Authors: Theodoros Sotirou, Vassilis Lyberatos, Orfeas Menis Mastromichalakis, Giorgos Stamou
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.
Source: http://arxiv.org/abs/2409.10496v1
