On the application of generic summarization algorithms to music
Several generic summarization algorithms were developed in the past and successfully applied in fields such as text and speech summarization. In this paper, we review and apply these algorithms to music. To evaluate their performance, we adopt an extrinsic approach: we compare a Fado genre classifie...
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| Outros Autores: | , |
| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2015
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10071/9338 |
| País: | Portugal |
| Oai: | oai:repositorio.iscte-iul.pt:10071/9338 |
| Resumo: | Several generic summarization algorithms were developed in the past and successfully applied in fields such as text and speech summarization. In this paper, we review and apply these algorithms to music. To evaluate their performance, we adopt an extrinsic approach: we compare a Fado genre classifier's performance using truncated contiguous clips against the summaries extracted with those algorithms on two different datasets. We show that Maximal Marginal Relevance (MMR), LexRank, and Latent Semantic Analysis (LSA) all improve classification performance in both datasets used for testing. |
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