A Technique to Detect Music Emotions Based on Machine Learning Classifiers

Unni, Devi; D'Cunha, Aminta Melta; Deepa, G.

doi:10.1109/icps55917.2022.00033

Cited by 5 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In music emotion classification, features are often used alone or combined [3][4][5] in models to obtain emotional information about music audio. Recent works have showed remarkable results using only spectral features for music emotion classification [2,5,7].…”

mentioning

confidence: 99%

See 1 more Smart Citation

MFFA: Music Feature Fusion Attention Model for Music Emotion Classification

Chen

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

Music emotion classification is becoming an important research direction due to its great significance for the music information retrieval (MIR). For the music emotion classification task, how to fully extract related features from the original music audio is the key to improve classification accuracy. In this paper, we propose a music feature fusion attention (MFFA) model to improve the efficiency of mining music emotional features. The proposed model combines a feature fusion attention (FFA) module and a Bi-directional Gated Recurrent Units (BiGRU) module to extract music emotional features from both spatial and temporal dimensions. Firstly, we use the FFA module as a feature extractor, feeding the log Mel-spectrogram of music audio into it, to obtain more comprehensive and effective feature information through multi-scale feature fusion and multi-layer attention mechanisms. At the same time, global residual connection and local residual connection in the FFA are used to learn features in all aspects. The BiGRU module is then used to further capture the temporal relationships of music sequences, and feature concatenation is used to fuse spatial and temporal features. The experimental results show that the proposed model has 1.2%~7.9% improvement over five other baselines. Also, the ablation experiments demonstrate the effectiveness of the combination of FFA module and BiGRU module of the proposed model.

show abstract

mentioning

confidence: 99%

“…However, the abstraction of music emotion makes the task of classifying musical emotions difficult. Therefore, how to construct an effective music emotion classification method has attracted extensive attention [2][3][4][5][6][7].…”

mentioning

confidence: 99%