An Efficient Neural Network for Modeling Human Auditory Neurograms for Speech
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917032589524992 |
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| author | Zohar, Eylon Nelken, Israel Rafaely, Boaz |
| author_facet | Zohar, Eylon Nelken, Israel Rafaely, Boaz |
| contents | Classical auditory-periphery models, exemplified by Bruce et al., 2018, provide high-fidelity simulations but are stochastic and computationally demanding, limiting large-scale experimentation and low-latency use. Prior neural encoders approximate aspects of the periphery; however, few are explicitly trained to reproduce the deterministic, rate-domain neurogram , hindering like-for-like evaluation. We present a compact convolutional encoder that approximates the Bruce mean-rate pathway and maps audio to a multi-frequency neurogram. We deliberately omit stochastic spiking effects and focus on a deterministic mapping (identical outputs for identical inputs). Using a computationally efficient design, the encoder achieves close correspondence to the reference while significantly reducing computation, enabling efficient modeling and front-end processing for auditory neuroscience and audio signal processing applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19354 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Efficient Neural Network for Modeling Human Auditory Neurograms for Speech Zohar, Eylon Nelken, Israel Rafaely, Boaz Audio and Speech Processing Classical auditory-periphery models, exemplified by Bruce et al., 2018, provide high-fidelity simulations but are stochastic and computationally demanding, limiting large-scale experimentation and low-latency use. Prior neural encoders approximate aspects of the periphery; however, few are explicitly trained to reproduce the deterministic, rate-domain neurogram , hindering like-for-like evaluation. We present a compact convolutional encoder that approximates the Bruce mean-rate pathway and maps audio to a multi-frequency neurogram. We deliberately omit stochastic spiking effects and focus on a deterministic mapping (identical outputs for identical inputs). Using a computationally efficient design, the encoder achieves close correspondence to the reference while significantly reducing computation, enabling efficient modeling and front-end processing for auditory neuroscience and audio signal processing applications. |
| title | An Efficient Neural Network for Modeling Human Auditory Neurograms for Speech |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2510.19354 |