Exploring Billions of Audio Features

Many works in audio and image signal analysis are based on the use of "features" to represent characteristics of sounds or images. Features are used in various ways, for instance as inputs to classifiers to categorize automatically objects, e.g. for audio scene description. Most, if not all, approaches focus on the development of clever classifiers and on the various processes of feature selection, classifier algorithms and parameter tuning. Strangely, the features themselves are rarely justified. The predominant paradigm consists in selecting, by hand, "generic", well-known features from the literature and focusing on the rest of the chain. In this study we try to generalize the notion of feature to make the choice of features more systematic and less prone to hazardous, unjustified human choices. We introduce to this aim the notion of "analytical feature": features built only from the analysis of the problem at hand, using a heuristic function generation process. We show some experiments aiming at answering some general questions about analytical features.

[1]  Malcolm Slaney,et al.  Construction and evaluation of a robust multifeature speech/music discriminator , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  Jenny Benois-Pineau,et al.  Audio Features for Noisy Sound Segmentation , 2004, ISMIR.

[3]  François Pachet,et al.  Improving the Classification of Percussive Sounds with Analytical Features: A Case Study , 2007, ISMIR.

[4]  George Tzanetakis,et al.  Automatic Musical Genre Classification of Audio Signals , 2001, ISMIR.

[5]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[6]  Jeroen Breebaart,et al.  Features for audio and music classification , 2003, ISMIR.

[7]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[8]  François Pachet,et al.  The bag-of-frames approach to audio pattern recognition: a sufficient model for urban soundscapes but not for polyphonic music. , 2007, The Journal of the Acoustical Society of America.

[9]  Ajay Kapur,et al.  Retrieval of percussion gestures using timbre classification techniques , 2004, ISMIR.

[10]  Xavier Rodet,et al.  Automatically selecting signal descriptors for SoundClassification , 2002, ICMC.

[11]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[12]  Ye Wang,et al.  Automatic Detection Of Vocal Segments In Popular Songs , 2004, ISMIR.

[13]  Gerhard Widmer,et al.  Improvements of Audio-Based Music Similarity and Genre Classificaton , 2005, ISMIR.

[14]  François Pachet,et al.  Automatic extraction of drum tracks from polyphonic music signals , 2002, Second International Conference on Web Delivering of Music, 2002. WEDELMUSIC 2002. Proceedings..

[15]  Stephen McAdams,et al.  Perspectives on the Contribution of Timbre to Musical Structure , 1999, Computer Music Journal.

[16]  Masataka Goto,et al.  AdaMast: A Drum Sound Recognizer based on Adaptation and Matching of Spectrogram Templates , 2004 .

[17]  Marc Leman,et al.  Classification of Percussive Sounds using Support Vector Machines , 2004 .

[18]  Fabien Gouyon,et al.  Drum sound classification in polyphonic audio recordings using localized sound models , 2004, ISMIR.

[19]  Ichiro Fujinaga,et al.  Automatic Genre Classification Using Large High-Level Musical Feature Sets , 2004, ISMIR.

[20]  Lie Lu,et al.  Automatic mood detection from acoustic music data , 2003, ISMIR.

[21]  John Shawe-Taylor,et al.  An Investigation of Feature Models for Music Genre Classification Using the Support Vector Classifier , 2005, ISMIR.

[22]  Aymeric Zils,et al.  Extraction de descripteurs musicaux : une approche évolutionniste , 2004 .

[23]  Parag Chordia,et al.  Segmentation and Recognition of Tabla Strokes , 2005, ISMIR.

[24]  Aymeric Zils,et al.  EXTRACTING AUTOMATICALLY THE PERCEIVED INTENSITY OF MUSIC TITLES , 2003 .

[25]  G. Peeters.  Automatic Classification of Large Musical Instrument Databases Using Hierarchical Classifiers with Inertia Ratio Maximization , 2003 .

[26]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[27]  Ichiro Fujinaga,et al.  jAudio: An Feature Extraction Library , 2005, ISMIR.

[28]  Stephen Cox,et al.  Features and classifiers for the automatic classification of musical audio signals , 2004, ISMIR.

[29]  Emilia Gómez Gutiérrez,et al.  Tonal description of music audio signals , 2006 .

[30]  Gaël Richard,et al.  Automatic Labelling of Tabla Signals , 2003 .

[31]  HongJiang Zhang,et al.  Automatic mood detection from acoustic music data , 2003, ISMIR.

[32]  François Pachet,et al.  ANALYTICAL FEATURES FOR THE CLASSIFICATION OF PERCUSSIVE SOUNDS : THE CASE OF THE PANDEIRO , 2007 .

[33]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[34]  Richard Barnett.  Eugenics , 2004, The Lancet.

[35]  Ichiro Fujinaga,et al.  Beatbox Classification Using ACE , 2005, ISMIR.

[36]  George Tzanetakis,et al.  Musical genre classification of audio signals , 2002, IEEE Trans. Speech Audio Process..

[37]  François Pachet,et al.  Evolving Automatically High-Level Music Descriptors from Acoustic Signals , 2003, CMMR.

[38]  Ichiro Fujinaga,et al.  Feature Selection Pitfalls and Music Classification , 2006, ISMIR.

[39]  Thomas Sikora,et al.  MPEG-7 Audio and Beyond: Audio Content Indexing and Retrieval , 2005 .

[40]  Ian Witten,et al.  Data Mining , 2000 .

[41]  Fabien Gouyon,et al.  Automatic Classification of Drum Sounds: A Comparison of Feature Selection Methods and Classification Techniques , 2002, ICMAI.

[42]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.