MuChoMusic: Evaluating Music Understanding in Multimodal Audio-Language Models
This provides a standardized evaluation tool for researchers and developers working on multimodal models in the music domain, though it is incremental as it builds on existing evaluation frameworks.
The authors tackled the challenge of evaluating music understanding in multimodal audio-language models by introducing MuChoMusic, a benchmark with 1,187 multiple-choice questions on 644 music tracks, which revealed pitfalls like over-reliance on language in five open-source models.
Multimodal models that jointly process audio and language hold great promise in audio understanding and are increasingly being adopted in the music domain. By allowing users to query via text and obtain information about a given audio input, these models have the potential to enable a variety of music understanding tasks via language-based interfaces. However, their evaluation poses considerable challenges, and it remains unclear how to effectively assess their ability to correctly interpret music-related inputs with current methods. Motivated by this, we introduce MuChoMusic, a benchmark for evaluating music understanding in multimodal language models focused on audio. MuChoMusic comprises 1,187 multiple-choice questions, all validated by human annotators, on 644 music tracks sourced from two publicly available music datasets, and covering a wide variety of genres. Questions in the benchmark are crafted to assess knowledge and reasoning abilities across several dimensions that cover fundamental musical concepts and their relation to cultural and functional contexts. Through the holistic analysis afforded by the benchmark, we evaluate five open-source models and identify several pitfalls, including an over-reliance on the language modality, pointing to a need for better multimodal integration. Data and code are open-sourced.