AUTOMATIC TIMBRE CLASSIFICATION OF ETHNOMUSICOLOGICAL AUDIO RECORDINGS

Dominique Fourer (1) , Jean-Luc Rouas (1) , Pierre Hanna (1) , Matthias Robine (1)

1 LaBRI - Laboratoire Bordelais de Recherche en Informatique

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.

Mots clés: Music Information Retrieval; Audio Analysis

Domaines: Son [cs.SD]; Multimédia [cs.MM]; Traitement du signal et de l'image [eess.SP]; Applications [stat.AP]; Machine Learning [stat.ML]

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⟩