On the Application of Generic Summarization Algorithms to Music
This work applies known methods to a new domain (music), which is incremental.
The paper applied existing generic summarization algorithms (MMR, LexRank, LSA) to music and showed they improve classification performance for a Fado genre classifier on two datasets.
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.