Music Foundation Model as Generic Booster for Music Downstream Tasks (2411.01135v3)

Abstract: We demonstrate the efficacy of using intermediate representations from a single foundation model to enhance various music downstream tasks. We introduce SoniDo, a music foundation model (MFM) designed to extract hierarchical features from target music samples. By leveraging hierarchical intermediate features, SoniDo constrains the information granularity, leading to improved performance across various downstream tasks including both understanding and generative tasks. We specifically evaluated this approach on representative tasks such as music tagging, music transcription, music source separation, and music mixing. Our results reveal that the features extracted from foundation models provide valuable enhancements in training downstream task models. This highlights the capability of using features extracted from music foundation models as a booster for downstream tasks. Our approach not only benefits existing task-specific models but also supports music downstream tasks constrained by data scarcity. This paves the way for more effective and accessible music processing solutions.

• The paper demonstrates a novel two-stage architecture using an HQ-VAE and an auto-regressive model to capture both coarse and fine musical features.
• The approach significantly improves downstream tasks such as music tagging, transcription, source separation, and mixing with measurable gains in metrics like F1 scores and SDR.
• The paper shows that SoniDo outperforms comparable models like Jukebox and MusicGen, indicating its potential as a resource-efficient booster for diverse music applications.

An Examination of SoniDo: A Music Foundation Model for Boosting Downstream Tasks

The paper titled "Music Foundation Model as Generic Booster for Music Downstream Tasks" introduces a unique approach to enhancing various music-related downstream tasks using a foundation model named SoniDo. This model represents a significant effort to leverage large-scale pre-trained models to improve the performance of task-specific models in music applications such as music tagging, transcription, source separation, and mixing. Unlike previous models which have been primarily developed for language tasks, SoniDo is specifically aimed at music applications, filling a crucial void in the discipline.

The core of SoniDo's architecture features a two-stage model: an HQ-VAE (Hierarchically Quantized Variational Autoencoder) for learning hierarchical feature representations, and an auto-regressive model for generating probabilities from these features. This architecture is designed to decode music input into a combination of coarse and fine features, capturing a broad range of music information across varying levels of abstraction.

Key Contributions and Results

1. Hierarchical Feature Extraction: SoniDo demonstrates a stratified structure where different levels of granularity are captured using hierarchical encoders. This aligns closely with human-perceptible music characteristics, such as rhythm and melody. The hierarchical representation promotes efficient adaptation of these features for a diverse range of tasks.
2. Evaluation on Multiple Tasks: SoniDo's effectiveness was rigorously evaluated across several downstream tasks, demonstrating improvements in music tagging, transcription, source separation, and mixing. The injected features were shown to boost the underlying task-specific models significantly, highlighting their utility as generic performance enhancers.
3. Empirical Performance: The paper reports quantitative performance improvements (e.g., increased F1 scores in transcription and better SDR in source separation) when injecting SoniDo features into task-specific pipelines. Such enhancements were notably significant when dealing with data scarcity, suggesting that SoniDo's features can provide valuable context and structure that improve model predictions with limited data.
4. Comparison with Contemporary Models: When compared to other foundation models like Jukebox and MusicGen, SoniDo's hierarchical approach provided competitive or superior performance in many benchmark tasks, proving that the hierarchical encapsulation of musical features holds promise for broader task applications beyond its original training setup.

Implications and Future Directions

The theoretical implications of SoniDo are manifold. It signifies a transition in the design of music processing systems from narrowly focused models to more universal frameworks capable of accommodating a wide array of tasks using a shared set of features. Such a model can greatly simplify the transfer learning process within the domain of music AI, enabling more accessible music processing solutions.

Practically, SoniDo offers a substantial advancement in music AI. By effectively reusing pre-trained music models, developers can economize resources while enhancing software toolkits used in music production environments. This paper might encourage further research into hierarchical modeling approaches and offer an impetus for developing enhanced auto-regressive models capable of spanning diverse domains, including non-music applications.

Moving forward, it will be essential to expand SoniDo's capabilities to handle non-western music and robust commercial datasets, ensuring that its efficacy holds across global music paradigms. Further refinement of its hierarchical feature extraction could also enable more nuanced understanding and generation of music, aligning model outputs even closer to human interpretations of musical richness and diversity. In anticipating these advancements, SoniDo lays the groundwork for a new era of AI-driven music creativity and analysis.