Publicação

MERGE App: A Prototype Software for Multi-User Emotion-Aware Music Management

Detalhes bibliográficos
Resumo:	We present a prototype software for multi-user music library management using the perceived emotional content of songs. The tool offers music playback features, song filtering by metadata, and automatic emotion prediction based on arousal and valence, with the possibility of personalizing the predictions by allowing each user to edit these values based on their own emotion assessment. This is an important feature for handling both classification errors and subjectivity issues, which are inherent aspects of emotion perception. A path-based playlist generation function is also implemented. A multi-modal audio-lyrics regression methodology is proposed for emotion prediction, with accompanying validation experiments on the MERGE dataset. The results obtained are promising, showing higher overall performance on train-validate-test splits (73.20% F1-score with the best dataset/split combination).
Autores principais:	Louro, Pedro
Outros Autores:	Branco, Guilherme; Redinho, Hugo; Santos, Ricardo Correia Nascimento Dos; Malheiro, Ricardo; Panda, Renato; Paiva, Rui Pedro
Assunto:	Music Emotion Recognition Machine Learning Deep Learning Software Music Information Retrieval
Ano:	2024
País:	Portugal
Tipo de documento:	documento de conferência
Tipo de acesso:	acesso aberto
Instituição associada:	Instituto Politécnico de Tomar
Idioma:	inglês
Origem:	Instituto Politécnico de Tomar

Descrição
Resumo:	We present a prototype software for multi-user music library management using the perceived emotional content of songs. The tool offers music playback features, song filtering by metadata, and automatic emotion prediction based on arousal and valence, with the possibility of personalizing the predictions by allowing each user to edit these values based on their own emotion assessment. This is an important feature for handling both classification errors and subjectivity issues, which are inherent aspects of emotion perception. A path-based playlist generation function is also implemented. A multi-modal audio-lyrics regression methodology is proposed for emotion prediction, with accompanying validation experiments on the MERGE dataset. The results obtained are promising, showing higher overall performance on train-validate-test splits (73.20% F1-score with the best dataset/split combination).

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