HAAQI-Net: A Non-Intrusive Neural Music Audio Quality Assessment Model for Hearing Aids

Abstract

This paper introduces HAAQI-Net, a non-intrusive music audio quality assessment model for hearing aid users. Unlike traditional methods such as Hearing Aid Audio Quality Index (HAAQI), which requires intrusive reference signal comparisons, HAAQI-Net offers a more accessible and computationally efficient alternative. Leveraging a bidirectional long short-term memory architecture with attention mechanisms and features extracted from a pre-trained BEATs model, it can predict HAAQI scores directly from music audio clips and hearing loss patterns. The experimental results demonstrate that, compared to the traditional HAAQI as the reference, HAAQI-Net achieves a linear correlation coefficient (LCC) of 0.9368, a Spearman's rank correlation coefficient (SRCC) of 0.9486, and a mean squared error (MSE) of 0.0064, while significantly reducing the inference time from 62.52 seconds to 2.54 seconds. Furthermore, a knowledge distillation strategy was applied, reducing the parameters by 75.85% and inference time by 96.46%, while maintaining strong performance (LCC: 0.9071, SRCC: 0.9307, MSE: 0.0091). To expand its capabilities, HAAQI-Net was adapted to predict subjective human scores, mean opinion score (MOS), by fine-tuning. This adaptation significantly improved the prediction accuracy. Furthermore, the robustness of HAAQI-Net was evaluated under varying sound pressure level (SPL) conditions, revealing optimal performance at a reference SPL of 65 dB, with the accuracy gradually decreasing as SPL deviated from this point. The advancements in subjective score prediction, SPL robustness, and computational efficiency position HAAQI-Net as a reliable solution for music audio quality assessment, significantly contributing to the development of efficient and accurate models in audio signal processing and hearing aid technology.