Environmental noise classifier using a new set of feature parameters based on pitch range

Abstract Automatic Noise Recognition was performed in two stages: (1) feature extraction based on the pitch range, found by analyzing the autocorrelation function and (2) classification using a classifier trained on the extracted features. Since most environmental noise types change their acoustical characteristics over time, we focused on the “pitch range” of the sounds in order to extract features. Two different classifiers, Support Vector Machines (SVM) and k-means clustering, were performed and compared using the proposed features. The SVM and k-means clustering classifiers achieve recognition rates up to 95.4% and 92.8%, respectively. Although both classifiers provided high accuracy, the SVM-based classifier outperformed the k-means clustering classifier by approximately 7.4%.

[1]  Anssi Klapuri,et al.  Multipitch Analysis of Polyphonic Music and Speech Signals Using an Auditory Model , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[2]  Craig Stuart Sapp,et al.  Efficient Pitch Detection Techniques for Interactive Music , 2001, ICMC.

[3]  Yuan Yao,et al.  Fingerprint Classification with Combinations of Support Vector Machines , 2001, AVBPA.

[4]  John L. Semmlow,et al.  Biosignal and Medical Image Processing , 2004 .

[5]  Giuseppe Ruggeri,et al.  New results in fuzzy pattern classification of background noise , 2000, WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.

[6]  Augusto Sarti,et al.  Scream and gunshot detection in noisy environments , 2007, 2007 15th European Signal Processing Conference.

[7]  Lie Lu,et al.  Using structure patterns of temporal and spectral feature in audio similarity measure , 2003, MULTIMEDIA '03.

[8]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[9]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[10]  Bo-Suk Yang,et al.  Cavitation detection of butterfly valve using support vector machines , 2005 .

[11]  Vincent Fontaine,et al.  AUTOMATIC CLASSIFICATION OF ENVIRONMENTAL NOISE EVENTS BY HIDDEN MARKOV MODELS , 1998 .

[12]  Buket D. Barkana,et al.  Automatic environmental noise source classification model using fuzzy logic , 2011, Expert Syst. Appl..

[13]  Ben P. Milner,et al.  Acoustic environment classification , 2006, TSLP.

[14]  Martin Bouchard,et al.  Efficient classification of noisy speech using neural networks , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..

[15]  Dragutin Petkovic,et al.  Towards robust features for classifying audio in the CueVideo system , 1999, MULTIMEDIA '99.

[16]  Stéphane Bressan,et al.  Environmental Noise Classification for Multimedia Libraries , 2005, DEXA.

[17]  Albert S. Bregman,et al.  The Auditory Scene. (Book Reviews: Auditory Scene Analysis. The Perceptual Organization of Sound.) , 1990 .

[18]  Buket D. Barkana,et al.  The Acoustic Properties of Different Noise Sources , 2009, 2009 Sixth International Conference on Information Technology: New Generations.