Autoencoding Neural Networks as Musical Audio Synthesizers

• URL: http://arxiv.org/abs/2004.13172v1
• Date: Mon, 27 Apr 2020 20:58:03 GMT
• Title: Autoencoding Neural Networks as Musical Audio Synthesizers
• Authors: Joseph Colonel and Christopher Curro and Sam Keene
• Abstract summary: A method for musical audio synthesis using autoencoding neural networks is proposed. The autoencoder is trained to compress and reconstruct magnitude short-time Fourier transform frames.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A method for musical audio synthesis using autoencoding neural networks is proposed. The autoencoder is trained to compress and reconstruct magnitude short-time Fourier transform frames. The autoencoder produces a spectrogram by activating its smallest hidden layer, and a phase response is calculated using real-time phase gradient heap integration. Taking an inverse short-time Fourier transform produces the audio signal. Our algorithm is light-weight when compared to current state-of-the-art audio-producing machine learning algorithms. We outline our design process, produce metrics, and detail an open-source Python implementation of our model.

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