We present a novel architecture for a synthesizer based on an autoencoder that compresses and reconstructs magnitude short time Fourier transform frames. This architecture outperforms previous topologies by using improved regularization, employing several activation functions, creating a focused training corpus, and implementing the Adam learning method. By multiplying gains to the hidden layer, users can alter the autoencoder’s output, which opens up a palette of sounds unavailable to additive/subtractive synthesizers. Furthermore, our architecture can be quickly re-trained on any sound domain, making it flexible for music synthesis applications. Samples of the autoencoder’s outputs can be found at http://soundcloud.com/ann_synth , and the code used to generate and train the autoencoder is open source, hosted at http://github.com/JTColonel/ann_synth.
Authors:
Colonel, Joseph; Curro, Christopher; Keene, Sam
Affiliation:
The Cooper Union for the Advancement of Science and Art, New York, NY, USA
AES Convention:
143 (October 2017)
Paper Number:
9846
Publication Date:
October 8, 2017
Subject:
Signal Processing
Download Now (308 KB)
This paper is Open Access which means you can download it for free.
No AES members have commented on this paper yet.
To be notified of new comments on this paper you can
subscribe to this RSS feed.
Forum users should login to see additional options.
If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.