The Timbre Toolbox: extracting audio descriptors from musical signals
Geoffroy Peeters1, Bruno L Giordano, Patrick Susini
1Institut de Recherche et Coordination Acoustique/Musique (STMS-IRCAM-CNRS), 1 place Igor-Stravinsky, F-75004 Paris, France. geoffroy.peeters@ircam.fr
The Journal of the Acoustical Society of America
|November 18, 2011
Summary
The Timbre Toolbox offers a wide range of audio descriptors for analyzing musical signals and sound events. This comprehensive tool aids in timbre characterization for research and music information retrieval.
Area of Science:
- Acoustics and Signal Processing
- Music Information Retrieval
- Computational Auditory Scene Analysis
Background:
- Previous timbre analysis methods were fragmented and limited in scope.
- A need exists for comprehensive audio descriptors for diverse sound analysis tasks.
Purpose of the Study:
- To introduce the Timbre Toolbox, a comprehensive suite of audio descriptors.
- To facilitate timbre characterization in perceptual research, music information retrieval, and machine learning.
Main Methods:
- Analysis of sound events using multiple input representations (e.g., Fourier transform, auditory models).
- Derivation of global and time-varying audio descriptors capturing temporal, spectral, and energetic properties.
- Statistical analysis, including correlational analysis and hierarchical clustering, to assess descriptor independence.
Main Results:
- The Timbre Toolbox generates a large number of audio descriptors from various sound representations.
- Analysis revealed ten relatively independent classes of audio descriptors.
- The toolbox provides a multidimensional approach to measuring the acoustical structure of sound signals.
Conclusions:
- The Timbre Toolbox is a versatile instrument for comprehensive timbre analysis.
- It supports advanced applications in music information retrieval and machine learning.
- The identified descriptor classes offer a structured understanding of acoustical properties.
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