Affine Modulation-based Audiogram Fusion Network for Joint Noise Reduction and Hearing Loss Compensation

Hearing aids (HAs) are widely used to provide personalized speech enhancement (PSE) services, improving the quality of life for individuals with hearing loss. However, HA performance significantly declines in noisy environments as it treats noise reduction (NR) and hearing loss compensation (HLC) as separate tasks. This separation leads to a lack of systematic optimization, overlooking the interactions between these two critical tasks, and increases the system complexity. To address these challenges, we propose a novel audiogram fusion network, named AFN-HearNet, which simultaneously tackles the NR and HLC tasks by fusing cross-domain audiogram and spectrum features. We propose an audiogram-specific encoder that transforms the sparse audiogram profile into a deep representation, addressing the alignment problem of cross-domain features prior to fusion. To incorporate the interactions between NR and HLC tasks, we propose the affine modulation-based audiogram fusion frequency-temporal Conformer that adaptively fuses these two features into a unified deep representation for speech reconstruction. Furthermore, we introduce a voice activity detection auxiliary training task to embed speech and non-speech patterns into the unified deep representation implicitly. We conduct comprehensive experiments across multiple datasets to validate the effectiveness of each proposed module. The results indicate that the AFN-HearNet significantly outperforms state-of-the-art in-context fusion joint models regarding key metrics such as HASQI and PESQ, achieving a considerable trade-off between performance and efficiency. The source code and data will be released at https://github.com/deepnetni/AFN-HearNet.

I$^2$S-TFCKD: Intra-Inter Set Knowledge Distillation with Time-Frequency Calibration for Speech Enhancement

In recent years, complexity compression of neural network (NN)-based speech enhancement (SE) models has gradually attracted the attention of researchers, especially in scenarios with limited hardware resources or strict latency requirements. The main difficulties and challenges lie in achieving a balance between complexity and performance according to the characteristics of the task. In this paper, we propose an intra-inter set knowledge distillation (KD) framework with time-frequency calibration (I$^2$S-TFCKD) for SE. Different from previous distillation strategies for SE, the proposed framework fully utilizes the time-frequency differential information of speech while promoting global knowledge flow. Firstly, we propose a multi-layer interactive distillation based on dual-stream time-frequency cross-calibration, which calculates the teacher-student similarity calibration weights in the time and frequency domains respectively and performs cross-weighting, thus enabling refined allocation of distillation contributions across different layers according to speech characteristics. Secondly, we construct a collaborative distillation paradigm for intra-set and inter-set correlations. Within a correlated set, multi-layer teacher-student features are pairwise matched for calibrated distillation. Subsequently, we generate representative features from each correlated set through residual fusion to form the fused feature set that enables inter-set knowledge interaction. The proposed distillation strategy is applied to the dual-path dilated convolutional recurrent network (DPDCRN) that ranked first in the SE track of the L3DAS23 challenge. Objective evaluations demonstrate that the proposed KD strategy consistently and effectively improves the performance of the low-complexity student model and outperforms other distillation schemes.