Ambisynth

Introduction In a previous post I talked about noise profiling . That generated some interest amongst our team members, so I made a web version that is easier to use than the previous command-line version. Links The code for the project is in the Ambisynth Private Repository . I'm not posting a link to the live web-page here because we might try to monetize it later. Features There are several noise profiles available by default. Users may also upload new recordings or corpuses of recordings, and the webpage will analyze them and create a new noise profile. Users can can play back synthesized sounds in real time or download a wave file. The theory of operation is described in more detail in the previous post. Figure 1: screenshot of the noise profile tool Future Work 1) Allow users to control the analysis window size and overlap. 2) Collect several noise profiles for each corpus and either average them, interpolate between them, or collect a distribution from them and

Introduction Adversarial autoencoders have been used to synthesize images, so I thought they might be employed to synthesize sound as well. One obvious difference is that images are static in time, while sound could go on forever, with each new bit of sound depending on all previous sound. This is a difficult property of sound, and to simplify it, I think autoencoders might initially provide a hybrid approach somewhere between granular synthesis and deep learning. My idea is that an auto encoder might be able to generate short windows (grains) of audio that would behave computationally like images, and that could later be assembled via granular synthesis . There might later be a separate machine learning thing that would tell us in what order to assemble the windows (more on this anon). The code described in this post is in the Ambisynth Private Repository Autoencoders Figure 1: Schematic of an autoencoder. A window of audio samples goes in the top, flows through a bottlen