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Low Latency Timbre Interpolation and Warping using Autoencoding Neural Networks

A lightweight algorithm for low latency timbre interpolation of two input audio streams using an autoencoding neural network is presented. Short-time Fourier transform magnitude frames of each audio stream are encoded, and a new interpolated representation is created within the autoencoder’s latent space. This new representation is passed to the decoder, which outputs a spectrogram. An initial phase estimation for the new spectrogram is calculated using the original phase of the two audio streams. Inversion to the time domain is done using a Griffin-Lim iteration. A method for avoiding pops between processed batches is discussed. An open source implementation in Python is made available.

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Permalink: http://www.aes.org/e-lib/browse.cfm?elib=20943

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