Audio-Maestro: Enhancing Large Audio-Language Models with Tool-Augmented Reasoning
Abstract
Recent advancements in large multimodal models (LMMs) have shown strong capabilities in audio understanding. However, most systems rely solely on end-to-end reasoning, limiting interpretability and accuracy for tasks that require structured knowledge or specialized signal analysis. In this work, we present Audio-Maestro -- a tool-augmented audio reasoning framework that enables audio-language models to autonomously call external tools and integrate their timestamped outputs into the reasoning process. This design allows the model to analyze, transform, and interpret audio signals through specialized tools rather than relying solely on end-to-end inference. Experiments show that Audio-Maestro consistently improves general audio reasoning performance: Gemini-2.5-flash's average accuracy on MMAU-Test rises from 67.4% to 72.1%, DeSTA-2.5 from 58.3% to 62.8%, and GPT-4o from 60.8% to 63.9%. To our knowledge, Audio-Maestro is the first framework to integrate structured tool output into the large audio language model reasoning process.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
Audio-Maestro introduces a novel framework for tool-augmented reasoning in audio-language models, significantly enhancing their interpretability and accuracy. The comprehensive evaluation of its methodology, experimental results, and broader implications underscores its potential to advance the field of audio understanding.
Methodology
The methodology presented in Audio-Maestro is innovative, focusing on tool-augmented reasoning in the audio domain. The two-phase design allows the model to decide when to invoke external tools, which is a significant advancement over traditional end-to-end models. The integration of structured outputs from tools into the reasoning process is well-articulated, and the decision-making mechanism is grounded in both semantic understanding and acoustic cues, enhancing interpretability.
Results & evaluation
The experiments are robust, utilizing the MMAU benchmark to evaluate the framework's performance across multiple models. The results clearly demonstrate improvements in accuracy when using the tool-augmented approach, with detailed comparisons against baseline models. The analysis of error cases provides valuable insights into the limitations of the current approach, particularly regarding tool output errors.
Reproducibility
The paper provides a GitHub repository for the complete codebase, which is essential for reproducibility. However, the details regarding the implementation of the tools and the exact experimental setup could be more thoroughly documented to facilitate easier replication of the results.
Limitations
The paper acknowledges two main limitations: increased inference time due to tool integration and the dependency on the accuracy of external tools. These factors could hinder real-time applications and indicate areas for future improvement, particularly in enhancing tool robustness.
Broader impact
The implications of this work are significant for various applications in audio processing, including speech recognition, music analysis, and environmental sound understanding. By bridging high-level reasoning with low-level acoustic analysis, Audio-Maestro opens avenues for more interpretable and accurate audio-language models, potentially impacting fields such as human-computer interaction and automated audio analysis. Audio-Maestro introduces a novel framework for tool-augmented reasoning in audio-language models, significantly enhancing their interpretability and accuracy. The comprehensive evaluation of its methodology, experimental results, and broader implications underscores its potential to advance the field of audio understanding.
Author of this paper? Send a correction.