{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T03:20:54Z","timestamp":1783999254055,"version":"3.55.0"},"reference-count":85,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,2,6]],"date-time":"2020-02-06T00:00:00Z","timestamp":1580947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Ministry of Science and Technology, Taiwan","award":["MOST 106-2221-E-158-004, MOST 107-2221-E-158 -003 and MOST-108-2221-E-158 -003"],"award-info":[{"award-number":["MOST 106-2221-E-158-004, MOST 107-2221-E-158 -003 and MOST-108-2221-E-158 -003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>A robust approach for the application of audio content classification (ACC) is proposed in this paper, especially in variable noise-level conditions. We know that speech, music, and background noise (also called silence) are usually mixed in the noisy audio signal. Based on the findings, we propose a hierarchical ACC approach consisting of three parts: voice activity detection (VAD), speech\/music discrimination (SMD), and post-processing. First, entropy-based VAD is successfully used to segment input signal into noisy audio and noise even if variable-noise level is happening. The determinations of one-dimensional (1D)-subband energy information (1D-SEI) and 2D-textural image information (2D-TII) are then formed as a hybrid feature set. The hybrid-based SMD is achieved because the hybrid feature set is input into the classification of the support vector machine (SVM). Finally, a rule-based post-processing of segments is utilized to smoothly determine the output of the ACC system. The noisy audio is successfully classified into noise, speech, and music. Experimental results show that the hierarchical ACC system using hybrid feature-based SMD and entropy-based VAD is successfully evaluated against three available datasets and is comparable with existing methods even in a variable noise-level environment. In addition, our test results with the VAD scheme and hybrid features also shows that the proposed architecture increases the performance of audio content discrimination.<\/jats:p>","DOI":"10.3390\/e22020183","type":"journal-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T03:13:27Z","timestamp":1581045207000},"page":"183","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Robust Audio Content Classification Using Hybrid-Based SMD and Entropy-Based VAD"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6478-7277","authenticated-orcid":false,"given":"Kun-Ching","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Information Technology &amp; Communication, Shih Chien University, No. 200, University Rd, Neimen Shiang, Kaohsiung 845, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ong, W.Q., Tan, A.W.C., Vengadasalam, V.V., Tan, C.H., and Ooi, T.H. (2017). Real-Time Robust Voice Activity Detection Using the Upper Envelope Weighted Entropy Measure and the Dual-Rate Adaptive Nonlinear Filter. Entropy, 19.","DOI":"10.3390\/e19110487"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13634-015-0277-z","article-title":"Features for voice activity detection: A comparative analysis","volume":"2015","author":"Graf","year":"2015","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TASL.2010.2052803","article-title":"Robust voice activity detection using long-term signal variability","volume":"19","author":"Ghosh","year":"2010","journal-title":"IEEE Trans. AudioSpeechand Lang. Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1186\/1687-4722-2013-21","article-title":"Efficient voice activity detection algorithm using long-term spectral flatness measure","volume":"2013","author":"Ma","year":"2013","journal-title":"EURASIP J. AudioSpeechand Music Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1109\/TASL.2012.2229986","article-title":"Deep belief networks based voice activity detection","volume":"21","author":"Zhang","year":"2013","journal-title":"IEEE Trans. AudioSpeechand Lang. Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"514","DOI":"10.17743\/jaes.2016.0022","article-title":"Improving Multilingual Interaction for Consumer Robots through Signal Enhancement in Multichannel Speech","volume":"64","author":"Tsardoulias","year":"2016","journal-title":"J. Audio Eng. Soc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MMUL.2015.33","article-title":"Syncing Shared Multimedia through Audiovisual Bimodal Segmentation","volume":"22","author":"Dimoulas","year":"2015","journal-title":"IEEE Multimed."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"25603","DOI":"10.1007\/s11042-016-4315-0","article-title":"Efficient audio-driven multimedia indexing through similarity-based speech\/music discrimination","volume":"76","author":"Tsipas","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.17743\/jaes.2016.0051","article-title":"Crowdsourcing audio semantics by means of hybrid bimodal segmentation with hierarchical classification","volume":"64","author":"Vrysis","year":"2016","journal-title":"J. Audio Eng. Soc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/j.specom.2012.01.004","article-title":"Investigation of broadcast-audio semantic analysis scenarios employing radioprogramme-adaptive pattern classification","volume":"54","author":"Kotsakis","year":"2012","journal-title":"Speech Commun."},{"key":"ref_11","unstructured":"Dimoulas, C., and Kalliris, G. (2013, January 4\u20137). Investigation \u03bff wavelet approaches for joint temporal, spectral and cepstral features in audio semantics. Proceedings of the 134th AES Convention, Rome, Italy."},{"key":"ref_12","unstructured":"Vrysis, L., Tsipas, N., Dimoulas, C., and Papanikolaou, G. (2017, January 20\u201323). Extending Temporal Feature Integration for Semantic Audio Analysis. Proceedings of the 142th Audio Engineering Society (AES) Convention, Berlin, Germany."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"807162","DOI":"10.1155\/2009\/807162","article-title":"Exploiting temporal feature integration for generalized sound recognition","volume":"2009","author":"Ntalampiras","year":"2009","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_14","unstructured":"Mathieu, B., Essid, S., Fillon, T., Prado, J., and Richard, G. (2010, January 9\u201313). Yaafe, an easy to use and efficient audio feature extraction software. Proceedings of the Eleventh International Society for Music Information Retrieval Conference (ISMIR 2010), Utrecht, The Netherlands."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"209814","DOI":"10.1155\/2015\/209814","article-title":"Optimized audio classification and segmentation algorithm by using ensemble methods","volume":"2015","author":"Zahid","year":"2015","journal-title":"Math. Probl. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lerch, A. (2012). An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics, John Wiley & Sons.","DOI":"10.1002\/9781118393550"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Deng, L., Hinton, G., and Kingsbury, B. (2013, January 26\u201331). New types of deep neural network learning for speech recognition and related applications: An overview. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6639344"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.neucom.2016.03.020","article-title":"An overview of applications and advancements in automatic sound recognition","volume":"200","author":"Sharan","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_19","unstructured":"Saunders, J. (1996, January 9). Real-time discrimination of broacast speech\/music. Proceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference (ICASSP\u201996), Atlanta, GA, USA."},{"key":"ref_20","unstructured":"Scheirer, E., and Slaney, M. (1997, January 21\u201324). Construction and evaluation of a robust multifeature speech\/music discriminator. Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP\u201997), Munich, Germany."},{"key":"ref_21","unstructured":"El-Maleh, K., Klein, M., Petrucci, G., and Kabal, P. (2000, January 5\u20139). Speech\/music discrimination for multimedia applications. Proceedings of the 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP\u20192000), Istanbul, Turkey."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Harb, H., and Chen, L. (2003, January 4). Robust speech music discrimination using spectrum\u2019s first order statistics and neural networks. Proceedings of the Seventh International Symposium on Signal Processing and Its Applications, Paris, France.","DOI":"10.1109\/ISSPA.2003.1224831"},{"key":"ref_23","unstructured":"Wang, W.Q., Gao, W., and Ying, D.W. (2003, January 15\u201318). A fast and robust speech\/music discrimination approach. Proceedings of the 4th Pacific Rim Conference on Multimedia, Singapore."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Keum, J.S., and Lee, H.S. (2006, January 12\u201315). Speech\/Music Discrimination using Spectral Peak Feature for Speaker Indexing. Proceedings of the 2006 International Symposium on Intelligent Signal Processing and Communications (ISPACS \u201806), Tottori, Japan.","DOI":"10.1109\/ISPACS.2006.364897"},{"key":"ref_25","unstructured":"Eronen, A., and Klapuri, A. (2000, January 5\u20139). Musical instrument recognition using cepstral coefficients and temporal features. Proceedings of the 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing (Cat. No. 00CH37100), Istanbul, Turkey."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ghosal, A., Chakraborty, R., Chakraborty, R., Haty, S., Dhara, B.C., and Saha, S.K. (2009, January 21\u201322). Speech\/music classification using occurrence pattern of zcr and ste. Proceedings of the 2009 Third International Symposium on Intelligent Information Technology Application, Shanghai, China.","DOI":"10.1109\/IITA.2009.427"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"West, K., and Cox, S. (2004, January 10\u201314). Features and classifiers for the automatic classification of musical audio signals. Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), Barcelona, Spain.","DOI":"10.1045\/december2004-droettboom"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1162\/014892604323112211","article-title":"The scientific evaluation of music information retrieval systems: Foundations and future","volume":"28","author":"Downie","year":"2004","journal-title":"Comput. Music J."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Beigi, H.S., Maes, S.H., Chaudhari, U.V., and Sorensen, J.S. (1999, January 5\u20139). A hierarchical approach to large-scale speaker recognition. Proceedings of the Sixth European Conference on Speech Communication and Technology, Budapest, Hungary.","DOI":"10.21437\/Eurospeech.1999-488"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"McKay, C., and Fujinaga, I. (2004, January 10\u201314). Automatic Genre Classification Using Large High-Level Musical Feature Sets. Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), Barcelona, Spain.","DOI":"10.1045\/december2004-droettboom"},{"key":"ref_31","unstructured":"West, K., and Cox, S. (2005, January 11\u201315). Finding An Optimal Segmentation for Audio Genre Classification. Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London, UK."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, T., and Kuo, C.-C.J. (1998, January 2). Content-based classification and retrieval of audio. Proceedings of the Advanced Signal Processing Algorithms, Architectures, and Implementations VIII, San Diego, CA, USA.","DOI":"10.1117\/12.325703"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ren, J.-M., Chen, Z.-S., and Jang, J.-S.R. (2010, January 14\u201319). On the use of sequential patterns mining as temporal features for music genre classification. Proceedings of the 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, TX, USA.","DOI":"10.1109\/ICASSP.2010.5495955"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ghosal, A., Chakraborty, R., Dhara, B.C., and Saha, S.K. (2011, January 8\u201310). Instrumental\/song classification of music signal using ransac. Proceedings of the 2011 3rd International Conference on Electronics Computer Technology, Kanyakumari, India.","DOI":"10.1109\/ICECTECH.2011.5941603"},{"key":"ref_35","unstructured":"Berenzweig, A.L., and Ellis, D.P. (2001, January 24\u201324). Locating singing voice segments within music signals. Proceedings of the 2001 IEEE Workshop on the Applications of Signal Processing to Audio and Acoustics (Cat. No. 01TH8575), New Platz, NY, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, T., and Kuo, C.C.J. (2001). Content-based Audio Classification and Retrieval for Audiovisual Data Parsing, Kluwer Academic.","DOI":"10.1007\/978-1-4757-3339-6"},{"key":"ref_37","unstructured":"Zhang, T. (2003, January 26). Semi-automatic approach for music classification. Proceedings of the SPIE Conference on Internet Multimedia Management Systems, Orlando, FL, USA."},{"key":"ref_38","first-page":"224","article-title":"A model for the prediction of thresholds, loudness and partial loudness","volume":"45","author":"Moore","year":"1997","journal-title":"J. Audio Eng. Soc"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zwicker, E., and Fastl, H. (1999). Psychoacoustics: Facts and Models, Springer.","DOI":"10.1007\/978-3-662-09562-1"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yu, G., and Slotine, J.-J. (2009, January 19\u201324). Audio classification from time frequency texture. Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Taipei, Taiwan.","DOI":"10.1109\/ICASSP.2009.4959924"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1109\/LSP.2010.2100380","article-title":"Spectrogram image feature for sound event classification in mismatched conditions","volume":"18","author":"Dennis","year":"2011","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_42","unstructured":"Foote, J.T. (1997, January 6). Content-based retrieval of music and audio. Proceedings of the Multimedia Storage and Archiving Systems II, Dallas, TX, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2959","DOI":"10.1121\/1.409056","article-title":"Classification of music type by a multilayer neural network","volume":"95","author":"Matityaho","year":"1994","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tsai, W.-H., and Bao, D.-F. (2010, January 21\u201323). Clustering music recordings based on genres. Proceedings of the 2010 International Conference on Information Science and Applications, Seoul, Korea.","DOI":"10.1109\/ICISA.2010.5480365"},{"key":"ref_45","first-page":"27","article-title":"Audio content analysis for online audiovisual data segmentation and classification","volume":"3","author":"Zhang","year":"2001","journal-title":"IEEE Trans. Speech Audio Process."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Simsekli, U. (2010, January 23\u201326). Automatic music genre classification using bass lines. Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.1006"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/TSA.2002.800560","article-title":"Music genre classification of audio signals","volume":"10","author":"Tzanetakis","year":"2002","journal-title":"IEEE Trans. Speech Audio Process."},{"key":"ref_48","unstructured":"Costa, Y.M.G., Oliveira, L.S., Koreich, A.L., and Gouyon, F. (2011, January 16\u201318). Music genre recognition using spectrograms. Proceedings of the 2011 18th International Conference on Systems, Signals and Image Processing, Sarajevo, Bosnia-Herzegovina."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Fernandez, F., Chavez, F., Alcala, R., and Herrera, F. (2011, January 5\u20138). Musical genre classification by means of fuzzy rule-based systems: A preliminary approach. Proceedings of the 2011 IEEE Congress of Evolutionary Computation (CEC), New Orleans, LA, USA.","DOI":"10.1109\/CEC.2011.5949938"},{"key":"ref_50","unstructured":"Pikrakis, A., and Theodoridis, S. (2014, January 1\u20135). Speech-music discrimination: A deep learning perspective. Proceedings of the 2014 22nd European Signal Processing Conference (EUSIPCO), Lisbon, Portugal."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Lee, J., Park, J., Kim, K.L., and Nam, J. (2018). SampleCNN: End-to-End Deep Convolutional Neural Networks Using Very Small Filters for Music Classification. Appl. Sci., 8.","DOI":"10.3390\/app8010150"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_53","first-page":"949","article-title":"A Wavelet-Based Voice Activity Detection Algorithm in Variable-Level Noise Environment","volume":"6","author":"Wang","year":"2009","journal-title":"WSEAS Trans. Comput."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Laws, K.I. (1980). Textured image segmentation. [Ph.D. Thesis, University of Southern California].","DOI":"10.21236\/ADA083283"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1458","DOI":"10.3390\/s150101458","article-title":"Time-Frequency Feature Representation Using Multi-Resolution Texture Analysis and Acoustic Activity Detector for Real-Life Speech Emotion Recognition","volume":"15","author":"Wang","year":"2015","journal-title":"Sensors"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wang, K.-C., Yang, Y.-M., and Yang, Y.-R. (2017, January 21\u201323). Speech\/music discrimination using hybrid-based feature extraction for audio data indexing. Proceedings of the 2017 International Conference on System Science and Engineering, (ICSSE 2017), Ho Chi Minh City, Vietnam.","DOI":"10.1109\/ICSSE.2017.8030927"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Neammalai, P., Phimoltares, S., and Lursinsap, C. (2014, January 9\u201312). Speech and music classification using hybrid Form of spectrogram and Fourier transformation. Proceedings of the Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific, Siem Reap, Cambodia.","DOI":"10.1109\/APSIPA.2014.7041658"},{"key":"ref_58","unstructured":"Yeh, J.H. (2004). Emotion Recognition from Mandarin Speech Signals. [Master\u2019s Thesis, Tatung University]."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1109\/89.294354","article-title":"A Robust Algorithm for Word Boundary Detection in the Presence of Noise","volume":"2","author":"Junqua","year":"1994","journal-title":"IEEE Trans. Speech Audio Process."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1109\/TASSP.1981.1163642","article-title":"An Improved Endpoint Detector for Isolated Word Recognition","volume":"29","author":"Lamel","year":"1981","journal-title":"IEEE ASSP Magazine"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1109\/TSA.2005.851909","article-title":"A Robust Endpoint Detection Algorithm Based on the Adaptive Band-Partitioning Spectral Entropy in Adverse Environments","volume":"13","author":"Wu","year":"2005","journal-title":"IEEE Trans. Speech Audio Process."},{"key":"ref_62","unstructured":"(2020, January 29). Wikipedia. Available online: https:\/\/en.wikipedia.org\/wiki\/Bark_scale."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.bspc.2006.08.004","article-title":"Novel wavelet domain Wiener filtering de-noising techniques: Application to bowel sounds captured by means of abdominal surface vibrations","volume":"1","author":"Dimoulas","year":"2006","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1587\/transinf.E93.D.341","article-title":"An Adaptive Wavelet-Based Denoising Algorithm for Enhancing Speech in Non-stationary Noise Environment","volume":"93","author":"Wang","year":"2010","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1109\/89.861373","article-title":"Word boundary detection with mel-scale frequency bank in noise environment","volume":"8","author":"Wu","year":"2000","journal-title":"IEEE Trans. Speech Audio Process."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1023\/B:VLSI.0000015092.19005.62","article-title":"Speech enhancement using perceptual wavelet packet decomposition and teager energy operator","volume":"36","author":"Chen","year":"2004","journal-title":"J. VLSI Signal Process."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2723","DOI":"10.1016\/j.sigpro.2012.04.023","article-title":"Music genre classification using LBP textural features","volume":"92","author":"Costa","year":"2012","journal-title":"Signal Process."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Costa, Y., Oliveira, L., Koerich, A., and Gouyon, F. (2012, January 10\u201315). Comparing textural features for music genre classification. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, QLD, Australia.","DOI":"10.1109\/IJCNN.2012.6252626"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/30.663728","article-title":"Genre classification system of tv sound signals based on a spectrogram analysis","volume":"44","author":"Han","year":"1998","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"16692","DOI":"10.3390\/s140916692","article-title":"The Feature Extraction Based on Texture Image Information for Emotion Sensing in Speech","volume":"14","author":"Wang","year":"2014","journal-title":"Sensors"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Ding, P., Chen, Z., Liu, Y., and Xu, B. (2002, January 13\u201317). Asymmetrical support vector machines and applications in speech processing. Proceedings of the 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando, FL, USA.","DOI":"10.1109\/ICASSP.2002.5743657"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2348","DOI":"10.1109\/TSP.2004.831018","article-title":"Applications of support vector machines to speech recognition","volume":"52","author":"Ganapathiraju","year":"2004","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/TNN.2002.806626","article-title":"Content-based audio classification and retrieval by support vector machines","volume":"14","author":"Guo","year":"2003","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_75","unstructured":"(2016, July 30). Gtzan Music Speech Dataset. Available online: http:\/\/marsyasweb.appspot.com\/download\/datasets\/."},{"key":"ref_76","unstructured":"Defferrard, M., Benzi, K., Vandergheynst, P., and Bresson, X. (2016). Fma: A dataset for music analysis. arXiv."},{"key":"ref_77","unstructured":"Goto, M., Hashiguchi, H., Nishimura, T., and Oka, R. (2002, January 13\u201317). RWC Music Database: Popular, Classical and Jazz Music Databases. Proceedings of the 3rd International Conference on Music Information Retrieval (ISMIR 2002), Paris, France."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/97.736233","article-title":"A statistical model-based voice activity detection","volume":"16","author":"Sohn","year":"1999","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1109\/LSP.2005.855551","article-title":"Statistical voice activity detection using a multiple observation likelihood ratio test","volume":"12","author":"Ramirez","year":"2005","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_80","unstructured":"(1996). ITU-T Recommendation G.729-Annex B, A silence compression scheme for G.729 optimized for terminals conforming to recommendation V.70."},{"key":"ref_81","unstructured":"(1999). ETSI EN 301708 recommendation, Voice activity detector (VAD) for adaptive multi-rate (AMR) speech traffic channels."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1109\/TASL.2007.894521","article-title":"A soft voice activity detection using GARCH filter and variance Gamma distribution","volume":"15","author":"Tahmasbi","year":"2007","journal-title":"IEEE Trans Audio Speech Lang Process."},{"key":"ref_83","unstructured":"(2020, January 29). Audio Classification Using CNN\u2014An Experiment. Available online: https:\/\/medium.com\/x8-the-ai-community\/audio-classification-using-cnn-coding-example-f9cbd272269e."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1109\/TSA.2002.804546","article-title":"Content analysis for audio classification and segmentation","volume":"10","author":"Lu","year":"2002","journal-title":"IEEE Trans. Speech Audio Process."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/JSTSP.2012.2237379","article-title":"Audiovisual voice activity detection based on microphone arrays and color information","volume":"7","author":"Minotto","year":"2013","journal-title":"IEEE J. Sel. Top. Signal Process."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/2\/183\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:55:12Z","timestamp":1760172912000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/2\/183"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,6]]},"references-count":85,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["e22020183"],"URL":"https:\/\/doi.org\/10.3390\/e22020183","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,6]]}}}