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ICASSP
2011
IEEE

Time-constrained sequential pattern discovery for music genre classification

10 years 1 months ago
Time-constrained sequential pattern discovery for music genre classification
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.
Jia-Min Ren, Jyh-Shing Roger Jang
Added 20 Aug 2011
Updated 20 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Jia-Min Ren, Jyh-Shing Roger Jang
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