Effective training methods for automatic musical genre classification

Name: Effective training methods for automatic musical genre classification
Author: Atsız, Eren, Albey, Erinç, Kayış, Enis

İsim	Effective training methods for automatic musical genre classification
Yazar	Atsız, Eren, Albey, Erinç, Kayış, Enis
Basım Tarihi:	2019
Basım Yeri	- SciTePress
Konu	Music information retrieval, Genre classification
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-989-758-377-3
Kayıt Numarası	2d74c870-7d00-4ff8-8449-72de4087724d
Lokasyon	Industrial Engineering
Tarih	2019
Örnek Metin	Musical genres are labels created by human and based on mutual characteristics of songs, which are also called musical features. These features are key indicators for the content of the music. Rather than predictions by human decisions, developing an automatic solution for genre classification has been a significant issue over the last decade. In order to have automatic classification for songs, different approaches have been indicated by studying various datasets and part of songs. In this paper, we suggest an alternative genre classification method based on which part of songs have to be used to have a better accuracy level. Wide range of acoustic features are obtained at the end of the analysis and discussed whether using full versions or pieces of songs is better. Both alternatives are implemented and results are compared. The best accuracy level is 55% while considering the full version of songs. Besides, additional analysis for Turkish songs is also performed. All analysis, data, and results are visualized by a dynamic dashboard system, which is created specifically for the study.
Editör	Hammoudi, S., Quix, C., Bernardino, J.
DOI	10.5220/0007933202750280
Cilt	1

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Effective training methods for automatic musical genre classification

Yazar Atsız, Eren, Albey, Erinç, Kayış, Enis

Basım Tarihi 2019

Basım Yeri - SciTePress

Konu Music information retrieval, Genre classification

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-989-758-377-3

Kayıt Numarası 2d74c870-7d00-4ff8-8449-72de4087724d

Lokasyon Industrial Engineering

Tarih 2019

Örnek Metin Musical genres are labels created by human and based on mutual characteristics of songs, which are also called musical features. These features are key indicators for the content of the music. Rather than predictions by human decisions, developing an automatic solution for genre classification has been a significant issue over the last decade. In order to have automatic classification for songs, different approaches have been indicated by studying various datasets and part of songs. In this paper, we suggest an alternative genre classification method based on which part of songs have to be used to have a better accuracy level. Wide range of acoustic features are obtained at the end of the analysis and discussed whether using full versions or pieces of songs is better. Both alternatives are implemented and results are compared. The best accuracy level is 55% while considering the full version of songs. Besides, additional analysis for Turkish songs is also performed. All analysis, data, and results are visualized by a dynamic dashboard system, which is created specifically for the study.

Editör Hammoudi, S., Quix, C., Bernardino, J.

DOI 10.5220/0007933202750280

Cilt 1