Year: 2016
Type: article
Abstract: With the growing number of digital music, the automatic genre recognition problem has been receiving the spotlight in music retrieval information field.A large number of musical acoustic features are reported to degrade the genre classification performance and lead to heavy computational cost.In this paper, we propose a new method for selecting genre-discriminative feature subset from a large number of musical features.We show that the proposed method is able to improve the genre recognition accuracy compared to the traditional selection method.
Authors: Jihae Yoon, Hyunki Lim, Dae-Won Kim
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2736779372