Abstract
The aim of this study is to evaluate a machine-learning method
in which symbolic representations of folk songs are segmented
and classified into tune families with Haar-wavelet filtering.
The method is compared with previously proposed Gestalt based
method. Melodies are represented as discrete symbolic
pitch-time signals. We apply the continuous wavelet transform
(CWT) with the Haar wavelet at specific scales, obtaining filtered
versions of melodies emphasizing their information at particular
time-scales. We use the filtered signal for representation
and segmentation, using the wavelet coefficients’ local maxima
to indicate local boundaries and classify segments by means of
k-nearest neighbours based on standard vector-metrics (Euclidean,
cityblock), and compare the results to a Gestalt-based segmentation
method and metrics applied directly to the pitch signal.
We found that the wavelet based segmentation and wavelet filtering
of the pitch signal lead to better classification accuracy
in cross-validated evaluation when the time-scale and other parameters
are optimized.
in which symbolic representations of folk songs are segmented
and classified into tune families with Haar-wavelet filtering.
The method is compared with previously proposed Gestalt based
method. Melodies are represented as discrete symbolic
pitch-time signals. We apply the continuous wavelet transform
(CWT) with the Haar wavelet at specific scales, obtaining filtered
versions of melodies emphasizing their information at particular
time-scales. We use the filtered signal for representation
and segmentation, using the wavelet coefficients’ local maxima
to indicate local boundaries and classify segments by means of
k-nearest neighbours based on standard vector-metrics (Euclidean,
cityblock), and compare the results to a Gestalt-based segmentation
method and metrics applied directly to the pitch signal.
We found that the wavelet based segmentation and wavelet filtering
of the pitch signal lead to better classification accuracy
in cross-validated evaluation when the time-scale and other parameters
are optimized.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Third International Workshop on Folk Music Analysis (FMA2013) |
| Number of pages | 7 |
| Publisher | Meertens Institute; Department of Information and Computing Sciences; Utrecht University |
| Publication date | 5 Jun 2013 |
| Pages | 56-62 |
| ISBN (Print) | 978-90-70389-78-9 |
| Publication status | Published - 5 Jun 2013 |
| Event | International Workshop for Folk Music Analysis - Meertens Institute, Amsterdam, Netherlands Duration: 6 Jun 2013 → 7 Jun 2013 Conference number: 3 |
Conference
| Conference | International Workshop for Folk Music Analysis |
|---|---|
| Number | 3 |
| Location | Meertens Institute |
| Country/Territory | Netherlands |
| City | Amsterdam |
| Period | 06/06/2013 → 07/06/2013 |
Keywords
- wavelet-filtering
- continuous wavelet transform
- symbolic music representation
- folk tunes
- segmentation
- classification