End-to-end Topographic Auditory Models Replicate Signatures of Human Auditory Cortex
- URL: http://arxiv.org/abs/2509.24039v1
- Date: Sun, 28 Sep 2025 19:20:30 GMT
- Title: End-to-end Topographic Auditory Models Replicate Signatures of Human Auditory Cortex
- Abstract summary: We show that cortical topography is not present in the previous best-performing models at predicting human auditory fMRI responses.<n>To encourage the emergence of topographic organization, we adapt a cortical wiring-constraint loss originally designed for visual perception.<n>TopoAudio is the first end-to-end biologically grounded auditory model to exhibit emergent topography.
- Score: 6.412156163233532
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The human auditory cortex is topographically organized. Neurons with similar response properties are spatially clustered, forming smooth maps for acoustic features such as frequency in early auditory areas, and modular regions selective for music and speech in higher-order cortex. Yet, evaluations for current computational models of auditory perception do not measure whether such topographic structure is present in a candidate model. Here, we show that cortical topography is not present in the previous best-performing models at predicting human auditory fMRI responses. To encourage the emergence of topographic organization, we adapt a cortical wiring-constraint loss originally designed for visual perception. The new class of topographic auditory models, TopoAudio, are trained to classify speech, and environmental sounds from cochleagram inputs, with an added constraint that nearby units on a 2D cortical sheet develop similar tuning. Despite these additional constraints, TopoAudio achieves high accuracy on benchmark tasks comparable to the unconstrained non-topographic baseline models. Further, TopoAudio predicts the fMRI responses in the brain as well as standard models, but unlike standard models, TopoAudio develops smooth, topographic maps for tonotopy and amplitude modulation (common properties of early auditory representation, as well as clustered response modules for music and speech (higher-order selectivity observed in the human auditory cortex). TopoAudio is the first end-to-end biologically grounded auditory model to exhibit emergent topography, and our results emphasize that a wiring-length constraint can serve as a general-purpose regularization tool to achieve biologically aligned representations.
Related papers
- Structured Phonological Representations for Audio-Articulatory rtMRI Speech Classification [9.697606604686817]
Real-time MRI makes it possible to observe vocal-tract articulation during speech, but mapping these articulatory patterns to phonetic and phonological categories remains challenging.<n>We investigate whether PhonoQ, an audio-based model trained to recognize structured phonological features, provides useful information for audio--articulatory modeling.
arXiv Detail & Related papers (2026-08-10T15:58:07Z) - CoarseSoundNet: Building a reliable model for ecological soundscape analysis [73.44688723989053]
A soundscape is composed of three types of sound: biophony (sounds made by animals), geophony (natural abiotic sounds) and anthropophony (sounds made by humans)
arXiv Detail & Related papers (2026-05-20T13:18:11Z) - WaveNet's Precision in EEG Classification [1.0885910878567457]
This study introduces a WaveNet-based deep learning model designed to automate the classification of EEG signals into physiological, pathological, artifact, and noise categories.<n>The model was trained, validated, and tested on 209,232 samples with a 70/20/10 percent split.<n>WaveNet's architecture, originally developed for raw audio synthesis, is well suited for EEG data due to its use of dilated causal convolutions and residual connections.
arXiv Detail & Related papers (2025-10-10T09:21:21Z) - Thinking While Listening: Simple Test Time Scaling For Audio Classification [61.3564313676731]
We propose a framework that enables neural models to "think while listening" to everyday sounds, thereby enhancing audio classification performance.<n>Motivated by recent advances in the reasoning capabilities of large language models, we address two central questions: (i) how can thinking be incorporated into existing audio classification pipelines to enable reasoning in the category space and improve performance, and (ii) can a new architecture be designed from the ground up to support both thinking and test-time scaling.
arXiv Detail & Related papers (2025-09-24T01:17:24Z) - WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations [67.6147632074449]
We introduce the World-of-Whale benchmark (WoW-Bench) to evaluate low-level auditory perception and cognition using marine mammal vocalizations.<n>WoW-Bench is composed of a Perception benchmark for categorizing novel sounds and a Cognition benchmark, inspired by Bloom's taxonomy, to assess the abilities to remember, understand, apply, and analyze sound events.<n>Experiments with state-of-the-art LALMs show performance far below human levels, indicating a need for stronger auditory grounding in LALMs.
arXiv Detail & Related papers (2025-08-28T16:29:46Z) - Learning Robust Spatial Representations from Binaural Audio through Feature Distillation [64.36563387033921]
We investigate the use of a pretraining stage based on feature distillation to learn a robust spatial representation of speech without the need for data labels.<n>Our experiments demonstrate that the pretrained models show improved performance in noisy and reverberant environments.
arXiv Detail & Related papers (2025-08-28T15:43:15Z) - Foundation Model Hidden Representations for Heart Rate Estimation from Auscultation [3.1379239557375223]
Auscultation, particularly heart sound, is a non-invasive technique that provides essential vital sign information.<n>Recently, self-supervised acoustic representation foundation models (FMs) have been proposed to offer insights into acoustics-based vital signs.
arXiv Detail & Related papers (2025-05-27T05:36:25Z) - Unveiling and Mitigating Bias in Audio Visual Segmentation [9.427676046134374]
Community researchers have developed a range of advanced audio-visual segmentation models to improve the quality of sounding objects' masks.
While masks created by these models may initially appear plausible, they occasionally exhibit anomalies with incorrect grounding logic.
We attribute this to real-world inherent preferences and distributions as a simpler signal for learning than the complex audio-visual grounding.
arXiv Detail & Related papers (2024-07-23T16:55:04Z) - Show from Tell: Audio-Visual Modelling in Clinical Settings [58.88175583465277]
We consider audio-visual modelling in a clinical setting, providing a solution to learn medical representations without human expert annotation.
A simple yet effective multi-modal self-supervised learning framework is proposed for this purpose.
The proposed approach is able to localise anatomical regions of interest during ultrasound imaging, with only speech audio as a reference.
arXiv Detail & Related papers (2023-10-25T08:55:48Z) - Self-supervised models of audio effectively explain human cortical
responses to speech [71.57870452667369]
We capitalize on the progress of self-supervised speech representation learning to create new state-of-the-art models of the human auditory system.
We show that these results show that self-supervised models effectively capture the hierarchy of information relevant to different stages of speech processing in human cortex.
arXiv Detail & Related papers (2022-05-27T22:04:02Z) - Visually Informed Binaural Audio Generation without Binaural Audios [130.80178993441413]
We propose PseudoBinaural, an effective pipeline that is free of recordings.
We leverage spherical harmonic decomposition and head-related impulse response (HRIR) to identify the relationship between spatial locations and received audios.
Our-recording-free pipeline shows great stability in cross-dataset evaluation and achieves comparable performance under subjective preference.
arXiv Detail & Related papers (2021-04-13T13:07:33Z)
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.