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September 10, 2025PeerJ Computer ScienceOpen Access

Music audio emotion regression using the fusion of convolutional neural networks and bidirectional long short-term memory models

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The model significantly outperformed existing methods in music emotion regression, achieving an R-squared value of 0.845.
By using convolutional neural networks to identify temporal patterns, the model captures the intricate nature of musical data.
Bidirectional long short-term memory networks allow the model to analyze sequences both forwards and backwards, enhancing its effectiveness.
Incorporating a multi-head attention mechanism promotes greater expressiveness and generalizability, making it well-suited for large datasets.

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Implication

This approach combines convolutional neural networks and bidirectional long short-term memory models, improving sentiment prediction in emotional music analysis.

Cite This Study

Qiu et al. (2025) studied this question.

synapsesocial.com/papers/68c1a12d54b1d3bfb60dc215 https://doi.org/10.7717/peerj-cs.3086

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Implication

This approach combines convolutional neural networks and bidirectional long short-term memory models, improving sentiment prediction in emotional music analysis.

Music audio emotion regression using the fusion of convolutional neural networks and bidirectional long short-term memory models

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Discussion

Implication

Cite This Study

Music audio emotion regression using the fusion of convolutional neural networks and bidirectional long short-term memory models

Key Points

Discussion

Implication

Cite This Study

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