Modulation-scale analysis for content identification

For nonstationary signal classification, e.g., speech or music, features are traditionally extracted from a time-shifted, yet short data window. For many applications, these short-term features do not efficiently capture or represent longer term signal variation. Partially motivated by human auditio...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2004-10, Vol.52 (10), p.3023-3035
Main Authors: Sukittanon, S., Atlas, L.E., Pitton, J.W.
Format: Article
Language:English
Subjects:
Data mining
Distortion
Feature extraction
Frequency
Human
Humans
Information analysis
Modulation
Multiple signal classification
Pattern analysis
Pattern classification
Perception
Psychoacoustics
Signal analysis
Signal classification
Simulation
Speech
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Summary:For nonstationary signal classification, e.g., speech or music, features are traditionally extracted from a time-shifted, yet short data window. For many applications, these short-term features do not efficiently capture or represent longer term signal variation. Partially motivated by human audition, we overcome the deficiencies of short-term features by employing modulation-scale analysis for long-term feature analysis. Our analysis, which uses time-frequency theory integrated with psychoacoustic results on modulation frequency perception, not only contains short-term information about the signals, but also provides long-term information representing patterns of time variation. This paper describes these features and their normalization. We demonstrate the effectiveness of our long-term features over conventional short-term features in content-based audio identification. A simulated study using a large data set, including nearly 10 000 songs and requiring over a billion audio pairwise comparisons, shows that modulation-scale features improves content identification accuracy substantially, especially when time and frequency distortions are imposed.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2004.833861