Abstract: Provided is a method and apparatus for designing and testing an audio codec using quantization based on white noise modeling. A neural network-based audio encoder design method includes generating a quantized latent vector and a reconstructed signal corresponding to an input signal by using a white noise modeling-based quantization process, computing a total loss for training a neural network-based audio codec, based on the input signal, the reconstruction signal, and the quantized latent vector, training the neural network-based audio codec by using the total loss, and validating the trained neural network-based audio codec to select the best neural network-based audio codec.

Abstract: An audio signal processing method, which is executed by a processor electronically communicating with a deep neural network within a computing system, may comprise: acquiring, by the processor, an input signal before encoding and an output signal after quantization and decoding; calculating, by the processor, a perceptual global loss for a frame corresponding to the input and the output signals; acquiring, by the processor, a plurality of subframes corresponding to the input and output signals by applying a windowing function to the frame of the input and output signals; calculating, by the processor, perceptual local losses for the plurality of subframes corresponding to the input and output signals; and acquiring, by the processor, multi-time scale perceptual loss based on the perceptual global and local losses.

Abstract: Provided is a method and apparatus for designing and testing an audio codec using quantization based on white noise modeling. A neural network-based audio encoder design method includes generating a quantized latent vector and a reconstructed signal corresponding to an input signal by using a white noise modeling-based quantization process, computing a total loss for training a neural network-based audio codec, based on the input signal, the reconstruction signal, and the quantized latent vector, training the neural network-based audio codec by using the total loss, and validating the trained neural network-based audio codec to select the best neural network-based audio codec.