On the Application of Generic Summarization Algorithms to Music
Information Retrieval
2015-03-24 v1 Machine Learning
Sound
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.
Cite
@article{arxiv.1406.4877,
title = {On the Application of Generic Summarization Algorithms to Music},
author = {Francisco Raposo and Ricardo Ribeiro and David Martins de Matos},
journal= {arXiv preprint arXiv:1406.4877},
year = {2015}
}
Comments
12 pages, 1 table; Submitted to IEEE Signal Processing Letters