General sound classification and similarity in MPEG-7

We introduce a system for generalised sound classification and similarity using a machine-learning framework. Applications of the system include automatic classification of environmental sounds, musical instruments, music genre and human speakers. In addition to classification, the system may also be used for computing similarity metrics between a target sound and other sounds in a database. We discuss the use of hidden Markov models for representing the temporal evolution of audio spectra and present results of testing the system on classification and retrieval tasks. The system has been incorporated into the MPEG-7 international standard for multimedia content description and is therefore publicly available in the form of a set of standardised interfaces and software reference tools for developers and researchers.

[1]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[2]  Douglas Keislar,et al.  Content-Based Classification, Search, and Retrieval of Audio , 1996, IEEE Multim..

[3]  Matthew Brand,et al.  Structure Learning in Conditional Probability Models via an Entropic Prior and Parameter Extinction , 1999, Neural Computation.

[4]  Jean-François Cardoso,et al.  Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..

[5]  Michael A. Casey.  Sound Classification and Similarity Tools , 2002 .

[6]  Youngmoo E. Kim,et al.  Musical instrument identification: A pattern‐recognition approach , 1998 .

[7]  M. Casey,et al.  MPEG-7 sound-recognition tools , 2001, IEEE Trans. Circuits Syst. Video Technol..

[8]  Michael A. Casey,et al.  Separation of Mixed Audio Sources By Independent Subspace Analysis , 2000, ICMC.

[9]  Wang Ru,et al.  Multimedia content description interface——MPEG-7 , 2003 .

[10]  Terrence J. Sejnowski,et al.  Blind separation and blind deconvolution: an information-theoretic approach , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[11]  C.-C. Jay Kuo,et al.  Content-based classification and retrieval of audio , 1998, Optics & Photonics.

[12]  John S. Boreczky,et al.  A hidden Markov model framework for video segmentation using audio and image features , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[13]  Matthew Brand,et al.  Pattern discovery via entropy minimization , 1999, AISTATS.