Method and apparatus for designing and testing audio codec by using white noise modeling

A neural network-based audio codec design apparatus comprising:

• an audio codec training device configured to generate a quantized latent vector and a reconstructed signal corresponding to an input signal by using a white noise modeling-based quantization process, compute a total loss for training of a neural network-based audio codec, based on the input signal, the reconstructed signal, and the quantized latent vector, and train the neural network-based audio codec by using the total loss;
• and
• an audio codec validation device configured to validate the trained neural network-based audio codec to select the best neural network-based audio codec,
• wherein the audio codec training device is configured to calculate a reconstruction loss term as mean squared error (MSE) between the input signal and the reconstructed signal, a bit-rate control loss term as an entropy of the quantized latent vector, and a perceptual loss term reflecting human perceptual characteristics, and calculate the total loss by adding the reconstruction loss term, the bit-rate control loss term, and the perceptual loss term,
• wherein the reconstruction loss term is determined based on a square of an L2-norm of a difference between the input signal and the reconstructed signal,
• wherein the bit-rate control loss term is determined based on probability distribution for a latent vector with added random noise,
• wherein the latent vector with added random noise is generated by adding a random noise to a latent vector output from an encoder of the neural network-based audio codec that receives the input signal, and
• wherein the quantized latent vector is generated by de-warping the latent vector with added random noise.