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Machine Hearing: Can Audio LLMs Generalize Beyond Human Auditory Perception? — arXiv:2611.10693

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.10693
License (as archived): CC0-1.0
Archived: 2026-11-10T18:45:03.232Z
SHA-256 of the archived text: e5202a9be072d8c67d3c115f8f8b87e965ed55fbf84794483d354d6dfc1e586e
Stable URL of this page: https://metraipolis.com/documento/dc_cb243b65249e

How to cite

Machine Hearing: Can Audio LLMs Generalize Beyond Human Auditory Perception? — arXiv:2611.10693 — arXiv (en), https://arxiv.org/abs/2611.10693 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cb243b65249e (Metraipolis archive dc_cb243b65249e, sha256 e5202a9be072d8c67d3c115f8f8b87e965ed55fbf84794483d354d6dfc1e586e)

BibTeX

@misc{dccb243b65249e,
  title = {Machine Hearing: Can Audio LLMs Generalize Beyond Human Auditory Perception? — arXiv:2611.10693},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.10693},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cb243b65249e; sha256 e5202a9be072d8c67d3c115f8f8b87e965ed55fbf84794483d354d6dfc1e586e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.10693. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multimodal LLMs have made remarkable progress in perceiving the physical world, while their perceptual scope remains largely human-centric, focusing on what humans can directly see or hear. This progress raises the prospect of perception beyond human limits, such as extending hearing to sounds that are inaudible to humans. More broadly, the physical world is full of waves, vibrations, and oscillations, only a small fraction of which are directly audible to humans. This invites the extension of multimodal LLMs toward a broader form of machine hearing, which remains largely underexplored. To investigate this direction, we introduce the Machine Hearing Benchmark (MHB), comprising 6,128 instances across 13 tasks and seven domains. MHB spans highly heterogeneous input regimes, with sampling rates from approximately $10^{-5}$ to $10^6$ Hz, sequence lengths from $10^1$ to $10^6$ samples, and both single- and multi-channel inputs. Evaluations of current open-source and proprietary Audio LLMs (ALLMs) reveal limited zero-shot transfer to machine hearing. Supervised adaptation on machine-hearing data alone does not close this gap, suggesting that the challenge extends beyond data exposure to a systematic mismatch between existing audio front ends and machine-hearing inputs. To address this mismatch, we introduce MachEar, a lightweight encoder extension that complements the pretrained Audio LLM for machine-hearing inputs. The results show that MachEar equips a pretrained Audio LLM with stronger machine-hearing capabilities while largely preserving its existing audio understanding performance.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Sound (cs.SD) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2611.10693 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-10T18:37:52.473Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).