HAAQI-Net is 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 0.9257, Spearman’s Rank Correlation Coefficient (SRCC) of 0.9394, and Mean Squared Error (MSE) of 0.0080. Notably, this high performance comes with a substantial reduction in inference time: from 62.52 seconds (by HAAQI) to 2.71 seconds (by HAAQI-Net), serving as an efficient music quality assessment model for hearing aid users.
When designing HAAQI-Net, we focused on three key properties that achieve significant improvements over HAAQI:
- Non-intrusive: HAAQI-Net predicts HAAQI scores based on corrupted signals and does not require clean references.
- Computationally Efficient: HAAQI-Net is implemented using a simple neural network, enabling quality predictions to be computed in linear time.
- Differentiable: Implemented as a neural network, HAAQI-Net can be incorporated into the loss function to train deep learning models for upstream tasks.
To utilize HAAQI-Net, refer to the usage example in haaqi_net-example.py. Our best-performing model is available in the 'model' folder, named "best_loss.pth". This model can be employed to predict HAAQI scores.
For the BEATs model, you can download it from BEATs. We specifically use the BEATs_iter3+ (AS2M) version.
For more details and evaluation results, please check out our HAAQI-Net Paper and dataset.