Subject: Fwd: LISA Machine Learning Laboratory -- ... automatic music composition
From: Kevin Austin (firstname.lastname@example.org)
Date: Fri Mar 18 2005 - 08:32:24 EST
CIRMMT is pleased to announce a guest lecture by Douglas Eck, Professor
in the Department of Computer Science at the University of Montreal.
Speaker: Douglas Eck
LISA Machine Learning Laboratory
Department of Computer Science
University of Montreal
Date: Friday, March 18, 2005
Location: Strathcona Music Building, 555 Sherbrooke St. West, Clara
Lichtenstein Recital Hall (C-209)
Title: Entropy and Autocorrelation: Using Simple Statistics to Find
Tempo and Metrical Structure in Unfiltered Digital Audio
Autocorrelation is a simple, fast-to-compute statistical method that
has long been used to discover metrical structure in music (e.g. Judy
Brown, 1993). Because autocorrelation can be performed online and
works on any time series, it is a promising method for detecting
temporal regularities in music.
However autocorrelation has a significant limitation: while it
provides the relative magnitude of signal energy at different
periods, it discards all information about phase. Furthermore,
autocorrelation does not tend to work well for vocals and for
non-percussive musical instruments such as strings. I provide a short
analysis of these limitations and address them by offering a
relatively fast method that computes a "phase-preserving"
autocorrelation. The resulting phase-by-period matrix provides
information relevant for tempo tracking and for meter prediction as
well as for related tasks such as beat induction. In this talk I will
focus on how a (Shannon) entropy analysis of phase information can
significantly enhance an autocorrelation-based measure of tempo and
This approach works well on vocal and non-percussive music.
Furthermore it achieves good performance without complex
pre-processing, operating directly on the absolute value of a
1Khz-sampled digital audio signal. I will present simulation results
for tempo tracking and for meter prediction. I will conclude by
observing that the model performs a particularly useful
dimensionality reduction on digital audio and can perhaps aid in more
complex musical learning tasks such as automatic music composition.
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