Audio Oracle Analysis of Musical Information Rate

Shlomo Dubnov (1) , Gérard Assayag (2) , Arshia Cont (3)

1 UC San Diego - University of California [San Diego]
2 STMS - Sciences et Technologies de la Musique et du Son
3 IRCAM - Institut de Recherche et Coordination Acoustique/Musique

This paper presents a method for analysis of changes in information contents in music based on an audio representation called Audio Oracle (AO). Using compression properties of AO we estimate the amount of information that passes between the past and the present at every instance in a musical signal. This formulation extends the notion of Information Rate (IR) to individual sequences and allows an optimal estimation of the AO threshold parameter. We show that changes in IR correspond to significant musical structures such as sections in a sonata form. Relation to musical perception and applications for composition and improvisation are discussed in the paper.

Domaines: Son [cs.SD]; Machine Learning [stat.ML]

Shlomo Dubnov, Gérard Assayag, Arshia Cont. Audio Oracle Analysis of Musical Information Rate. Proceedings of IEEE Semantic Computing Conference (ICSC2011), 2011, Unknown, United States. pp.567--571. ⟨hal-00839065⟩