On the Application of Generic Summarization Algorithms to Music

Autor:	Raposo, Francisco, Ribeiro, Ricardo, de Matos, David Martins
Rok vydání:	2014
Předmět:	Computer Science - Information Retrieval Computer Science - Learning Computer Science - Sound H.5.5
Zdroj:	IEEE Signal Processing Letters, IEEE, vol. 22, n. 1, January 2015
Druh dokumentu:	Working Paper
DOI:	10.1109/LSP.2014.2347582
Popis:	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. Comment: 12 pages, 1 table; Submitted to IEEE Signal Processing Letters
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1406.4877 Zobrazit plný text záznamu View this record from Arxiv