  This paper introduces HAAQI-Net, a non-intrusive deep learning model for
music audio quality assessment tailored for hearing aid users. Unlike
traditional methods like the Hearing Aid Audio Quality Index (HAAQI), which
rely on intrusive comparisons to a reference signal, HAAQI-Net offers a more
accessible and efficient alternative. Using a bidirectional Long Short-Term
Memory (BLSTM) architecture with attention mechanisms and features from the
pre-trained BEATs model, HAAQI-Net predicts HAAQI scores directly from music
audio clips and hearing loss patterns. Results show HAAQI-Net's effectiveness,
with predicted scores achieving 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, reducing inference time from 62.52 seconds to
2.54 seconds. Although effective, feature extraction via the large BEATs model
incurs computational overhead. To address this, a knowledge distillation
strategy creates a student distillBEATs model, distilling information from the
teacher BEATs model during HAAQI-Net training, reducing required parameters.
The distilled HAAQI-Net maintains strong performance with an LCC of 0.9071, an
SRCC of 0.9307, and an MSE of 0.0091, while reducing parameters by 75.85% and
inference time by 96.46%. This reduction enhances HAAQI-Net's efficiency and
scalability, making it viable for real-world music audio quality assessment in
hearing aid settings. This work also opens avenues for further research into
optimizing deep learning models for specific applications, contributing to
audio signal processing and quality assessment by providing insights into
developing efficient and accurate models for practical applications in hearing
aid technology.
