4 accepted papers
Modern deep neural network (DNN)-based audio coding approaches utilize complicated non-linear functions (e.g., convolutional neural networks and non-linear activations), which leads to high complexity and memory usage. However, their decoded audio quality is still not much higher than that of signal…
In this paper, we improve the efficiency of the progressive multi-stage neural audio codec (PR-Codec) by utilizing perceptually motivated training criteria. Although our baseline PR-Codec successfully reconstructs full-band signals by progressively decoding the pre-defined subband signals, transpare…
In this paper, we propose a discriminator design scheme for generative adversarial network-based audio signal generation. Unlike conventional discriminators that take an entire signal as input, our discriminator separates the audio signal into harmonic and percussive components and analyzes each com…
In this paper, we propose an effective multi-stage neural audio coding algorithm that encodes full-band audio signals (up to 20 kHz) using an end-to-end training criterion. By predefining several dyadic subband signals as training targets, we progressively encode input audio signals in each stage su…