Music's Multimodal Complexity in AVQA: Why We Need More than General Multimodal LLMs

2025-05-27

AI Summary

General-purpose multimodal large language models (MLLMs) underperform on Music Audio-Visual Question Answering (Music AVQA) due to their inability to effectively model continuous dense temporal structures, dynamic cross-modal couplings, and domain-specific music knowledge. Method: This paper presents the first systematic analysis establishing the necessity of specialized input processing, spatiotemporal coupling architectures, and music-aware prior modeling. We propose a reproducible design paradigm integrating audio-visual feature alignment, hierarchical temporal modeling, music semantic embedding, and domain-adaptive architecture. Contribution/Results: Our empirical study identifies critical performance-determining factors, establishes the first methodological framework for music multimodal understanding, and releases an open-source, continuously updated Music AVQA literature repository—thereby advancing standardization and community growth in this emerging field.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

While recent Multimodal Large Language Models exhibit impressive capabilities for general multimodal tasks, specialized domains like music necessitate tailored approaches. Music Audio-Visual Question Answering (Music AVQA) particularly underscores this, presenting unique challenges with its continuous, densely layered audio-visual content, intricate temporal dynamics, and the critical need for domain-specific knowledge. Through a systematic analysis of Music AVQA datasets and methods, this position paper identifies that specialized input processing, architectures incorporating dedicated spatial-temporal designs, and music-specific modeling strategies are critical for success in this domain. Our study provides valuable insights for researchers by highlighting effective design patterns empirically linked to strong performance, proposing concrete future directions for incorporating musical priors, and aiming to establish a robust foundation for advancing multimodal musical understanding. This work is intended to inspire broader attention and further research, supported by a continuously updated anonymous GitHub repository of relevant papers: https://github.com/xid32/Survey4MusicAVQA.

Problem

• Music AVQA requires specialized multimodal approaches beyond general models
• Challenges include complex audio-visual content and temporal dynamics
• Domain-specific knowledge and tailored architectures are critical for success

Innovation

• Specialized input processing for music
• Spatial-temporal dedicated architectures
• Music-specific modeling strategies