On the application of generic summarization algorithms to music

• Author(s): Raposo, F. ; Ribeiro, R. ; de Matos, D. M.
• Date: 2015
• Persistent ID: http://hdl.handle.net/10071/9338
• Origin: Repositório ISCTE
• Subject(s): Automatic music summarization; Generic summarization algorithms
• Document Type: Journal article
• Language: English

Description

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.