Home Uncoditional Generation Music Variations Bandwidth-Extenstion Inpainting Denoising Limitations



Learning to Generate Audio from a Single Short Example

Gal Greshler, Tamar Rott Shaham, Tomer Michaeli

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Models for audio generation are typically trained on hours of recordings. Here, we illustrate that capturing the essence of an audio source is typically possible from as little as a few tens of seconds from a single training signal. Specifically, we present a GAN-based generative model that can be trained on one short audio signal from a ny domain (e.g.speech, music, etc.) and does not require pre-training or any other form of external supervision. Once trained, our model can generate random samples of arbitrary duration that maintain semantic similarity to the training waveform, yet exhibit new compositions of its audio primitives. This enables along line of interesting applications, including generating new jazz improvisations or new a-cappella rap variants based on a single short example, producing coherent modifications to famous songs (e.g.adding a new verse to a Beatles song based solely on the original recording), filling-in of missing parts (inpainting), extending the bandwidth of a speech signal (super-resolution), and enhancing old recordings without access to any clean training example. We show that in all cases, no more than 20 seconds of training audio commonly suffice for our model to achievestate-of-the-art results. This is despite its complete lack of prior knowledge about the nature of audio signals in general.

Catch-A-Waveform: Waveform generation from a single short example
Catch-A-Waveform's Applications

Catch-A-Waveform: Waveform generation from a single short example Catch-A-Waveform's Applications

Unconditional Generation

Unconditional generation of different signal types with arbitrary sizes.
Monophonic Music
Instrument Input (Real) Fake (20 [sec]) Fake (40 [sec]) Fake (60 [sec])
Speech Signals
Input (Real) Fake (20 [sec]) Fake (40 [sec]) Fake (60 [sec])
Ambient Sounds
Input (Real) Fake (20 [sec]) Fake (40 [sec]) Fake (60 [sec])
Effect of Receptive Field
Demonstration of creating speech signals, with different receptive field sizes.
training length [sec] Input (Real) Small Receptive Field
Normal Receptive Field
Large Receptive Field

Music Variations

Training on famous songs, and then creating different variations on these songs.
Name Artist Input (Real) Fake 1 Fake 2

Bandwidth Extension

In this section there are bandwidth extention examles on VCTK [3] dataset, compared to TFiLM [2] model. For each examples there are six signals; In the upper row (left to right): low-resolution version, ground-truth high resolution signal, the signal used for training our model (20-25 [sec]); In the lower row: extended with TFiLM model trained on single speaker (30 [min]), extended with TFiLM model trained on multi speaker (600 [min]), extended with our model.
4Khz -> 16KHz Extensions
8Khz -> 16KHz Extensions
When we perform bandwidth extension for wider-bandwidth inputs, we are able to get much better and clearer results. Here are some examples where the input signals are sampled at 8Khz.


Completing a missing part in a given signal in rock songs. Comparison between GACELA [1] model (trained on 8 [hours]) and ours (trained on 12 [sec]).
Input GT GACELA Ours


In this part, we demonstrate denoising old recording by famous violinist Joseph Joachim. Click on the play/pause button next to each example. You can switch between the noisy and denoised version by clicking the "switch" button.

Denoising Synthesized Noisy Signals

In order to measure the SNR improvements of denoised signals, we took a clean recording of Bach's Adagio played by violinist Hilary Hahn and added to it white noise and old gramophone noise in two noise levels. You can listen to the input and reconstructed sounds, where the SNR levels and noise types are written above the spectrograms.


Here are some examples where our model created signals of degraded quality.
Real (Input) Fake


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    Speeches are taken from Miller Center and American Rhetoric websites.
    Cliparts are taken from Clipart Library website.