# ADFF: Attention Based Deep Feature Fusion Approach for Music Emotion Recognition

arXiv: https://arxiv.org/abs/2204.05649 · HTML: https://mlsanity.com/paper/2204.05649/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2022.

## Built on by (most cited first, 3 of 3)

- [Towards Unified Music Emotion Recognition across Dimensional and Categorical Models](https://mlsanity.com/paper/2502.03979/) (2025)
- [Joint Learning of Emotions in Music and Generalized Sounds](https://mlsanity.com/paper/2408.02009/) (2024)
- [Memo2496: Expert-Annotated Dataset and Dual-view Adaptive Framework for Music Emotion Recognition](https://mlsanity.com/paper/2512.13998/) (2025)

## Abstract (arXiv)

Music emotion recognition (MER), a sub-task of music information retrieval (MIR), has developed rapidly in recent years. However, the learning of affect-salient features remains a challenge. In this paper, we propose an end-to-end attention-based deep feature fusion (ADFF) approach for MER. Only taking log Mel-spectrogram as input, this method uses adapted VGGNet as spatial feature learning module (SFLM) to obtain spatial features across different levels. Then, these features are fed into squeeze-and-excitation (SE) attention-based temporal feature learning module (TFLM) to get multi-level emotion-related spatial-temporal features (ESTFs), which can discriminate emotions well in the final emotion space. In addition, a novel data processing is devised to cut the single-channel input into multi-channel to improve calculative efficiency while ensuring the quality of MER. Experiments show that our proposed method achieves 10.43% and 4.82% relative improvement of valence and arousal respectively on the R2 score compared to the state-of-the-art model, meanwhile, performs better on datasets with distinct scales and in multi-task learning.