Neuro-MSBG: An End-to-End Neural Model for Hearing Loss Simulation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908458046980096 |
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| author | Yuan, Hui-Guan Zezario, Ryandhimas E. Ahmed, Shafique Wang, Hsin-Min Hua, Kai-Lung Tsao, Yu |
| author_facet | Yuan, Hui-Guan Zezario, Ryandhimas E. Ahmed, Shafique Wang, Hsin-Min Hua, Kai-Lung Tsao, Yu |
| contents | Hearing loss simulation models are essential for hearing aid deployment. However, existing models have high computational complexity and latency, which limits real-time applications and lack direct integration with speech processing systems. To address these issues, we propose Neuro-MSBG, a lightweight end-to-end model with a personalized audiogram encoder for effective time-frequency modeling. Experiments show that Neuro-MSBG supports parallel inference and retains the intelligibility and perceptual quality of the original MSBG, with a Spearman's rank correlation coefficient (SRCC) of 0.9247 for Short-Time Objective Intelligibility (STOI) and 0.8671 for Perceptual Evaluation of Speech Quality (PESQ). Neuro-MSBG reduces simulation runtime by a factor of 46 (from 0.970 seconds to 0.021 seconds for a 1 second input), further demonstrating its efficiency and practicality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15396 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neuro-MSBG: An End-to-End Neural Model for Hearing Loss Simulation Yuan, Hui-Guan Zezario, Ryandhimas E. Ahmed, Shafique Wang, Hsin-Min Hua, Kai-Lung Tsao, Yu Sound Artificial Intelligence Audio and Speech Processing Hearing loss simulation models are essential for hearing aid deployment. However, existing models have high computational complexity and latency, which limits real-time applications and lack direct integration with speech processing systems. To address these issues, we propose Neuro-MSBG, a lightweight end-to-end model with a personalized audiogram encoder for effective time-frequency modeling. Experiments show that Neuro-MSBG supports parallel inference and retains the intelligibility and perceptual quality of the original MSBG, with a Spearman's rank correlation coefficient (SRCC) of 0.9247 for Short-Time Objective Intelligibility (STOI) and 0.8671 for Perceptual Evaluation of Speech Quality (PESQ). Neuro-MSBG reduces simulation runtime by a factor of 46 (from 0.970 seconds to 0.021 seconds for a 1 second input), further demonstrating its efficiency and practicality. |
| title | Neuro-MSBG: An End-to-End Neural Model for Hearing Loss Simulation |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2507.15396 |