Tzanetakis, G., and P. Cook. ‘‘Musical Genre Classification
of Audio Signals.’’ IEEE Transactions on Speech and Audio Processing
10, n. 5, pp. 293–302. 2002.
Abstract
Musical genres are categorical labels created by humans
to characterize pieces of music. A musical genre is characterized
by the common characteristics shared by its members.
These characteristics typically are related to the instrumentation,
rhythmic structure, and harmonic content of the music. Genre hierarchies
are commonly used to structure the large collections of
music available on the Web. Currently musical genre annotation
is performed manually. Automatic musical genre classification can
assist or replace the human user in this process and would be a
valuable addition to music information retrieval systems. In addition,
automatic musical genre classification provides a framework
for developing and evaluating features for any type of content-
based analysis of musical signals.
In this paper, the automatic classification of audio signals into
an hierarchy of musical genres is explored. More specifically,
three feature sets for representing timbral texture, rhythmic
content and pitch content are proposed. The performance and
relative importance of the proposed features is investigated by
training statistical pattern recognition classifiers using real-world
audio collections. Both whole file and real-time frame-based
classification schemes are described. Using the proposed feature
sets, classification of 61% for ten musical genres is achieved. This
result is comparable to results reported for human musical genre
classification.
Index Terms
Audio classification, beat analysis, feature extraction,
musical genre classification, wavelets.