AUTOMATIC TIMBRE CLASSIFICATION OF ETHNOMUSICOLOGICAL AUDIO RECORDINGS

Abstract : Automatic timbre characterization of audio signals can help to measure similarities between sounds and is of in-terest for automatic or semi-automatic databases indexing. The most effective methods use machine learning approaches which require qualitative and diversified training databases to obtain accurate results. In this paper, we introduce a diversified database composed of worldwide non-western instruments audio recordings on which is evaluated a com-putationally effective timbre classification method. A com-parative evaluation based on the well studied Iowa musical instruments database shows results comparable with those of state-of-the-art methods. Thus, the proposed method offers a practical solution for automatic ethnomusicologi-cal indexing of a database composed of diversified sounds with various quality. The relevance of audio features for the timbre characterization is also discussed in the context of non-western instruments analysis.
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Submitted on : Wednesday, December 17, 2014 - 2:25:06 PM
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Dominique Fourer, Jean-Luc Rouas, Pierre Hanna, Matthias Robine. AUTOMATIC TIMBRE CLASSIFICATION OF ETHNOMUSICOLOGICAL AUDIO RECORDINGS. International Society for Music Information Retrieval Conference (ISMIR 2014), Oct 2014, Taipei, Taiwan. ⟨hal-01095153⟩

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