On the Application of Generic Summarization Algorithms to Music
Francisco Raposo, Ricardo Ribeiro, David Martins de Matos

TL;DR
This paper explores the adaptation of generic text and speech summarization algorithms to music, evaluating their effectiveness through a genre classification task on Fado music datasets.
Contribution
It demonstrates that algorithms like MMR, LexRank, and LSA can enhance music genre classification performance when applied to music summarization.
Findings
MMR, LexRank, and LSA improve classification accuracy
Summarization algorithms are effective for music data
Evaluation used genre classification performance
Abstract
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 this summarization's 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 2 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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