5 accepted papers
This paper presents a causal speech enhancement (SE) model based on a complex two-branch nested U-Net architecture (CNUNet-TB) combined with a two-stage (TS) training method that leverages speech embeddings from a large self-supervised speech representation learning (SRL) model. The proposed archite…
This paper proposes a two-dimensional (2D) deep neural network (DNN) model for the electrocardiogram (ECG) abnormality classification, which effectively utilizes the inter and intra-lead information comprised in the 12-lead ECG. The proposed model is designed using a stack of residual U-shaped (ResU…
This study investigates the implication of utilizing the psychoacoustic model (PAM) within the neural audio coder (NAC), specifically focusing on the masking of quantization noise. We introduce a novel training strategy to incorporate the PAM into the NAC more accurately. This method involves a disc…
This paper proposes an end-to-end neural audio coder based on a mean-scale hyperprior model together with a perceptual optimization using a psychoacoustic model (PAM)-based loss function. The proposed coder estimates the mean and scale hyperpriors using a sub-network after assuming that the probabil…
A new end-to-end audio coder based on a deep neural network (DNN) is proposed. To compensate for the perceptual distortion that occurred by quantization, the proposed coder is optimized to minimize distortions in both signal and perceptual domains. The distortion in the perceptual domain is measured…