Audio Query–Audio Answer (AQAA)

Updated 3 December 2025

Audio Query–Audio Answer (AQAA) is a machine learning paradigm that integrates paired audio and query inputs to produce semantically aligned audio outputs.
It leverages end-to-end token-based models, modular pipelines, and compositional approaches for applications in expressive human-computer interactions and audio scene understanding.
State-of-the-art AQAA systems demonstrate improved performance across speech, music, and environmental domains, paving the way for future research in continuous audio token representations and multimodal reasoning.

Audio Query–Audio Answer (AQAA) defines the class of machine learning systems that perform end-to-end reasoning over paired audio and query inputs to yield responses in the audio modality. The AQAA paradigm subsumes classical Audio Question Answering (AQA)—which typically produces text output—and extends it to include systems generating acoustic, speech-like, or non-linguistic audio answers, leveraging recent advances in large audio-LLMs (LALMs), cross-modal representation learning, and compositional audio reasoning. AQAA is integral to applications in expressive human-computer interaction, audio scene understanding, multi-turn audio dialogue, and grounded reasoning across speech, music, and environmental domains.

1. Technical Foundations and Definitions

Formally, an AQAA system accepts an input tuple (x_\mathrm{audio}, q_\mathrm{text/audio}), where x_\mathrm{audio} is a raw waveform or time-frequency representation, and q_\mathrm{text/audio} is a query rendered in text or audio. The model aims to synthesize an output y_\mathrm{audio} such that y_\mathrm{audio} semantically satisfies the query q conditioned on the acoustic scene described by x_\mathrm{audio}. This output can span spoken language, musical snippets, non-linguistic sound events, or compositional audio constructs (Huang et al., 10 Jun 2025, Abdelnour et al., 2018).

In contrast to legacy AQA, which predicts y_\mathrm{text}, AQAA systems face additional challenges: (a) learning discrete or continuous audio token representations for compact reasoning and synthesis, (b) integrating text and audio context in a unified model context, (c) ensuring semantic and prosodic controllability of the output waveform, and (d) robust evaluation across interaction, control, and audio generation categories.

2. Model Architectures and Compositional Pipelines

AQAA system architectures can be categorized as (i) fully end-to-end token-based models; (ii) multi-expert modular pipelines; (iii) compositional functional program-based approaches; and (iv) hybrid chain-of-thought LALMs with explicit audio-context modeling.

2.1 End-to-End Token-Based Models

"Step-Audio-AQAA" exemplifies a unified, expressive LALM comprising three tightly integrated components: (A) a dual-codebook audio tokenizer, with parallel vector-quantized codebooks for linguistic (16.7 Hz, size=1024, Paraformer-based) and semantic (25 Hz, size=4096, CosyVoice 1.0-based) features, forming an interleaved audio-token stream; (B) a 130B-parameter decoder-only Transformer LLM backbone incorporating text and audio tokens, using group query attention, RMSNorm, and sinusoidal position encoding for text and interleaved positions for audio; (C) a flow-matching neural vocoder (U-Net, 1D-ResNet+Transformer backbone) conditioned solely on generated token sequences, trained with a flow-matching objective plus multi-scale spectrogram consistency losses (Huang et al., 10 Jun 2025).

2.2 Modular and Expert-Based Pipelines

The "Comprehensive Audio Query Handling System" implements intent-driven modularity, routing text-audio queries to specialized expert models (ASR, diarization, music ID, text-to-audio) via a BERT-based intent classifier (overall accuracy=0.85), extracting log-mel spectrograms, and constructing structured JSON metadata (events, segments) for grounding reasoning. Final response synthesis uses a compact 3.8B-parameter LLM (Phi-3.5), consuming extracted metadata, expert model outputs, and chat history, and leveraging chain-of-thought prompting and retrieval-augmented generation (Naveen et al., 5 Dec 2024).

2.3 Compositional Functional and Programmatic Approaches

Datasets such as CLEAR adopt a compositional functional program paradigm, generating synthetic audio scenes from controlled banks of elementary sounds, paired with functional programs for question generation (filter, relate, query attr, count, etc.). Extension to AQAA proceeds by mapping functional program leaves to audio tokens, which are then rendered as spoken TTS bursts or by concatenative synthesis of audio elements indexed by answer tokens (Abdelnour et al., 2018).

3. Datasets and Benchmarks

The development and evaluation of AQAA systems are grounded in a diversity of datasets spanning synthetic, crowdsourced, and domain-general benchmarks.

Dataset	Audio Domain	Question Format	Answer Modality
StepEval-Audio-360 (Huang et al., 10 Jun 2025)	Speech, Multilingual	Expressive Dialogue	Audio (MOS, 1–5)
CoTA (Xie et al., 4 Mar 2025)	Speech, Music, Sound	Structured CoT QA	Text (implied audio possible)
Clotho-AQA (Lipping et al., 2022)	Environmental	Binary/Single-word	Text
CLEAR (Abdelnour et al., 2018)	Synthetic Scenes	Functionally Programmed	Text, extensible to audio
MMAU, AIR-Bench, ACD-{timestamp, temporal}-QA (Naveen et al., 5 Dec 2024, Xie et al., 4 Mar 2025)	Mixed	Classification, Open QA	Text, some audio

"StepEval-Audio-360" tests nine skill categories, ranging from speech emotion control and singing to logical reasoning, language ability, and voice instruction following, with multi-language and dialect coverage, and uses a 1–5 MOS scale from human raters (Huang et al., 10 Jun 2025). "CoTA" introduces reasoning-centric QA with structured chain-of-thought, drawing 1.2M samples across speech, music, and environmental domains (Xie et al., 4 Mar 2025). "Clotho-AQA" focuses on environmental audio, employing LSTM-based multimodal classifiers for binary and 828-class single-word answers (Lipping et al., 2022). The MMAU and AIR-Bench benchmarks are used for comprehensive model comparison and ablation.

4. Training Paradigms and Optimization

AQAA systems employ multi-stage supervised finetuning, explicit preference optimization, structured data augmentation, and model ensembling:

"Step-Audio-AQAA" uses two-stage Supervised Fine-Tuning (first on AQTA + AQTAA, then on high-quality expressive data), followed by token-masked Direct Preference Optimization (DPO) where only non-audio tokens contribute to the preference loss, as well as a "model soup" parameter merge procedure combining SFT and DPO checkpoint weights in a 5:5:1 ratio (Huang et al., 10 Jun 2025).
"Audio-Reasoner" trains using a structured chain-of-thought process: $\mathcal{L}_\text{total} = \lambda_{qa}\mathcal{L}_{qa} + \lambda_{cot}\mathcal{L}_{cot} + \lambda_{cls}\mathcal{L}_{cls}$ , with inference-time temperature sampling and beam search. Ablation studies highlight the necessity of CoT training, synthetic reasoning-heavy samples, and extended context windows for strong performance (Xie et al., 4 Mar 2025).
Modular systems train intent classifiers (BERT, F₁ up to 0.96 per class) and expert models with standard cross-entropy, binary cross-entropy, and contrastive or adversarial losses as relevant (Naveen et al., 5 Dec 2024).

5. Evaluation Methodologies and Results

Evaluation encompasses both intrinsic QA accuracy and perceptual metrics for audio answers. Step-Audio-AQAA demonstrates state-of-the-art performance on StepEval-Audio-360, leading in 7/9 categories:

Skill Category	Step-Audio-AQAA	Kimi-Audio	Qwen-Omni
Speech Emotion	4.53	4.20	4.15
Creativity	4.47	4.11	4.09
Voice Understanding	4.40	4.35	4.33
Singing	3.90	4.05	4.00

On MMAU-mini, "Audio-Reasoner" improves average classification accuracy from 49.20% (Qwen2-Audio-Instruct) to 61.71%. In AIR-Bench "Chat," GPT-4-based scoring shows an average score increase to 7.94 for Audio-Reasoner (Xie et al., 4 Mar 2025). Binary classification in Clotho-AQA (multimodal LSTM baseline) yields 62.7% overall accuracy; multiclass (828-class) achieves 54.2% Top-1, 93.7% Top-5 accuracy (Lipping et al., 2022).

Ablation studies across works confirm that (i) token/audiotext interleaving and marker-preserving concatenation bolster chat and factuality metrics; (ii) inclusion of structured JSON metadata for event grounding in QA increases accuracy by 6–7 percentage points; (iii) chain-of-thought stepwise reasoning is critical for temporally and causally rich queries (Huang et al., 10 Jun 2025, Naveen et al., 5 Dec 2024, Xie et al., 4 Mar 2025).

6. Limitations and Future Research Directions

Several unresolved challenges persist in AQAA:

Expressive singing and high-fidelity music generation lag specialized TTS and music synthesis models (Huang et al., 10 Jun 2025).
Discrete audio tokens may fail to encode subtle acoustic nuances; research into continuous or hybrid VQ-flow schemes is ongoing.
End-to-end unsupervised audio-only token generation, without reliance on text, is an open area.
Existing datasets (e.g., Clotho-AQA, CLEAR) often emphasize single-word or binary answers, and show strong language modeling bias; richer reasoning and grounding are active areas for expansion (Lipping et al., 2022, Abdelnour et al., 2018).
Practical deployment on-device remains constrained by model size/latency: quantization, pruning, and expert model offloading to the cloud are active optimizations (Naveen et al., 5 Dec 2024).

Planned research includes chain-of-thought reasoning directly over audio token streams, extension to non-linguistic or multi-instrumental outputs, and differentiable pipelines enabling joint optimization of comprehension and synthesis. The integration of o1-style reasoning, expanded CoT prompting, and broader coverage of audio scene complexity is also suggested (Huang et al., 10 Jun 2025).

7. Significance and Context in Multimodal AI

AQAA advances the methodological frontier of forced cross-modal grounding—moving from text-grounded QA toward full audio–audio interaction. This transition enables seamless spoken and acoustic dialogue, fine-grained affect and prosody control, and multi-domain reasoning (speech, music, environment) not achievable with pure ASR–LLM–TTS cascades. AQAA stands as a core benchmark for the next generation of large audio-LLMs, challenging them to integrate structured reasoning, multi-modal generation, and intuitive human interaction in a unified, end-to-end pipeline (Huang et al., 10 Jun 2025, Xie et al., 4 Mar 2025, Naveen et al., 5 Dec 2024).