language in noisy environments. VQ (Vector Quantization), GMM (Gaussian Mixture Models), BPNN (Back propagation neural network), MFCC (Mel-frequency cepstrum coefficients), GFCC (Gammatone Frequency Cepstral Coefficients), and a hybrid approach had been use as techniques for identifying speakers of Amharic language in noisy environments. For the identification process, speech signals are collected from different speakers including both sexes; for our data set, a total of 90 speakers’ speech samples were collected, and each speech have 10 seconds duration from each individual. From these speakers, 59.2%, 70.9% and 84.7% accuracy are achieved when VQ, GMM and BPNN are used on the combined feature vector of MFCC and GFCC. </p>

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