Audio-Maestro: Modular Audio Reasoning

• Audio-Maestro is a tool-augmented framework that integrates external analysis modules to enhance audio-language models for expert audio reasoning.
• It employs a two-phase pipeline to dynamically decide between direct responses and invoking domain-specific tools while fusing timestamped outputs.
• Empirical benchmarks demonstrate consistent accuracy improvements in music, speech, and environmental sound tasks compared to traditional approaches.

Audio-Maestro is a tool-augmented framework for enhancing large audio-LLMs (LALMs) in automated audio reasoning and understanding. Unlike monolithic end-to-end approaches, Audio-Maestro introduces a modular pipeline that integrates external, domain-specific analysis tools to process audio input, inject structured and timestamped outputs into the reasoning flow, and derive more accurate, interpretable results. This paradigm has demonstrated consistent gains in expert-level audio reasoning benchmarks across music, speech, and environmental sound domains (Lee et al., 13 Oct 2025).

1. Architectural Principles and Design

Audio-Maestro operates as a two-phase pipeline:

1. Decision-Making: The LALM receives an audio input ($x_{\text{audio}}$), a textual query ($q$), and a toolkit $\mathcal{T}=\{t_1, ..., t_K\}$. The model decides between direct answering (Ans) or invoking one or more tools (`C$)$:

$$a_{\text{decision}} = \mathcal{M}_{\text{lalm}}(x_{\text{audio}}, q, \mathcal{T}) \in \{\text{Ans}, C\}$$

1. Tool Invocation and Integration: If tool calling is required, each selected tool $t_k$ analyzes the audio input and produces a structured, timestamped output ($y_k$), typically in JSON:

$y_k = t_k(x_{\text{audio}})$

These outputs are concatenated with the original input context:

$$c_{\text{aug}} = \text{Concat}(x_{\text{audio}}, q, y_1, ..., y_{|\mathcal{T}_{\text{sel}}|})$$

The LALM uses this enriched context to produce the final answer:

$r = \mathcal{M}_{\text{lalm}}(c_{\text{aug}})$

Tools are not hard-coded; instead, tool selection and output parsing leverage structured prompts, enabling extensibility across diverse tasks.

2. Tool-Augmented Reasoning Process

Audio-Maestro allows the LALM to offload specialized signal-processing or knowledge extraction tasks to external modules whenever the query context requires detailed, expert-level audio analysis. Tool categories include:

• Speech Recognition (e.g., Whisper-large-v3)
• Emotion Recognition (e.g., emotion2vec_plus_large)
• Speaker Diarization (e.g., pyannote)
• Chord and Melody Analysis (e.g., autochord)
• Generic Sound Classification and other specialized analyzers

Each tool yields structured semantic or acoustic features with precise temporal localization. These outputs, typically formatted as:

{ "timestamp": [0.52, 4.18], "value": "C Major" }

are serially fused into the LALM’s reasoning context, enabling hybrid symbolic and signal-level inference.

The LALM maintains interpretability by tracing which sub-query components trigger tool usage, how outputs are fused, and whether errors stem from tool selection, tool inference, or integration steps.

3. Empirical Performance and Benchmarks

On the Massive Multi-Task Audio Understanding (MMAU) benchmark, Audio-Maestro consistently improves audio reasoning accuracy. Comparative results, observed across several models and task domains, are summarized below (averaged over test splits):

Model	Baseline Accuracy	Audio-Maestro Accuracy	Relative Gain (%)
Gemini-2.5-flash	67.4	72.1	+7.0
DeSTA-2.5	58.3	62.8	+7.7
GPT-4o	60.8	63.9	+5.1

Further breakdowns indicate robust gains in music, speech, and environment sound tasks. Performance tables in (Lee et al., 13 Oct 2025) report consistent accuracy improvements when tool outputs are integrated, surpassing previous LALM-only approaches.

4. Technical Implementation Details

The core implementation centers around interfaces for:

• Structured Prompting: The LALM is prompted to decide tool invocation dynamically based on input context, outputting tool-calling policies without explicit scripting.
• Timestamped Output Serialization: Tool outputs are standardized (usually JSON with timestamps), enabling reliable alignment and integration into text-based context for further reasoning.
• Context Fusion Algorithm: The concatenation mechanism ($\text{Concat}$) supports variable numbers and types of tools, with flexible merging of symbolic outputs and acoustic descriptors.

The approach is fundamentally modular: new tools can be added, replaced, or updated independently, without retraining the LALM's core weights.

5. Innovations and Distinctive Contributions

Audio-Maestro establishes the first framework where large audio-LLMs systematically leverage structured, tool-derived outputs during inference. Key contributions include:

• Interpretability: By decomposing complex queries and explicitly calling tools, developers can audit system decisions and trace inaccuracies to specific tool outputs or integration logic.
• Extensibility: The toolkit is defined by prompts and output schemas, not fixed model architectures, enabling rapid adaptation to new analysis domains and tool upgrades.
• Bridging Symbolic and Signal Knowledge: The pipeline systematically grounds high-level semantic reasoning in precise, time-localized acoustic observations—critical for expert musicology, audio forensics, and advanced MIR tasks.

6. Limitations and Prospective Directions

Current limitations stem from:

• Tool Dependency: Quality and accuracy are fundamentally bounded by external tool performance. Inaccuracies in signal analysis propagate into final answers.
• Latency and Throughput: External tool invocation introduces additional inference time, which may impede real-time applications.
• Integration Complexity: Structured output fusion, especially with complex queries and multi-tool output, may introduce challenges in modeling dependencies, alignment, or semantic consistency.

Future research is oriented toward:

• Improved Tool Robustness: Upgrading underlying MIR tools and integrating ensemble approaches can reduce error propagation.
• Real-Time Optimization: Streamlining tool invocation pathways and pipeline parallelism for bounded-latency applications.
• Policy Learning for Tool Invocation: Reinforcement learning or supervised fine-tuning of tool-calling policies to optimize reasoning efficiency and accuracy.
• Expanded Modalities: Fusing tool-augmented reasoning over not only audio but also video/sensor data for multi-modal understanding.

7. Broader Impact and Significance

Audio-Maestro sets a precedent in audio-LLM development by embedding tool-augmented reasoning and structured output fusion into the standard inference regime. The observed performance gains across diverse models and tasks directly support its utility for general and expert-level audio analysis, with applications spanning automated musicology, speech analytics, sound event identification, and cross-domain multimedia reasoning.

A plausible implication is that with continued tool quality improvements and integration research, tool-augmented reasoning could become a standard paradigm for interpretable, accurate multi-modal analysis not only in audio understanding but also in related fields where signal processing and semantic inference intersect.