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Abstract: Today, many private households as well as broadcasting or lm companies own large collections of digital music plays. These are time series that di er from, e.g., weather reports or stocks market data. The task is normally that of classi cation, not prediction of the next value or recognizing a shape or motif. New methods for extracting features that allow to classify audio data have been developed. However, the development of appropriate feature extraction methods is a tedious e ort,... (Update)
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BibTeX entry: (Update)
Ingo Mierswa and Katharina Morik. Automatic feature extraction for classifying audio data. Machine Learning Journal, 58:127--149, 2005. http://citeseer.ist.psu.edu/mierswa05automatic.html More
@misc{ mierswa05automatic,
author = "I. Mierswa and K. Morik",
title = "Automatic feature extraction for classifying audio data",
text = "Ingo Mierswa and Katharina Morik. Automatic feature extraction for classifying
audio data. Machine Learning Journal, 58:127--149, 2005.",
year = "2005",
url = "citeseer.ist.psu.edu/mierswa05automatic.html" }
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