On the Application of Generic Summarization Algorithms to Music

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.