HAAQI-Net: A non-intrusive neural music quality assessment model for hearing aids
CoRR(2024)
摘要
This paper introduces HAAQI-Net, a non-intrusive deep learning model for
music quality assessment tailored to hearing aid users. In contrast to
traditional methods like the Hearing Aid Audio Quality Index (HAAQI), HAAQI-Net
utilizes a Bidirectional Long Short-Term Memory (BLSTM) with attention. It
takes an assessed music sample and a hearing loss pattern as input, generating
a predicted HAAQI score. The model employs the pre-trained Bidirectional
Encoder representation from Audio Transformers (BEATs) for acoustic feature
extraction. Comparing predicted scores with ground truth, HAAQI-Net achieves a
Longitudinal Concordance Correlation (LCC) of 0.9368, Spearman's Rank
Correlation Coefficient (SRCC) of 0.9486, and Mean Squared Error (MSE) of
0.0064. Notably, this high performance comes with a substantial reduction in
inference time: from 62.52 seconds (by HAAQI) to 2.54 seconds (by HAAQI-Net),
serving as an efficient music quality assessment model for hearing aid users.
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