NDLI: Time-constrained sequential pattern discovery for music genre classification

Content Provider	IEEE Xplore Digital Library
Author	Jia-Min Ren Jang, J.-S.R.
Copyright Year	2011
Description	Author affiliation: Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan (Jia-Min Ren; Jang, J.-S.R.)
Abstract	Music consists of both local and long-term temporal information. However, for a genre classification task, most of the text categorization based approaches only capture local temporal dependences (e.g. statistics of unigrams and bigrams). In our previous work, we use sequential patterns to capture long-term temporal information from the tokenized sequences of music pieces. In this paper, we propose the use of time-constrained sequential patterns (TSPs) to enhance the mined long-term temporal structures so that these TSPs can fit more closely to the human perception. Experimental results show that the proposed method can discover more temporal structures than statistical language modeling approaches and achieves better recognition accuracy.
Starting Page	173
Ending Page	176
File Size	461972
Page Count	4
File Format	PDF
ISBN	9781457705380
ISSN	15206149
e-ISBN	9781457705397
e-ISBN	9781457705373
DOI	10.1109/ICASSP.2011.5946368
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-22
Publisher Place	Czech Republic
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Hidden Markov models Feature extraction Support vector machines Metals Rhythm music genre classification Time-constrained sequential pattern hidden Markov models temporal structure
Content Type	Text
Resource Type	Article

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