Jan. 31, 2024, 3:47 p.m. | Dyah A. M. G. Wisnu Epri Pratiwi Stefano Rini Ryandhimas E. Zezario Hsin-Min Wang Yu Tsao

cs.LG updates on arXiv.org arxiv.org

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 …

assessment attention audio audio quality contrast cs.lg cs.sd deep learning eess.as hearing index long short-term memory memory music paper quality sample

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