MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector Quantization (2501.01108v1)

Abstract: Recent years have witnessed the success of foundation models pre-trained with self-supervised learning (SSL) in various music informatics understanding tasks, including music tagging, instrument classification, key detection, and more. In this paper, we propose a self-supervised music representation learning model for music understanding. Distinguished from previous studies adopting random projection or existing neural codec, the proposed model, named MuQ, is trained to predict tokens generated by Mel Residual Vector Quantization (Mel-RVQ). Our Mel-RVQ utilizes residual linear projection structure for Mel spectrum quantization to enhance the stability and efficiency of target extraction and lead to better performance. Experiments in a large variety of downstream tasks demonstrate that MuQ outperforms previous self-supervised music representation models with only 0.9K hours of open-source pre-training data. Scaling up the data to over 160K hours and adopting iterative training consistently improve the model performance. To further validate the strength of our model, we present MuQ-MuLan, a joint music-text embedding model based on contrastive learning, which achieves state-of-the-art performance in the zero-shot music tagging task on the MagnaTagATune dataset. Code and checkpoints are open source in https://github.com/tencent-ailab/MuQ.

An Evaluation of MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector Quantization

The exploration of self-supervised learning (SSL) in music informatics has revealed its potential in various tasks, such as music tagging, instrument classification, and key detection. The paper introduces MuQ, an SSL framework specifically designed for music representation learning. The model uniquely employs Mel Residual Vector Quantization (Mel-RVQ) for efficient, stable music representation. This essay will provide a detailed examination of the methodology, empirical results, and potential future contributions to AI-based music processing derived from the proposed MuQ model.

Methodology and Contributions

MuQ innovates by leveraging Mel-RVQ, a pre-trained linear residual vector quantizer, which processes the Mel spectrogram of music audio into discrete tokens. This approach diverges from previous studies that relied on random projection quantizers or neural codecs. Mel-RVQ's lightweight structure is achieved using a single linear layer for quantization, which enhances training efficiency while maintaining high performance. Notably, MuQ benefits from iterative refinement training, where a pre-trained MuQ model's latent features are used as inputs for further RVQ iterations.

Additionally, the MuQ framework has been expanded to MuQ-MuLan, a joint music-text embedding model employing contrastive learning. This extension demonstrates the contextual versatility provided by MuQ in aligning music and text modalities, further bolstering its applicability in music understanding.

Empirical Results

The empirical evaluation of MuQ and its iterative version across multiple downstream tasks from the MARBLE benchmark highlights substantial advancements over prior models like MERT and MusicFM. MuQ, trained with significantly smaller datasets (0.9K to 160K hours), surpasses these models particularly in genre classification, singer identification, and instrument classification tasks, with notable improvements also observed in emotional analysis and key detection.

For instance, the iterative training version, MuQ\textsubscript{iter}, achieves the highest average score across tasks, demonstrating the effectiveness of Mel-RVQ in producing reliable, robust representations without excessive computational demands. Moreover, in the zero-shot music tagging task using the MagnaTagATune dataset, MuQ-MuLan attains state-of-the-art performance, indicating the model's proficiency in music-text alignment.

Discussion and Implications

The success of MuQ in SSL contexts is underscored by the implementation of Mel-RVQ which outperforms traditional tokenizers like Encodec in efficiency and stability while maintaining accuracy. The methodology reflects a balance between computational efficiency and model performance, an asset for handling large-scale music data.

The paper also raises intriguing possibilities for future exploration, such as scaling Mel-RVQ with more codebooks and further investigating virtual classes' role in SSL. These extensions could refine the representation quality and expand the theoretical understanding of SSL frameworks.

Finally, MuQ's framework, being open-source, positions itself as an essential tool for future research and practical applications in music informatics. The potential synergies with other AI domains, such as natural language processing and multimedia analysis, promise exciting avenues for interdisciplinary innovations.

Conclusion

In conclusion, the MuQ framework presents a promising direction in self-supervised music representation learning. Its methodological contributions through Mel-RVQ and empirical validation across multiple tasks set a new benchmark in music informatics. The implications of this research extend beyond music processing to broader AI applications that require nuanced, efficient audiovisual data representations.