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ARXIV:2603.09853 · AUDIO UNDERSTANDING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.09853AUDIO UNDERSTANDINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
SCENEBench is a benchmark suite designed to evaluate audio understanding across various real-world categories, addressing gaps in current audio processing models.
Opportunity summary
Pain SCENEBench is a benchmark suite designed to evaluate audio understanding across various real-world categories, addressing gaps in current audio processing models.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
SCENEBench is a benchmark suite designed to evaluate audio understanding across various real-world categories, addressing gaps in current audio processing models. However, minimal work has been done to measure audio understanding beyond automatic speech…
Advances in large language models (LLMs) have enabled significant capabilities in audio processing, resulting in state-of-the-art models now known as Large Audio Language Models (LALMs). However, minimal work has been done to measure audio…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. These results provide direction for targeted improvements in model capabilities.
Audio Understanding moved forward this cycle; last verified April 2026. Public score 4.0/10.
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SCENEBench is a benchmark suite designed to evaluate audio understanding across various real-world categories, addressing gaps in current audio processing models.
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10.48550/arXiv.2603.09853SCENEBench is a benchmark suite designed to evaluate audio understanding across various real-world categories, addressing gaps in current audio processing models.
Abstract
Advances in large language models (LLMs) have enabled significant capabilities in audio processing, resulting in state-of-the-art models now known as Large Audio Language Models (LALMs). However, minimal work has been done to measure audio understanding beyond automatic speech recognition (ASR). This paper closes that gap by proposing a benchmark suite, SCENEBench (Spatial, Cross-lingual, Environmental, Non-speech Evaluation), that targets a broad form of audio comprehension across four real-world categories: background sound understanding, noise localization, cross-linguistic speech understanding, and vocal characterizer recognition. These four categories are selected based on understudied needs from accessibility technology and industrial noise monitoring. In addition to performance, we also measure model latency. The purpose of this benchmark suite is to assess audio beyond just what words are said - rather, how they are said and the non-speech components of the audio. Because our audio samples are synthetically constructed (e.g., by overlaying two natural audio samples), we further validate our benchmark against 20 natural audio items per task, sub-sampled from existing datasets to match our task criteria, to assess ecological validity. We assess five state-of-the-art LALMs and find critical gaps: performance varies across tasks, with some tasks performing below random chance and others achieving high accuracy. These results provide direction for targeted improvements in model capabilities.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 4.0
PROBLEM
SCENEBench is a benchmark suite designed to evaluate audio understanding across various real-world categories, addressing gaps in current audio processing models. However, minimal work has been done to measure audio understanding beyond automatic speech recognition (ASR).
METHOD
Advances in large language models (LLMs) have enabled significant capabilities in audio processing, resulting in state-of-the-art models now known as Large Audio Language Models (LALMs). However, minimal work has been done to measure audio understanding beyond automatic speech r...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. These results provide direction for targeted improvements in model capabilities.
WHY NOW
Audio Understanding moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
SCENEBench is a benchmark suite designed to evaluate audio understanding across various real-world categories, addressing gaps in current audio processing models. However, minimal work has been done to measure audio understanding beyond automatic speech recognition (ASR).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Advances in large language models (LLMs) have enabled significant capabilities in audio processing, resulting in state-of-the-art models now known as Large Audio Language Models (LALMs). However, minimal work has been done to measure audio understanding beyond automatic speech recognition (ASR).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. These results provide direction for targeted improvements in model capabilities.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Audio Understanding moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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SCENEBench is a benchmark suite designed to evaluate audio understanding across various real-world categories, addressing gaps in current audio processing models.
Segment
Audio Understanding
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4.0/10 public viability
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proof status
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