Listen, Think, and Understand (2305.10790v3)

Abstract: The ability of AI systems to perceive and comprehend audio signals is crucial for many applications. Although significant progress has been made in this area since the development of AudioSet, most existing models are designed to map audio inputs to pre-defined, discrete sound label sets. In contrast, humans possess the ability to not only classify sounds into general categories, but also to listen to the finer details of the sounds, explain the reason for the predictions, think about what the sound infers, and understand the scene and what action needs to be taken, if any. Such capabilities beyond perception are not yet present in existing audio models. On the other hand, modern LLMs exhibit emerging reasoning ability but they lack audio perception capabilities. Therefore, we ask the question: can we build a model that has both audio perception and a reasoning ability? In this paper, we propose a new audio foundation model, called LTU (Listen, Think, and Understand). To train LTU, we created a new OpenAQA-5M dataset consisting of 1.9 million closed-ended and 3.7 million open-ended, diverse (audio, question, answer) tuples, and have used an autoregressive training framework with a perception-to-understanding curriculum. LTU demonstrates strong performance and generalization ability on conventional audio tasks such as classification and captioning. More importantly, it exhibits emerging audio reasoning and comprehension abilities that are absent in existing audio models. To the best of our knowledge, LTU is one of the first multimodal LLMs that focus on general audio (rather than just speech) understanding.

Overview of "Listen, Think, and Understand"

The paper "Listen, Think, and Understand" presents a novel approach to audio processing by introducing a multimodal LLM named LTU. The focus of LTU is not merely to categorize audio signals into predefined categories but to advance audio models to the level of human-like listening, reasoning, and understanding. While existing models primarily emphasize the perception aspect of audio by mapping inputs to discrete labels, LTU aims to encompass the broader and more nuanced capabilities beyond mere categorization.

LTU: Architecture and Training Methodology

The authors propose LTU as an innovative integration of an Audio Spectrogram Transformer (AST) with LLaMA, an open-source LLM. This integration is distinctive because it leverages AST for enhanced audio perception while drawing on the reasoning capabilities inherent in LLMs. A novel dataset, OpenAQA-5M, is introduced, consisting of a total of 5.6 million audio-question-answer tuples, with both closed and open-ended questions designed to facilitate the training of LTU.

Strong Numerical Findings and Techniques

LTU achieves notable performance across conventional metrics, such as audio classification and captioning, by outperforming existing models like CLAP on multiple benchmarks with a substantial average relative improvement of 23.6%. It has been demonstrated that LTU can effectively answer open-ended questions about audio, boasting an instruction-following and factual correctness rate of 82.9% in human evaluations.

To train LTU effectively, the authors devised a perception-to-understanding curriculum. This involves progressive training stages starting from basic classification and acoustic feature recognition tasks, advancing towards closed and open-ended question-answering exercises. Such a curriculum ensures that LTU first anchors its performance in accurate perception before advancing to sophisticated reasoning tasks.

Implications: Theoretical and Practical Impact

Practically, implementing a model like LTU could revolutionize fields that rely on nuanced audio interpretation, such as automated customer support, where the ability to understand context and not just content is critical. Theoretically, LTU serves as a bridging model between audio perception and reasoning, advancing our understanding of how to design models that are not domain-specific but instead multipurpose. This lays down an architectural template for future multimodal endeavors.

Future Perspectives on AI Developments

Looking ahead, LTU raises thought-provoking questions on the trajectory of multimodal LLMs. The combination of a high-performance audio perception model with a reasoning-capable LLM highlights a roadmap for future work that can include refining these models to cover more intricate audio scenes, scaling to larger LLMs, or introducing additional modalities such as vision to create robust, fully-rounded AI systems. Additionally, the approach of constructing large-scale datasets like OpenAQA-5M is likely to see further adoption as it reflects a holistic view of audio understanding.

In conclusion, this paper makes significant strides in advancing the capabilities of audio models. By endowing models with enhanced reasoning capabilities, it shifts the paradigm from mere perception to deeper, contextual understanding, thereby addressing some of the longstanding limitations in audio AI.