MUGEN: Evaluating and Improving Multi-audio Understanding of Large Audio-Language Models

• URL: http://arxiv.org/abs/2603.09714v1
• Date: Tue, 10 Mar 2026 14:22:22 GMT
• Title: MUGEN: Evaluating and Improving Multi-audio Understanding of Large Audio-Language Models
• Abstract summary: Multi-audio understanding is critical for large audio-language models (LALMs)<n>We introduce MUGEN, a benchmark evaluating this capability across speech, general audio, and music.<n>Experiments reveal consistent weaknesses in multi-audio settings, and performance degrades sharply as the number of concurrent audio inputs increases.
• Score: 35.734379373439886
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While multi-audio understanding is critical for large audio-language models (LALMs), it remains underexplored. We introduce MUGEN, a comprehensive benchmark evaluating this capability across speech, general audio, and music. Our experiments reveal consistent weaknesses in multi-audio settings, and performance degrades sharply as the number of concurrent audio inputs increases, identifying input scaling as a fundamental bottleneck. We further investigate training-free strategies and observe that Audio-Permutational Self-Consistency, which diversifies the order of audio candidates, helps models form more robust aggregated predictions, yielding up to 6.28% accuracy gains. Combining this permutation strategy with Chain-of-Thought further improves performance to 6.74%. These results expose blind spots in current LALMs and provide a foundation for evaluating complex auditory comprehension.

Related papers

• SpectCount: Spectrotemporal Counting via Synthetic Signals Improves Large Audio Language Models [11.23346080242434]
  Large audio language models (LALMs) extend large language models with an audio encoder and large-scale audio data.<n>We propose Spectrotemporal Counting (SpectCount), a data-efficient fine-tuning approach based on fully synthetic audio signals generated on-the-fly.
  arXiv  Detail & Related papers  (2026-06-05T04:50:34Z)
• AnyAudio-Judge: A Dynamic Rubric-Based Benchmark and Evaluator for Audio Instruction Following [17.52339026461815]
  We introduce a novel dynamic rubric-based evaluation paradigm that adaptively decomposes complex audio captions into a variable number of independent, verifiable binary rubric items.<n>We benchmark this capability with the AnyAudio-Judge Bench, a comprehensive, bilingual benchmark comprising 7,920 meticulously curated samples.<n>Experiments demonstrate that AnyAudio-Judge not only significantly enhances zero-shot alignment detection compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-06-02T04:00:32Z)
• AudioMosaic: Contrastive Masked Audio Representation Learning [53.52371029884106]
  We introduce textbfAudioMosaic, a contrastive learning-based audio encoder for general audio understanding.<n>AudioMosaic constructs positive pairs by applying structured time-frequency masking to spectrogram patches.<n>Experiments show that AudioMosaic achieves state-of-the-art performance on several standard audio benchmarks.
  arXiv  Detail & Related papers  (2026-05-14T00:56:51Z)
• Thinking with Sound: Audio Chain-of-Thought Enables Multimodal Reasoning in Large Audio-Language Models [49.097347801692166]
  We introduce Thinking-with-Sound (TwS), a framework that equips Large Audio-Language Models with Audio CoT.<n>TwS enables models to actively think with audio signals, performing numerical analysis and digital manipulation through multimodal reasoning.<n>Experiments reveal that state-of-the-art LALMs suffer dramatic performance degradation on MELD-Hard1k, with accuracy dropping by more than $50%$ compared to clean audio.
  arXiv  Detail & Related papers  (2025-09-26T01:27:59Z)
• When Audio and Text Disagree: Revealing Text Bias in Large Audio-Language Models [18.160420407067743]
  MCR-BENCH is the first benchmark designed to evaluate how LALMs prioritize information when presented with inconsistent audio-text pairs.<n>We reveal a concerning phenomenon: when inconsistencies exist between modalities, LALMs display a significant bias toward textual input.<n>This tendency leads to substantial performance degradation in audio-centric tasks and raises important reliability concerns for real-world applications.
  arXiv  Detail & Related papers  (2025-08-21T09:58:24Z)
• AudioJudge: Understanding What Works in Large Audio Model Based Speech Evaluation [55.607230723223346]
  This work presents a systematic study of Large Audio Model (LAM) as a Judge, AudioJudge, investigating whether it can provide a unified evaluation framework that addresses both challenges.<n>We explore AudioJudge across audio characteristic detection tasks, including pronunciation, speaking rate, speaker identification and speech quality, and system-level human preference simulation for automated benchmarking.<n>We introduce a multi-aspect ensemble AudioJudge to enable general-purpose multi-aspect audio evaluation. This method decomposes speech assessment into specialized judges for lexical content, speech quality, and paralinguistic features, achieving up to 0.91 Spearman correlation with human preferences on
  arXiv  Detail & Related papers  (2025-07-17T00:39:18Z)
• Make Some Noise: Towards LLM audio reasoning and generation using sound tokens [19.48089933713418]
  We introduce a novel approach that combines Variational Quantization with Flow Matching to convert audio into ultra-low discrete tokens of 0.23kpbs.<n>Our tokenizer outperforms a traditional VQ-VAE across various datasets with diverse acoustic events.
  arXiv  Detail & Related papers  (2025-03-28T09:43:47Z)
• Can Large Audio-Language Models Truly Hear? Tackling Hallucinations with Multi-Task Assessment and Stepwise Audio Reasoning [55.2480439325792]
  Large audio-language models (LALMs) have shown impressive capabilities in understanding and reasoning about audio and speech information.<n>These models still face challenges, including hallucinating non-existent sound events, misidentifying the order of sound events, and incorrectly attributing sound sources.
  arXiv  Detail & Related papers  (2024-10-21T15:55:27Z)
• Enhancing Audio-Language Models through Self-Supervised Post-Training with Text-Audio Pairs [3.8300818830608345]
  Multi-modal contrastive learning strategies for audio and text have rapidly gained interest.<n>The ability of these models to understand natural language and temporal relations is still a largely unexplored and open field for research.<n>We propose to equip the multi-modal ALMs with temporal understanding without loosing their inherent prior capabilities of audio-language tasks with a temporal instillation method TeminAL.
  arXiv  Detail & Related papers  (2024-08-17T18:53:17Z)
• Multilingual Audio-Visual Speech Recognition with Hybrid CTC/RNN-T Fast Conformer [59.57249127943914]
  We present a multilingual Audio-Visual Speech Recognition model incorporating several enhancements to improve performance and audio noise robustness. We increase the amount of audio-visual training data for six distinct languages, generating automatic transcriptions of unlabelled multilingual datasets. Our proposed model achieves new state-of-the-art performance on the LRS3 dataset, reaching WER of 0.8%.
  arXiv  Detail & Related papers  (2024-03-14T01:16:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.