A Novel Model for Emotion Detection with Multilayer Perceptron Neural Network

Muthumari, M.; Akash, V.; PrudhviCharan, K.

doi:10.1109/iciccs53718.2022.9788269

Cited by 4 publications

(3 citation statements)

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“…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].…”

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confidence: 99%

“…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]. Therefore, in this paper, we use log Melspectrogram feature of music audios for music emotion classification.…”

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confidence: 99%

“…The key of music emotion classification lies in the effective feature extraction. Attention mechanism [7] and residual learning [9] are often used to improve feature mining. The main idea of attention mechanism is to introduce a dynamic weighting mechanism in the network that allows the model to perform different weighted calculations based on different parts of the input, thus enabling the network to focus more on important information and ignore irrelevant information [2,10,11].…”

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confidence: 99%

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MFFA: Music Feature Fusion Attention Model for Music Emotion Classification

Chen

Zhang

et al. 2023

Preprint

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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.

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confidence: 99%