Neuro-MSBG: An End-to-End Neural Model for Hearing Loss Simulation
This work addresses the need for efficient, real-time hearing loss simulation for hearing aid deployment, representing an incremental improvement over existing models.
The paper tackled the problem of high computational complexity and latency in hearing loss simulation models by proposing Neuro-MSBG, a lightweight end-to-end neural model that reduces simulation runtime by a factor of 46 while maintaining intelligibility and perceptual quality with SRCC scores of 0.9247 for STOI and 0.8671 for PESQ.
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.