Acoustic Cues to Beat Induction: A Machine Learning Perspective
Sprache des Titels:
This paper brings forward the question of which acoustic features are the
most adequate for identifying beats computationally in acoustic music pieces.
We consider many different features computed on consecutive short portions
of acoustic signal, among which those currently promoted in the literature
on beat induction from acoustic signals and several original features, unmentioned
in this literature. Evaluation of feature sets regarding their ability
to provide reliable cues to the localization of beats is based on a machine
learning methodology with a large corpus of beat-annotated music pieces, in
audio format, covering distinctive music categories.
Confirming common knowledge, energy is shown to be a very relevant cue
to beat induction (especially the temporal variation of energy in various
frequency bands, with the special relevance of frequency bands below 500 Hz
and above 5 kHz). Some of the new features proposed in this paper are
shown to outperform features currently promoted in the literature on beat
induction from acoustic signals. We finally hypothesize that modelling beat
induction may involve many different, complementary, acoustic features
and that the process of selecting relevant features should partly depend on
acoustic properties of the very signal under consideration.