Tim Pohle, Gerhard Widmer, Markus Schedl, Peter Knees,
"Independent Component Analysis for Music Similarity Computation."
: Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR 2007), Victoria, Canada., 2006
Independent Component Analysis for Music Similarity Computation.
Sprache des Titels:
Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR 2007), Victoria, Canada.
In the recent years, a number of publications have appeared
that deal with automatically calculating the similarity
of music tracks. Most of them are based on features
that are not intuitively understandable to humans, as they do
not have a musically meaningful counterpart, but are merely
measures of basic physical properties of the audio signal.
Furthermore, most of these algorithms do not take into account
the temporal development of the audio signal, which
certainly is an important aspect of music. All of them consider
the musical signal as a whole, not trying to reconstruct
the listening process of dividing the signal into a number of
In this work, we present a novel approach to fill this gap
by combining a number of existing ideas. At the heart of
our approach, Independent Component Analysis (ICA) decomposes
an audio signal into individual parts that appear
maximally independent from each other. We present one
basic algorithm to use these components for similarity computations,
and evaluate a number of modifications to it with
respect to genre classification accuracy. Our results indicate
that this approach is at least of similar quality as many existing
feature extraction routines.