MusicLIME: Explainable Multimodal Music Understanding

Abstract: Multimodal models are critical for music understanding tasks, as they capture the complex interplay between audio and lyrics. However, as these models become more prevalent, the need for explainability grows-understanding how these systems make decisions is vital for ensuring fairness, reducing bias, and fostering trust. In this paper, we introduce MusicLIME, a model-agnostic feature importance explanation method designed for multimodal music models. Unlike traditional unimodal methods, which analyze each modality separately without considering the interaction between them, often leading to incomplete or misleading explanations, MusicLIME reveals how audio and lyrical features interact and contribute to predictions, providing a holistic view of the model's decision-making. Additionally, we enhance local explanations by aggregating them into global explanations, giving users a broader perspective of model behavior. Through this work, we contribute to improving the interpretability of multimodal music models, empowering users to make informed choices, and fostering more equitable, fair, and transparent music understanding systems.

• The paper introduces MusicLIME, a novel framework extending LIME for multimodal music models using audio and lyrical data to explain how different modalities interact in predictions.
• Experiments show multimodal models perform better than unimodal ones, with MusicLIME revealing how audio and lyrics have differential impacts on tasks like genre vs emotion classification.
• MusicLIME has significant implications for improving model design, user trust, and fairness in music information retrieval by providing crucial insights into modality interactions.

Explainable Multimodal Music Understanding: An Overview of MusicLIME

This paper presents MusicLIME, a novel method aimed at enhancing the explainability of multimodal models in music understanding tasks. The framework addresses the interpretability challenges associated with such models by integrating both audio and lyrical data, offering insights into how these modalities interact in the model's decision-making processes. Here, the authors contribute notably to the domain of Explainable AI (XAI) within music information retrieval, ensuring transparency and reducing bias in model predictions.

MusicLIME builds upon the Local Interpretable Model-agnostic Explanations (LIME) framework, widely used for explaining machine learning predictions. The authors adapt this approach for multimodal usage, focusing specifically on models that process both audio and textual data. Traditional unimodal methods provide explanations only for each data type separately, often resulting in incomplete interpretations. In contrast, MusicLIME offers a holistic view of the interactions between modalities, thereby delivering more comprehensive explanations.

The research involves the implementation of a transformer-based multimodal model, combining RoBERTa for text processing with the Audio Spectrogram Transformer (AST) for audio analysis. Such a model serves as the testing ground for evaluating MusicLIME's effectiveness. The study makes use of two datasets, Music4All and a subset crafted from AudioSet, which provide a robust foundation for emotion and genre recognition tasks, both critical in music information retrieval.

Key Findings and Implications

The experimental results confirm the practical utility of MusicLIME. Evaluations indicate that multimodal models outperform unimodal counterparts, leveraging the complementary nature of lyrics and audio. Importantly, MusicLIME can highlight the differential impact of audio and lyrical features on genre versus emotion classification, reinforcing the superiority of multimodal interpretability solutions.

For researchers and practitioners in music information retrieval, MusicLIME heralds profound implications. Its capacity to provide a deeper understanding of the interaction between modalities can lead to better model designs, improved user trust, and the facilitation of more equitable music recommendation systems. Furthermore, this method underscores the necessity of explainable models in AI's advancement, especially given AI's integration into sensitive domains like music where subjective interpretations play a significant role.

Future Directions

Building on these insights, the authors suggest further refining MusicLIME's component methodologies, including data preprocessing and feature encoding strategies. Future work could explore incorporating more context-aware text analysis capabilities, potentially strengthening the interpretability of not only individual words but also broader semantic concepts within song lyrics. The paper also advocates exploring other explanation techniques, such as counterfactual explanations, to enhance the richness of the interpretative insights provided by multimodal models.

In conclusion, MusicLIME represents a significant step forward in the development of transparent and interpretable AI models within the complex domain of music information retrieval. This work encourages ongoing discourse and innovation in XAI, aligning technical advancements with ethical considerations in the AI field.