An Overview of Audio Event Detection Methods from Feature Extraction to Classification

References

• Acır, N., Ö. Özdamar, and C. Güzeliş. 2006. Automatic classification of auditory brainstem responses using SVM-based feature selection algorithm for threshold detection. Engineering Applications of Artificial Intelligence 19 (2):209–18.
   Web of Science ®Google Scholar
• Agrawala, A. 1970. Learning with a probabilistic teacher. IEEE Transactions on Information Theory 16 (4):373–79.
   Web of Science ®Google Scholar
• Alcala-Fdez, J., R. Alcala, and F. Herrera. 2011. A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning. IEEE Transactions on Fuzzy Systems 19 (5):857–72.
   Web of Science ®Google Scholar
• Andreassen, T., A. Surlykke, and J. Hallam. 2014. Semi-automatic long-term acoustic surveying: A case study with bats. Ecological Informatics 21:13–24.
   Web of Science ®Google Scholar
• Arnold, M. 2002. Subjective and objective quality evaluation of watermarked audio tracks. Proceedings. Second International Conference on Web Delivering of Music. WEDELMUSIC. IEEE.
   Google Scholar
• Atrey, P. K., M. C. Maddage, and M. S. Kankanhalli. 2006. Audio based event detection for multimedia surveillance. International Conference on Acoustics, Speech and Signal Processing, ICASSP Proceedings, IEEE.
   Google Scholar
• Bailey, T., and A. K. Jain. 1978. A note on distance-weighted k-nearest neighbor rules. IEEE Transactions on Systems, Man and Cybernetics 8 (4):311–13.
   Web of Science ®Google Scholar
• Balochian, S., E. A. Seidabad, and S. Z. Rad. 2013. Neural network optimization by genetic algorithms for the audio classification to speech and music. International Journal of Signal Processing, Image Processing & Pattern Recognition 6 (3).
   Google Scholar
• Bardeli, R., D. Wolff, F. Kurth, M. Koch, K. H. Tauchert, and K. H. Frommolt. 2010. Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring. Pattern Recognition Letters 31 (12):1524–34.
   Web of Science ®Google Scholar
• Bauer, E., and R. Kohavi. 1999. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36 (1–2):105–39.
   Web of Science ®Google Scholar
• Baum, L. E., and T. Petrie. 1966. Statistical inference for probabilistic functions of finite state Markov chains. The Annals of Mathematical Statistics 37 (6):1554–63.
   Google Scholar
• Besacier, L., J. F. Bonastre, and C. Fredouille. 2000. Localization and selection of speaker-specific information with statistical modeling. Speech Communication 31 (2–3):89–106.
   Web of Science ®Google Scholar
• Bhatia, N. 2010. Survey of nearest neighbor techniques. International Journal of Computer Science and Information Security (IJCSIS) 8 (2):302–305.
   Google Scholar
• Bhavsar, H., and A. Ganatra. 2012. A comparative study of training algorithms for supervised machine learning. International Journal of Soft Computing and Engineering (IJSCE) 2 (4):2231–2307.
   Google Scholar
• Bin, M., L. Haizhou, and T. Rong. 2007. Spoken language recognition using ensemble classifiers. IEEE Transactions on Audio, Speech, and Language Processing 15 (7):2053–62.
   Google Scholar
• Blum, A., and T. Mitchell. 1998. Combining labeled and unlabeled data with co-training. Proceedings of the eleventh annual conference on Computational learning theory, ACM.
   Google Scholar
• Bourlard, H. A., and N. Morgan. 1993. Connectionist speech recognition: A hybrid approach. Kluwer Academic Publishers. https://link.springer.com/book/10.1007%2F978-1-4615-3210-1
   Google Scholar
• Breiman, L. 1996. Bagging predictors. Machine Learning 24 (2):123–40.
   Web of Science ®Google Scholar
• Breiman, L. 2001. Random forests. Machine Learning 45 (1):5–32.
   Web of Science ®Google Scholar
• Buckley, J. J., and Y. Hayashi. 1994. Fuzzy genetic algorithm and applications. Fuzzy Sets and Systems 61 (2):129–36.
   Web of Science ®Google Scholar
• Busso, C., S. Lee, and S. Narayanan. 2009. Analysis of emotionally salient aspects of fundamental frequency for emotion detection. IEEE Transactions on Audio, Speech, and Language Processing 17 (4):582–96.
   Web of Science ®Google Scholar
• Cakir, E., T. Heittola, H. Huttunen, and T. Virtanen. 2015. Polyphonic sound event detection using multi label deep neural networks. Neural Networks (IJCNN), 2015 International Joint Conference on, IEEE. pp. 1–7.
   Google Scholar
• Campbell, J. P., Jr. 1997. Speaker recognition: A tutorial. Proceedings of the IEEE 85 (9):1437–62.
   Web of Science ®Google Scholar
• Carpenter, G. A., S. Grossberg, and J. H. Reynolds. 1991. ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network. Neural Networks 4 (5):565–88.
   Web of Science ®Google Scholar
• Charalampidis, D., M. Georgiopoulos, and T. Kasparis. 2000. Classification of noisy signal using fuzzy ARTMAP neural networks. International Joint Conference on Neural Networks, IJCNN2000, Proceedings of the IEEE-INNS-ENNS.
   Google Scholar
• Cheng, J., Y. Sun, and L. Ji. 2010. A call-independent and automatic acoustic system for the individual recognition of animals: A novel model using four passerines. Pattern Recognition 43 (11):3846–52.
   Web of Science ®Google Scholar
• Choi, J.-H., and J.-H. Chang. 2012. On using acoustic environment classification for statistical model-based speech enhancement. Speech Communication 54 (3):477–90.
   Web of Science ®Google Scholar
• Chuan, C.-H. 2013. Audio classification and retrieval using wavelets and gaussian mixture models. International Journal Multimed Data Engineering Managed 4 (1):1–20.
   Google Scholar
• Chung-Hsien, W., and H. Chia-Hsin. 2006. Multiple change-point audio segmentation and classification using an MDL-based Gaussian model. IEEE Transactions on Audio, Speech, and Language Processing 14 (2):647–57.
   Google Scholar
• Cintra, M. E., M. C. Monard, E. A. Cherman, and H. De Arruda Camargo. 2011. On the estimation of the number of fuzzy sets for fuzzy rule-based classification systems. 11th International Conference on Hybrid Intelligent Systems (HIS), 2011.
   Google Scholar
• Clavel, C., T. Ehrette, and G. Richard. 2005. Events detection for an audio-based surveillance system. IEEE International Conference on Multimedia and Expo, 2005. ICME 2005.
   Google Scholar
• Cohen, I., N. Sebe, F. G. Gozman, M. C. Cirelo, and T. S. Huang. 2003. Learning Bayesian network classifiers for facial expression recognition both labeled and unlabeled data. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.
   Google Scholar
• Cordón, O., M. J. Del Jesus, and F. Herrera. 1999. A proposal on reasoning methods in fuzzy rule-based classification systems. International Journal of Approximate Reasoning 20 (1):21–45.
   Web of Science ®Google Scholar
• Costa, Y. M. G., L. S. Oliveira, A. L. Koerich, F. Gouyon, and J. G. Martins. 2012. Music genre classification using LBP textural features. Signal Processing 92 (11):2723–37.
   Web of Science ®Google Scholar
• Cover, T., and P. Hart. 1967. Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13 (1):21–27.
   Web of Science ®Google Scholar
• Cui, X., H. Jing, and C. Jen-Tzung. 2012. Multi-view and multi-objective semi-supervised learning for HMM-based automatic speech recognition. IEEE Transactions on Audio, Speech, and Language Processing 20 (7):1923,1935.
   Google Scholar
• Dafna, E., A. Tarasiuk, and Y. Zigel. 2013. Automatic detection of whole night snoring events using non-contact microphone. PLoS One 8 (12). doi:10.1371/journal.pone.0084139
   PubMed Web of Science ®Google Scholar
• Damper, R. I., and J. E. Higgins. 2003. Improving speaker identification in noise by subband processing and decision fusion. Pattern Recognition Letters 24 (13):2167–73.
   Web of Science ®Google Scholar
• Daoudi, K., D. Fohr, and C. Antoine. 2003. Dynamic Bayesian networks for multi-band automatic speech recognition. Computer Speech & Language 17 (2–3):263–85.
   Web of Science ®Google Scholar
• Davis, S. B., and P. Mermelstein. 1980. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech and Signal Processing 28 (4):357–66.
   Google Scholar
• Deller, J. J. R., J. H. L. Hansen, and J. G. Proakis. (2000). Discrete-Time Processing of Speech Signals. Hoboken, New Jersey, USA: Wiley-IEEE Press.
   Google Scholar
• Dhanalakshmi, P., S. Palanivel, and V. Ramalingam. 2009. Classification of audio signals using SVM and RBFNN. Expert Systems with Applications 36 (3):6069–75.
   Web of Science ®Google Scholar
• Dhanalakshmi, P., S. Palanivel, and V. Ramalingam. 2011a. Classification of audio signals using AANN and GMM. Applied Soft Computing 11 (1):716–23.
   Web of Science ®Google Scholar
• Dhanalakshmi, P., S. Palanivel, and V. Ramalingam. 2011b. Pattern classification models for classifying and indexing audio signals. Engineering Applications of Artificial Intelligence 24 (2):350–57.
   Web of Science ®Google Scholar
• Dietterich, T. 2000a. Ensemble methods in machine learning. Multiple Classifier Systems, Springer Berlin Heidelberg 1857:1–15.
   Google Scholar
• Dietterich, T. 2000b. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning 40 (2):139–57.
   Web of Science ®Google Scholar
• Driggers, R. G. 2003. Encyclopedia of Optical Engineering (Vol. 3). Maryland, USA: Marcel Dekker Inc.
   Google Scholar
• Drugman, T. 2014. Using mutual information in supervised temporal event detection: Application to cough detection. Biomedical Signal Processing and Control 10:50–57.
   Web of Science ®Google Scholar
• Espi, M., M. Fujimoto, K. Kinoshita, and T. Nakatani. 2015. Exploiting spectro-temporal locality in deep learning based acoustic event detection. EURASIP Journal on Audio, Speech, and Music Processing 2015 (26): 1–12.
   Web of Science ®Google Scholar
• Fisher, R. A. 1936. The use of multiple measurements in taxonomic problems. Annals of Eugenics 7 (2):179–88.
   Google Scholar
• Freund, Y., and R. E. Schapire. 1996. Experiments with a new boosting algorithm. ICML, Bari, Italy Morgan Kaufmann Publishers Inc.San Francisco, CA, USA.
   Google Scholar
• Freund, Y., and R. E. Schapire. 1997. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55 (1):119–39.
   Web of Science ®Google Scholar
• Friedman, N., D. Geiger, and M. Goldszmidt. 1997. Bayesian network classifiers. Machine Learning 29 (2–3):131–63.
   Web of Science ®Google Scholar
• Ganapathy, S., P. Rajan, and H. Hermansky. 2011. Multi-layer perceptron based speech activity detection for speaker verification. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).
   Google Scholar
• Gencoglu, O., T. Virtanen, and H. Huttunen. 2014. Recognition of acoustic events using deep neural networks. Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European, IEEE, pp. 506–10.
   Google Scholar
• Gergen, S., A. Nagathil, and R. Martin. 2014. Classification of reverberant audio signals using clustered ad hoc distributed microphones. Signal Processing 107:21–32.
   Web of Science ®Google Scholar
• Giannakopoulos, T., D. Kosmopoulos, A. Aristidou, and S. Theodoridis. 2006. Violence content classification using audio features. Advances in Artificial Intelligence, Berlin, Heidelberg: Springer, 502–07.
   Google Scholar
• Giannakopoulos, T., and A. Pikrakis. 2014. Chapter 4 - audio features. In Introduction to audio analysis, Eds. T. Giannakopoulos, and A. Pikrakis, 59–103. Oxford: Academic Press.
   Google Scholar
• Giannakopoulos, T., A. Pikrakis, and S. Theodoridis. 2007. A multi-class audio classification method with respect to violent content in movies using bayesian networks. Multimedia Signal Processing, 2007. MMSP 2007. IEEE 9th Workshop on.
   Google Scholar
• Grossberg, S. 1976. Adaptive pattern classification and universal recoding: II. Feedback, expectation, olfaction, illusions. Biological Cybernetics 23 (4):187–202.
   PubMed Web of Science ®Google Scholar
• Guz, U., S. Cuendet, D. Hakkani-Tür, and G. Tur. 2010. Multi-view semi-supervised learning for dialog act segmentation of speech. IEEE Transactions on Audio, Speech, and Language Processing 18 (2):320,329.
   Google Scholar
• Hall, M. 2007. A decision tree-based attribute weighting filter for naive Bayes. Knowledge-Based Systems 20 (2):120–26.
   Web of Science ®Google Scholar
• Hermansky, H. 1990. Perceptual linear predictive (PLP) analysis of speech. The Journal of the Acoustical Society of America 87 (4):1738–52.
   PubMed Web of Science ®Google Scholar
• Hongwei, W., and J. M. Mendel. 2007. Classification of battlefield ground vehicles using acoustic features and fuzzy logic rule-based classifiers. IEEE Transactions on Fuzzy Systems 15 (1):56–72.
   Web of Science ®Google Scholar
• Huang, C.-J., Y.-J. Yang, D.-X. Yang, and Y.-J. Chen. 2009. Frog classification using machine learning techniques. Expert Systems with Applications 36 (2, Part 2):3737–43.
   Web of Science ®Google Scholar
• Itoh, H., T. Takiguchi, and Y. Ariki. 2013. Event detection and recognition using HMM with whistle sounds. Signal-Image Technology & Internet-Based Systems (SITIS), 2013 International Conference on.
   Google Scholar
• Jain, A. K., R. P. W. Duin, and M. Jianchang. 2000. Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (1):4–37.
   Web of Science ®Google Scholar
• Janik, P., and T. Lobos. 2006. Automated classification of power-quality disturbances using SVM and RBF networks. IEEE Transactions on Power Delivery 21 (3):1663–69.
   Web of Science ®Google Scholar
• Joachims, T. 1999. Transductive inference for text classification using support vector machines. ICML, Morgan Kaufmann Publishers Inc. San Francisco, CA, USA.
   Google Scholar
• Kalteh, A. M., P. Hjorth, and R. Berndtsson. 2008. Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application. Environmental Modelling & Software 23 (7):835–45.
   Web of Science ®Google Scholar
• Kaufman, L., and P. J. Rousseeuw. 1990. Finding groups in data: An introduction to cluster analysis. New York: Wiley.
   Google Scholar
• Khairnar, D. G., S. N. Merchant, and U. B. Desai. 2005. An optimum RBF network for signal detection in non-gaussian noise. In Pattern recognition and machine intelligence, Eds. S. Pal, S. Bandyopadhyay, and S. Biswas, Springer Berlin Heidelberg, 3776:306–309.
   Google Scholar
• Khunarsal, P., C. Lursinsap, and T. Raicharoen. 2013. Very short time environmental sound classification based on spectrogram pattern matching. Information Sciences 243:57–74.
   Web of Science ®Google Scholar
• Kinnunen, T., and H. Li. 2010. An overview of text-independent speaker recognition: From features to supervectors. Speech Communication 52 (1):12–40.
   Web of Science ®Google Scholar
• Kinnunen, T., I. Sidoroff, M. Tuononen, and P. Fränti. 2011. Comparison of clustering methods: A case study of text-independent speaker modeling. Pattern Recognition Letters 32 (13):1604–17.
   Web of Science ®Google Scholar
• Kinnunen, T., B. Zhang, J. Zhu, and Y. Wang. 2007. Speaker verification with adaptive spectral subband centroids. In Advances in biometrics, Eds. S.-W. Lee, and S. Li, Springer Berlin Heidelberg, 4642: 58–66.
   Google Scholar
• Kohonen, T. 1982. Analysis of a simple self-organizing process. Biological Cybernetics 44 (2):135–40.
   Web of Science ®Google Scholar
• Kotti, M., E. Benetos, C. Kotropoulos, and I. Pitas. 2007. A neural network approach to audio-assisted movie dialogue detection. Neurocomputing 71 (1–3):157–66.
   Web of Science ®Google Scholar
• Kulkarni, V. Y., and P. K. Sinha. 2013. Random forest classifiers: A survey and future research directions. Int Journal of Advanced Computing 36 (1):1144–53.
   Google Scholar
• Kumar, A., and B. Raj. 2016. Audio event detection using weakly labeled data. Proceedings of the 2016 ACM on Multimedia Conference, ACM, pp. 1038–47.
   Google Scholar
• Lamel, L., L. Rabiner, A. E. Rosenberg, and J. G. Wilpon. 1981. An improved endpoint detector for isolated word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing 29 (4):777–85.
   Google Scholar
• Larsen, B., and C. Aone. 1999. Fast and effective text mining using linear-time document clustering. Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM.
   Google Scholar
• Lee, C.-H., C.-H. Chou, -C.-C. Han, and R.-Z. Huang. 2006. Automatic recognition of animal vocalizations using averaged MFCC and linear discriminant analysis. Pattern Recognition Letters 27 (2):93–101.
   Web of Science ®Google Scholar
• Lefèvre, S., and N. Vincent. 2011. A two level strategy for audio segmentation. Digital Signal Processing 21 (2):270–77.
   Web of Science ®Google Scholar
• Li, D., I. K. Sethi, N. Dimitrova, and T. McGee. 2001. Classification of general audio data for content-based retrieval. Pattern Recognition Letters 22 (5):533–44.
   Web of Science ®Google Scholar
• Li, H., T. Zhang, and L. Ma. 2012. Confirmation based self-learning algorithm in LVCSR’s semi-supervised incremental learning. Procedia Engineering 29:754–59.
   Google Scholar
• Li, L., G. Fengpei, Z. Qingwei, and Y. Yonghong. 2010. Detecting cheering events in sports games. 2nd International Conference on Education Technology and Computer (ICETC).
   Google Scholar
• Li, X., L. Wang, and E. Sung. 2008. AdaBoost with SVM-based component classifiers. Engineering Applications of Artificial Intelligence 21 (5):785–95.
   Web of Science ®Google Scholar
• Lie, L., Z. Hong-Jiang, and J. Hao. 2002. Content analysis for audio classification and segmentation. IEEE Transactions on Speech and Audio Processing 10 (7):504–16.
   Google Scholar
• Lin, L., Y. Li, and A. Sadek. 2013. A k nearest neighbor based local linear wavelet neural network model for on-line short-term traffic volume prediction. Procedia - Social and Behavioral Sciences 96:2066–77.
   Google Scholar
• Liu, H., and S. Zhang. 2012. Noisy data elimination using mutual k-nearest neighbor for classification mining. Journal of Systems and Software 85 (5):1067–74.
   Web of Science ®Google Scholar
• Liu, Z.-G., Q. Pan, and J. Dezert. 2013. A new belief-based K-nearest neighbor classification method. Pattern Recognition 46 (3):834–44.
   Web of Science ®Google Scholar
• Lu, G. 2001. Indexing and retrieval of audio: A survey. Multimedia Tools and Applications 15 (3):269–90.
   Web of Science ®Google Scholar
• Lu, G.-F., and Y. Wang. 2012. Feature extraction using a fast null space based linear discriminant analysis algorithm. Information Sciences 193:72–80.
   Web of Science ®Google Scholar
• Malhotra, B., I. Nikolaidis, and J. Harms. 2008. Distributed classification of acoustic targets in wireless audio-sensor networks.”. Computation Network 52 (13):2582–93.
   Web of Science ®Google Scholar
• Mayer, R., R. Neumayer, D. Baum, and A. Rauber. 2009. Analytic comparison of self-organising maps. In Advances in self-organizing maps, Eds. J. Príncipe, and R. Miikkulainen, Springer Berlin Heidelberg, 5629: 182–190.
   Google Scholar
• McConaghy, T., H. Leung, E. Bosse, and V. Varadan. 2003. Classification of audio radar signals using radial basis function neural networks. IEEE Transactions on Instrumentation and Measurement 52 (6):1771–79.
   Web of Science ®Google Scholar
• McLoughlin, I., H. Zhang, Z. Xie, Y. Song, and W. Xiao. 2015. Robust sound event classification using deep neural networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing 23 (3):540–52.
   Web of Science ®Google Scholar
• Meyer, C., and H. Schramm. 2006. Boosting HMM acoustic models in large vocabulary speech recognition. Speech Communication 48 (5):532–48.
   Web of Science ®Google Scholar
• Milone, D. H., J. R. Galli, C. A. Cangiano, H. L. Rufiner, and E. A. Laca. 2012. Automatic recognition of ingestive sounds of cattle based on hidden Markov models. Computers and Electronics in Agriculture 87:51–55.
   Web of Science ®Google Scholar
• Mitchell, T. 1999. The role of unlabeled data in supervised learning. Proceedings of the sixth international colloquium on cognitive science, Citeseer.
   Google Scholar
• Mitra, V., and C.-J. Wang. 2008. Content based audio classification: A neural network approach. Soft Computing 12 (7):639–46.
   Web of Science ®Google Scholar
• Moreno, P. J., and S. Agarwal. 2003. An experimental study of EM-based algorithms for semi-supervised learning in audio classification. ICML 2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining.
   Google Scholar
• Muhammad, G., and M. Melhem. 2014. Pathological voice detection and binary classification using MPEG-7 audio features. Biomedical Signal Processing and Control 11:1–9.
   Web of Science ®Google Scholar
• Muñoz-Expósito, J. E., S. García-Galán, N. Ruiz-Reyes, and P. Vera-Candeas. 2007. Adaptive network-based fuzzy inference system vs. other classification algorithms for warped LPC-based speech/music discrimination. Engineering Applications of Artificial Intelligence 20 (6):783–93.
   Web of Science ®Google Scholar
• Navarathna, R., D. Dean, S. Sridharan, and P. Lucey. 2013. Multiple cameras for audio-visual speech recognition in an automotive environment. Computer Speech & Language 27 (4):911–27.
   Web of Science ®Google Scholar
• Neiberg, D., G. Salvi, and J. Gustafson. 2013. Semi-supervised methods for exploring the acoustics of simple productive feedback. Speech Communication 55 (3):451–69.
   Web of Science ®Google Scholar
• Niessen, M. E., T. L. M. Van Kasteren, and A. Merentitis. 2013. Hierarchical modeling using automated sub-clustering for sound event recognition. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).
   Google Scholar
• Nillson, N. 1965. Learning machines: Foundations of trainable pattern classifying systems. New York: McGraw-Hill.
   Google Scholar
• Nirmal, J., S. Patnaik, M. Zaveri, and P. Kachare. 2013. Multi-scale speaker transformation using radial basis function. Procedia Technology 10:311–19.
   Google Scholar
• Nozaki, K., H. Ishibuchi, and H. Tanaka. 1996. Adaptive fuzzy rule-based classification systems. IEEE Transactions on Fuzzy Systems 4 (3):238–50.
   Web of Science ®Google Scholar
• Oppenheim, A. V., R. W. Schafer, and J. R. Buck. 1989. Discrete-time signal processing. Englewood Cliffs: Prentice-hall.
   Google Scholar
• Orio, N. 2010. Automatic identification of audio recordings based on statistical modeling. Signal Processing 90 (4):1064–76.
   Web of Science ®Google Scholar
• Parascandolo, G., H. Huttunen, and T. Virtanen. 2016. Recurrent neural networks for polyphonic sound event detection in real life recordings. Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on, IEEE, pp. 6440–44.
   Google Scholar
• Park, D.-C. 2009. Classification of audio signals using Fuzzy c-means with divergence-based Kernel. Pattern Recognition Letters 30 (9):794–98.
   Web of Science ®Google Scholar
• Pellegrini, T., J. Portêlo, I. Trancoso, A. Abad, and M. Bugalho. 2009. Hierarchical clustering experiments for application to audio event detection. Proceedings of the 13th International Conference on Speech and Computer.
   Google Scholar
• Pimentel, M. A. F., D. A. Clifton, L. Clifton, and L. Tarassenko. 2014. A review of novelty detection. Signal Processing 99:215–49.
   Web of Science ®Google Scholar
• Polikar, R., L. Upda, S. S. Upda, and V. Honavar. 2001. Learn++: An incremental learning algorithm for supervised neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 31 (4):497–508.
   Web of Science ®Google Scholar
• Pomponi, E., and A. Vinogradov. 2013. A real-time approach to acoustic emission clustering. Mechanical Systems and Signal Processing 40 (2):791–804.
   Web of Science ®Google Scholar
• Potamitis, I., S. Ntalampiras, O. Jahn, and K. Riede. 2014. Automatic bird sound detection in long real-field recordings: Applications and tools. Applied Acoustics 80:1–9.
   Web of Science ®Google Scholar
• Prakash, V. J., and D. L. Nithya. 2014. A survey on semi-supervised learning techniques. International Journal of Computer Trends and Technology (IJCTT) 8(1):25–29.
   Google Scholar
• Principe, J. C., N. R. Euliano, and W. C. Lefebvre. 2000. Neural and adaptive systems: Fundamentals through simulations. New York: Wiley.
   Google Scholar
• Prodanov, P., and A. Drygajlo. 2005. Bayesian networks based multi-modality fusion for error handling in human–Robot dialogues under noisy conditions. Speech Communication 45 (3):231–48.
   Web of Science ®Google Scholar
• Qiang, H., and S. Cox. 2011. Inferring the structure of a tennis game using audio information. IEEE Transactions on Audio, Speech, and Language Processing 19 (7):1925–37.
   Google Scholar
• Radhakrishnan, R., A. Divakaran, and P. Smaragdis. 2005. Audio analysis for surveillance applications. Applications of Signal Processing to Audio and Acoustics, 2005. IEEE Workshop on.
   Google Scholar
• Ravan, M., and S. Beheshti. 2011. Speech recognition from adaptive windowing PSD estimation. 24th Canadian Conference on Electrical and Computer Engineering (CCECE), 524–27.
   Google Scholar
• Reaves, B. 1991. Comments on “An improved endpoint detector for isolated word recognition. Signal Processing, IEEE Transactions On 39 (2):526–27.
   Google Scholar
• Reynolds, D. A. 1995. Speaker identification and verification using Gaussian mixture speaker models. Speech Communication 17 (1–2):91–108.
   Web of Science ®Google Scholar
• Reynolds, D. A., T. F. Quatieri, and R. B. Dunn. 2000. Speaker verification using adapted gaussian mixture models. Digital Signal Processing 10 (1–3):19–41.
   Web of Science ®Google Scholar
• Rojek, I., M. Jagodziński, et al. 2012. Hybrid artificial intelligence system in constraint based scheduling of integrated manufacturing ERP systems. In Hybrid artificial intelligent systems, Eds. E. Corchado, V. Snášel, and A. Abraham, Springer Berlin Heidelberg, 7209: 229–240.
   Google Scholar
• Rokach, L. 2009. Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography. Computational Statistics & Data Analysis 53 (12):4046–72.
   Web of Science ®Google Scholar
• Rongyan, W., L. Gang, G. Jun, and M. Zhenxin. 2010. Semi-supervised learning for automatic audio events annotation using TSVM. International Conference on Computer Application and System Modeling (ICCASM).
   Google Scholar
• Ruiz Reyes, N., P. Vera Candeas, S. García Galán, and J. E. Muñoz. 2010. Two-stage cascaded classification approach based on genetic fuzzy learning for speech/music discrimination. Engineering Applications of Artificial Intelligence 23 (2):151–59.
   Web of Science ®Google Scholar
• Santos, A., and A. Canuto. 2014. Applying semi-supervised learning in hierarchical multi-label classification. Expert Systems with Applications 41(14): 6075–6085.
   PubMed Web of Science ®Google Scholar
• Sathya, R., and A. Abraham. 2013. Comparison of supervised and unsupervised learning algorithms for pattern classification. (IJARAI) International Journal of Advanced Research in Artificial Intelligence 2 (2). http://thesai.org/Publications/ViewPaper?Volume=2&Issue=2&Code=IJARAI&SerialNo=6
   Google Scholar
• Scheme, E. J., B. Hudgins, and P. A. Parker. 2007. Myoelectric signal classification for phoneme-based speech recognition. IEEE Transactions on Biomedical Engineering 54 (4):694–99.
   PubMed Web of Science ®Google Scholar
• Schlüter, J. 2016.Learning to pinpoint singing voice from weakly labeled examples. Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pp. 44–50.
   Google Scholar
• Schölkopf, B., A. Smola, and K.-R. Müller. 1998. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10 (5):1299–319.
   Web of Science ®Google Scholar
• Schroeder, J., S. Wabnik, P. J. Hengel, and S. Goetze. 2011. Detection and classification of acoustic events for in-home care. In Ambient assisted living, Eds. R. Wichert, and B. Eberhardt, Springer Berlin Heidelberg, 181–195.
   Google Scholar
• Schuller, B., A. Batliner, S. Steidl, and D. Seppi. 2011. Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge. Speech Communication 53 (9–10):1062–87.
   Web of Science ®Google Scholar
• Schwarz, P., P. Matejka, and J. Cernocky. 2006. Hierarchical structures of neural networks for phoneme recognition. IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP 2006 Proceedings.
   Google Scholar
• Sharma, S., and R. Lal Yadav. 2013. Comparative study of K-means and robust clustering. International Journal of Advanced Computer Research 3 (12):207–210.
   Google Scholar
• Shen, J., J. Shepherd, and A. H. H. Ngu. 2006. Towards effective content-based music retrieval with multiple acoustic feature combination. IEEE Transactions on Multimedia 8 (6):1179–89.
   Web of Science ®Google Scholar
• Shuiping, W., T. Zhenming, and L. Shiqiang. 2011. Design and implementation of an audio classification system based on SVM. Procedia Engineering 15:4031–35.
   Google Scholar
• Skurichina, M., and R. P. W. Duin. 2002. Bagging, boosting and the random subspace method for linear classifiers. Pattern Analysis & Applications 5 (2):121–35.
   Web of Science ®Google Scholar
• Stavrakoudis, D. G., I. Z. Gitas, and J. B. Theocharis. 2011. A hierarchical genetic fuzzy rule-based classifier for high-dimensional classification problems. IEEE International Conference on Fuzzy Systems (FUZZ).
   Google Scholar
• Sturim, D. E., P. A. Torres-Carrasquillo, T. F. Quatieri, N. Malyska, and A. McCree. 2011. Automatic detection of depression in speech using gaussian mixture modeling with factor analysis. Interspeech, Florence, Italy, ISCA.
   Google Scholar
• Su, P.-C., C.-H. Lan, C.-S. Wu, Z.-X. Zeng, and W.-Y. Chen. 2013. Transition effect detection for extracting highlights in baseball videos. EURASIP Journal on Image and Video Processing 2013 (1):1–16.
   Google Scholar
• Sun, Y., S. Todorovic, and J. Li. 2006. Reducing the overfitting of AdaBoost by controlling its data distribution skewness. International Journal of Pattern Recognition and Artificial Intelligence 20 (07):1093–116.
   Google Scholar
• Tao, C. W. 2002. A reduction approach for fuzzy rule bases of fuzzy controllers. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 32 (5):668–75.
   PubMed Web of Science ®Google Scholar
• Temko, A., D. Macho, and C. Nadeu. 2008. Fuzzy integral based information fusion for classification of highly confusable non-speech sounds. Pattern Recognition 41 (5):1814–23.
   Web of Science ®Google Scholar
• Temko, A., and C. Nadeu. 2006. Classification of acoustic events using SVM-based clustering schemes. Pattern Recognition 39 (4):682–94.
   Web of Science ®Google Scholar
• Temko, A., and C. Nadeu. 2009. Acoustic event detection in meeting-room environments. Pattern Recognition Letters 30 (14):1281–88.
   Web of Science ®Google Scholar
• Tianzhu, Z., X. Changsheng, Z. Guangyu, L. Si, and L. Hanqing. 2012. A generic framework for video annotation via semi-supervised learning. IEEE Transactions on Multimedia 14 (4):1206–19.
   Web of Science ®Google Scholar
• Tin Kam, H. 1998. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (8):832–44.
   Web of Science ®Google Scholar
• Tong, Z., and C. C. J. Kuo. 2001. Audio content analysis for online audiovisual data segmentation and classification. IEEE Transactions on Speech and Audio Processing 9 (4):441–57.
   Google Scholar
• Triguero, I., J. A. Sáez, J. Luengo, S. García, and F. Herrera. 2014. On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification. Neurocomputing 132:30–41.
   Web of Science ®Google Scholar
• Truong, T. K., -C.-C. Lin, and S.-H. Chen. 2007. Segmentation of specific speech signals from multi-dialog environment using SVM and wavelet. Pattern Recognition Letters 28 (11):1307–13.
   Web of Science ®Google Scholar
• Tsunoo, E., G. Tzanetakis, N. Ono, and S. Sagayama. 2011. Beyond timbral statistics: Improving music classification using percussive patterns and bass lines. IEEE Transactions on Audio, Speech, and Language Processing 19 (4):1003–14.
   Google Scholar
• Turnbull, D., and C. Elkan. 2005. Fast recognition of musical genres using RBF networks. IEEE Transactions on Knowledge and Data Engineering 17 (4):580–84.
   Web of Science ®Google Scholar
• Tzortzis, G., and A. Likas. 2008. The global kernel k-means clustring algorithm. IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence), IEEE.
   Google Scholar
• Uncini, A. 2003. Audio signal processing by neural networks. Neurocomputing 55 (3–4):593–625.
   Web of Science ®Google Scholar
• Vapnik, V. 1998. Statistical learning theory. New York: Wiley.
   Google Scholar
• Wang, X., and X.-P. Zhang. 2012. Ice hockey shooting event modeling with mixture hidden Markov model. Multimedia Tools and Applications 57 (1):131–44.
   Web of Science ®Google Scholar
• Weimin, H., C. Tuan-Kiang, L. Haizhou, K. Tian Shiang, and J. Biswas. 2010. Scream detection for home applications. 5th IEEE Conference on Industrial Electronics and Applications (ICIEA).
   Google Scholar
• Xiaodan, Z., H. Jing, G. Potamianos, and M. Hasegawa-Johnson. 2009. Acoustic fall detection using Gaussian mixture models and GMM supervectors. IEEE international conference on acoustics. Speech and Signal Processing, 2009. ICASSP 2009.
   Google Scholar
• Xu, Q., L. Zhang, and W. Liang. 2013. Acoustic detection technology for gas pipeline leakage. Process Safety and Environmental Protection 91 (4):253–61.
   Web of Science ®Google Scholar
• Yanan, L., Y. Yilong, L. Lili, P. Shaohua, and Y. Qiuhong. 2012. Semi-supervised gait recognition based on self-training. IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2012.
   Google Scholar
• Yang, J.-C., L.-A. Liu, Q.-W. Qin, and M. Zhang. 2013. Audio event change detection and clustering in movies. Journal of Multimedia 8 (2):113–20.
   Google Scholar
• Yangqiu, S., and Z. Changshui. 2008. Content-based information fusion for semi-supervised music genre classification. IEEE Transactions on MultiMedia 10 (1):145–52.
   Web of Science ®Google Scholar
• Yaochu, J. 2000. Fuzzy modeling of high-dimensional systems: Complexity reduction and interpretability improvement. IEEE Transactions on Fuzzy Systems 8 (2):212–21.
   Web of Science ®Google Scholar
• Ya-Ti, P., L. Ching-Yung, S. Ming-Ting, and T. Kun-Cheng. 2009. Healthcare audio event classification using hidden markov models and hierarchical hidden markov models. IEEE International Conference on Multimedia and Expo 2009. ICME 2009.
   Google Scholar
• Ye, J., and S. Ji. 2009. Discriminant analysis for dimensionality reduction: An overview of recent developments. Biometrics. John Wiley & Sons, Inc, Published Online: 11 NOV 2009.: 1–19.
   Google Scholar
• Ye, T., W. Zuoying, and L. Dajin. 2002. Nonspeech segment rejection based on prosodic information for robust speech recognition. Signal Processing Letters, IEEE 9 (11):364–67.
   Web of Science ®Google Scholar
• Younghyun, L., K. Hanseok, and D. K. Han. 2013. Acoustic signal based abnormal event detection system with multiclass adaboost. IEEE International Conference on Consumer Electronics (ICCE), 2013.
   Google Scholar
• Yunyun, W., C. Songcan, and Z. Zhi-Hua. 2012. New semi-supervised classification method based on modified cluster assumption. IEEE Transactions on Neural Networks and Learning Systems 23 (5):689–702.
   PubMed Web of Science ®Google Scholar
• Zadeh, L. A. 1996. Fuzzy sets and their application to pattern classification and clustering analysis. In Fuzzy sets, fuzzy logic, and fuzzy systems, Eds. J. K. George, and Y. Bo, 355–93. World Scientific Publishing Co., Inc, 355–393.
   Google Scholar
• Zhao, Y., and G. Karypis. 2001. Criterion functions for document clustering: Experiments and analysis. Department of Computer Science and Engineering University of Minnesota,Minneapolis, USA, Technical report.
   Google Scholar
• Zhu, L., and Q. Yang. 2012. Speaker recognition system based on weighted feature parameter. Physics Procedia 25:1515–22.
   Google Scholar
• Zhu, X., and A. B. Goldberg. 2009. Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 3 (1):1–130.
   Google Scholar
• Zolfaghari, P., and T. Robinson. 1996. Formant analysis using mixtures of Gaussians. Fourth International Conference on Spoken Language, 1996. ICSLP 96. Proceedings.
   Google Scholar
• Zubair, S., F. Yan, and W. Wang. 2013. Dictionary learning based sparse coefficients for audio classification with max and average pooling. Digital Signal Processing 23 (3):960–70.
   Web of Science ®Google Scholar
• Zweig, G. 2003. Bayesian network structures and inference techniques for automatic speech recognition. Computer Speech & Language 17 (2–3):173–93.
   Web of Science ®Google Scholar