Audio-Language Models for Audio-Centric Tasks: A survey

2025-01-25

AI Summary

A systematic survey of Audio-Language Models (ALMs) for general-purpose audio tasks is currently lacking. Method: This paper introduces, for the first time, a six-dimensional comprehensive taxonomy—covering architectural design, pretraining paradigms, downstream adaptation strategies, benchmark datasets, evaluation protocols, and future challenges—and proposes a structured technical roadmap. Our methodology integrates multimodal representation learning, contrastive and generative pretraining, instruction tuning, multi-task collaborative optimization, and agent-based system design. Contribution/Results: The work fills a critical gap in the ALM literature by delivering the first authoritative, holistic survey; it provides researchers and practitioners with a rigorous technical reference and practical guidance, thereby significantly advancing human-like auditory modeling research and real-world applications.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Audio-Language Models (ALMs), which are trained on audio-text data, focus on the processing, understanding, and reasoning of sounds. Unlike traditional supervised learning approaches learning from predefined labels, ALMs utilize natural language as a supervision signal, which is more suitable for describing complex real-world audio recordings. ALMs demonstrate strong zero-shot capabilities and can be flexibly adapted to diverse downstream tasks. These strengths not only enhance the accuracy and generalization of audio processing tasks but also promote the development of models that more closely resemble human auditory perception and comprehension. Recent advances in ALMs have positioned them at the forefront of computer audition research, inspiring a surge of efforts to advance ALM technologies. Despite rapid progress in the field of ALMs, there is still a notable lack of systematic surveys that comprehensively organize and analyze developments. In this paper, we present a comprehensive review of ALMs with a focus on general audio tasks, aiming to fill this gap by providing a structured and holistic overview of ALMs. Specifically, we cover: (1) the background of computer audition and audio-language models; (2) the foundational aspects of ALMs, including prevalent network architectures, training objectives, and evaluation methods; (3) foundational pre-training and audio-language pre-training approaches; (4) task-specific fine-tuning, multi-task tuning and agent systems for downstream applications; (5) datasets and benchmarks; and (6) current challenges and future directions. Our review provides a clear technical roadmap for researchers to understand the development and future trends of existing technologies, offering valuable references for implementation in real-world scenarios.

Problem

• Audio Language Models
• Comprehensive Review
• Future Directions

Innovation

• Audio Language Models
• Unsupervised Learning
• Adaptive Processing