6 accepted papers
Deep learning surrogates for 3D Partial Differential Equations (PDEs) often fail to generalize across geometric transformations because they depend heavily on specific coordinate systems. While equivariant networks offer a solution, they typically rely on local operations in the spatial domain, maki…
Controllable Dialogue Generation (CDG) enables chatbots to generate responses with desired attributes, and weighted decoding methods have achieved significant success in the CDG task. However, using a fixed constant value to manage the bias of attribute probabilities makes it challenging to find an
Mesh-based 3D static analysis methods have recently emerged as efficient alternatives to traditional computational numerical solvers, significantly reducing computational costs and runtime for various physics-based analyses. However, these methods primarily focus on surface topology and geometry, of…
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…