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Multi-Modal Song Mood Detection with Deep Learning.

Konstantinos Pyrovolakis1, Paraskevi Tzouveli1, Giorgos Stamou1

  • 1School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece.

Sensors (Basel, Switzerland)
|February 15, 2022
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Summary
This summary is machine-generated.

This study compares single-channel and multi-modal deep learning for music mood detection. Multi-modal approaches analyzing audio and lyrics together show promise for accurate music mood classification.

Keywords:
BERTconvolutional neural networksdeep learningdigital signal processingmood classificationnatural language processingtransfer learning

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Area of Science:

  • Computer Science
  • Music Information Retrieval

Background:

  • The contemporary music industry generates vast amounts of data, necessitating automated management.
  • Automated music mood detection is a key challenge in Music Information Retrieval (MIR).
  • Previous research explored marketing correlations (1990), CNNs for genre/mood (2016), and multi-modal Deep Learning (2018).

Purpose of the Study:

  • To examine and compare single-channel and multi-modal approaches for music mood detection using Deep Learning.
  • To evaluate the effectiveness of utilizing audio signals and lyrics separately versus a unified multi-modal analysis.

Main Methods:

  • Implementation of Deep Learning architectures for music mood classification.
  • Training and evaluation using the MoodyLyrics dataset (2000 songs, 4 mood classes: happy, angry, sad, relaxed).
  • Comparison of models analyzing audio and lyrics independently against a combined multi-modal model.

Main Results:

  • The study investigates the performance of single-channel (audio or lyrics) versus multi-modal (audio and lyrics) Deep Learning models.
  • Comparative analysis of different deep learning architectures for music mood classification is performed.

Conclusions:

  • Multi-modal approaches are investigated for their potential to improve music mood detection accuracy.
  • The research aims to provide a uniform prediction of music mood for diverse applications.