A Timbre Analysis And Classification Toolkit For Pure Data

This paper describes example applications of a timbre analysis and classification toolkit for pure data (Pd). The timbreID collection of Pd externals enable both systematic and casual exploration of sound characteristics via objects that are streamlined and easy to use. Details surrounding signal buffering, blocking, and windowing are performed independently by the objects, so that analyses can be obtained with very little patching. A modular design allows for adaptable configurations and many possible creative ends. The applications described here include vowel classification, targetdriven concatenative synthesis, ordering sounds by timbre, and mapping of a sound set in timbre space.

[1]  Ian H. Witten,et al.  WEKA: a machine learning workbench , 1994, Proceedings of ANZIIS '94 - Australian New Zealnd Intelligent Information Systems Conference.

[2]  Jamie Bullock,et al.  Libxtract: a Lightweight Library for audio Feature Extraction , 2007, ICMC.

[3]  Zbigniew W. Ras,et al.  Analysis of Sound Features for Music Timbre Recognition , 2007, 2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07).

[4]  Michael A. Casey,et al.  Soundspotter / remix-TV: Fast Approximate Matching for audio and video Performance , 2007, ICMC.

[5]  Miller Puckette,et al.  Real-time audio analysis tools for Pd and MSP , 1998, ICMC.

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

[7]  W. Brent Cepstral Analysis Tools for Percussive Timbre Identification , 2011 .

[8]  Diemo Schwarz,et al.  REAL-TIME CORPUS-BASED CONCATENATIVE SYNTHESIS WITH CATART , 2006 .