Audio Time-Scale Modification with Temporal Compressing Networks

Abstract

We proposed a novel approach in the field of time-scale modification on audio signals. While traditional methods use the framing technique, spectral approach uses the short-time Fourier transform to preserve the frequency during temporal stretching. TSM-Net, our neural-network model encodes the raw audio into a high-level latent representation. We call it Neuralgram, in which one vector represents 1024 audio samples. It is inspired by the framing technique but addresses the clipping artifacts. The Neuralgram is a two-dimensional matrix with real values, we can apply some existing image resizing techniques on the Neuralgram and decode it using our neural decoder to obtain the time-scaled audio. Both the encoder and decoder are trained with GANs, which shows fair generalization ability on the scaled Neuralgrams. Our method yields little artifacts and opens a new possibility in the research of modern time-scale modification. The audio samples can be found on https://ernestchu.github.io/tsm-net-demo