The Timbre Toolbox: extracting audio descriptors from musical signals.

By Geoffroy Peeters, Bruno L. Giordano, P. Susini, N. Misdariis, S. McAdams. Journal of the Acoustical Society of America, 2011

421 citations recorded by the source corpus.

How researchers see this paper

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Search Result Clustering in Collaborative Sound Collections (2020, methodology): “Combining these different types of features enables the representation of the timbre of musical instruments or more high-level characteristics such as music moods and genres [11, 18, 23, 31, 41].”
TSFEL: Time Series Feature Extraction Library (2020, methodology): “Finally, the goniometer _ y _ Slope is an adequate feature to discriminate between sit-to-stand and stand-to-sit.”
Neural Music Synthesis for Flexible Timbre Control (2018, background): “Timbre is often modeled using a timbre space [8], in which similar timbres lie closer than dissimilar timbres.”