# MusicLIME: Explainable Multimodal Music Understanding

arXiv: https://arxiv.org/abs/2409.10496 · HTML: https://mlsanity.com/paper/2409.10496/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 17 of 17)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Hybrid Transformers for Music Source Separation](https://mlsanity.com/paper/2211.08553/) (2022)
- [MuLan: A Joint Embedding of Music Audio and Natural Language](https://mlsanity.com/paper/2208.12415/) (2022)
- [Toward Interpretable Music Tagging with Self-Attention](https://mlsanity.com/paper/1906.04972/) (2019)
- [Global Aggregations of Local Explanations for Black Box models](https://mlsanity.com/paper/1907.03039/) (2019)
- [Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions](https://mlsanity.com/paper/2306.05731/) (2023)
- [audioLIME: Listenable Explanations Using Source Separation](https://mlsanity.com/paper/2008.00582/) (2020)
- [Music Mood Detection Based On Audio And Lyrics With Deep Neural Net](https://mlsanity.com/paper/1809.07276/) (2018)
- [Multimodal music information processing and retrieval: survey and future challenges](https://mlsanity.com/paper/1902.05347/) (2019)
- [Concept-Based Techniques for "Musicologist-friendly" Explanations in a Deep Music Classifier](https://mlsanity.com/paper/2208.12485/) (2022)
- [Perceptual Musical Features for Interpretable Audio Tagging](https://mlsanity.com/paper/2312.11234/) (2023)
- [A Model You Can Hear: Audio Identification with Playable Prototypes](https://mlsanity.com/paper/2208.03311/) (2022)
- [Leveraging Pre-Trained Autoencoders for Interpretable Prototype Learning of Music Audio](https://mlsanity.com/paper/2402.09318/) (2024)
- [Semantic Prototypes: Enhancing Transparency Without Black Boxes](https://mlsanity.com/paper/2407.15871/) (2024)
- [Multi-Modality in Music: Predicting Emotion in Music from High-Level Audio Features and Lyrics](https://mlsanity.com/paper/2302.13321/) (2023)

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

- [Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2412.02104/) (2024)
- [Investigating Modality Contribution in Audio LLMs for Music](https://mlsanity.com/paper/2509.20641/) (2025)
- [Semantic-Aware Interpretable Multimodal Music Auto-Tagging](https://mlsanity.com/paper/2505.17233/) (2025)

## Abstract (arXiv)

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.