Multimodal Mood Classification Framework for Hindi Songs

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Autore principale: Braja Gopal Patra
Natura: Artículo científico
Lingua:en
Pubblicazione: Instituto Politécnico Nacional 2016
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author Braja Gopal Patra
author_facet Braja Gopal Patra
contents Multimodal Mood Classification Framework for Hindi Songs Braja Gopal Patra Dipankar Das Sivaji Bandyopadhyay Computación audio lyrics Hindi songs mood taxonomy multimodal dataset Music information retrieval is currently an active domain of research. An interesting aspect of music information retrieval involves mood classification. While the Western music captured much attention, research on Indian music was limited and mostly based on audio data. In this work, the authors propose a mood taxonomy and describe the framework for developing a multimodal dataset (audio and lyrics) for Hindi songs. We observed differences in mood for several instances of Hindi songs while annotating the audio of such songs in contrast to their corresponding lyrics. Finally, the mood classification frameworks were developed for Hindi songs and they consist of three different systems based on the features of audio, lyrics and both. The mood classification systems based on audio and lyrics achieved F-measures of 58.2% and 55.1%, respectively whereas the multimodal system (combination of both audio and lyrics) achieved the maximum F-measure of 68.6%. 2016 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61547469020 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.20
format Artículo científico
id redalyc_61547469020
institution Redalyc
language en
publishDate 2016
publisher Instituto Politécnico Nacional
spellingShingle Multimodal Mood Classification Framework for Hindi Songs
Braja Gopal Patra
Computación
audio
lyrics
Hindi songs
mood taxonomy
multimodal dataset
Multimodal Mood Classification Framework for Hindi Songs Braja Gopal Patra Dipankar Das Sivaji Bandyopadhyay Computación audio lyrics Hindi songs mood taxonomy multimodal dataset Music information retrieval is currently an active domain of research. An interesting aspect of music information retrieval involves mood classification. While the Western music captured much attention, research on Indian music was limited and mostly based on audio data. In this work, the authors propose a mood taxonomy and describe the framework for developing a multimodal dataset (audio and lyrics) for Hindi songs. We observed differences in mood for several instances of Hindi songs while annotating the audio of such songs in contrast to their corresponding lyrics. Finally, the mood classification frameworks were developed for Hindi songs and they consist of three different systems based on the features of audio, lyrics and both. The mood classification systems based on audio and lyrics achieved F-measures of 58.2% and 55.1%, respectively whereas the multimodal system (combination of both audio and lyrics) achieved the maximum F-measure of 68.6%. 2016 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61547469020 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.20
title Multimodal Mood Classification Framework for Hindi Songs
topic Computación
audio
lyrics
Hindi songs
mood taxonomy
multimodal dataset
url https://www.redalyc.org/articulo.oa?id=61547469020