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Sample-level sound synthesis with recurrent neural networks and conceptors
Conceptors are a recent development in the field of reservoir computing; they can be used to influence the dynamics of recurrent neural networks (RNNs), enabling generation of arbitrary patterns based on training data. Conceptors allow interpolation and extrapolation between patterns, and also provi...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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PeerJ Inc.
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924416/ https://www.ncbi.nlm.nih.gov/pubmed/33816858 http://dx.doi.org/10.7717/peerj-cs.205 |
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author | Kiefer, Chris |
author_facet | Kiefer, Chris |
author_sort | Kiefer, Chris |
collection | PubMed |
description | Conceptors are a recent development in the field of reservoir computing; they can be used to influence the dynamics of recurrent neural networks (RNNs), enabling generation of arbitrary patterns based on training data. Conceptors allow interpolation and extrapolation between patterns, and also provide a system of boolean logic for combining patterns together. Generation and manipulation of arbitrary patterns using conceptors has significant potential as a sound synthesis method for applications in computer music but has yet to be explored. Conceptors are untested with the generation of multi-timbre audio patterns, and little testing has been done on scalability to longer patterns required for audio. A novel method of sound synthesis based on conceptors is introduced. Conceptular Synthesis is based on granular synthesis; sets of conceptors are trained to recall varying patterns from a single RNN, then a runtime mechanism switches between them, generating short patterns which are recombined into a longer sound. The quality of sound resynthesis using this technique is experimentally evaluated. Conceptor models are shown to resynthesise audio with a comparable quality to a close equivalent technique using echo state networks with stored patterns and output feedback. Conceptor models are also shown to excel in their malleability and potential for creative sound manipulation, in comparison to echo state network models which tend to fail when the same manipulations are applied. Examples are given demonstrating creative sonic possibilities, by exploiting conceptor pattern morphing, boolean conceptor logic and manipulation of RNN dynamics. Limitations of conceptor models are revealed with regards to reproduction quality, and pragmatic limitations are also shown, where rises in computation and memory requirements preclude the use of these models for training with longer sound samples. The techniques presented here represent an initial exploration of the sound synthesis potential of conceptors, demonstrating possible creative applications in sound design; future possibilities and research questions are outlined. |
format | Online Article Text |
id | pubmed-7924416 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79244162021-04-02 Sample-level sound synthesis with recurrent neural networks and conceptors Kiefer, Chris PeerJ Comput Sci Artificial Intelligence Conceptors are a recent development in the field of reservoir computing; they can be used to influence the dynamics of recurrent neural networks (RNNs), enabling generation of arbitrary patterns based on training data. Conceptors allow interpolation and extrapolation between patterns, and also provide a system of boolean logic for combining patterns together. Generation and manipulation of arbitrary patterns using conceptors has significant potential as a sound synthesis method for applications in computer music but has yet to be explored. Conceptors are untested with the generation of multi-timbre audio patterns, and little testing has been done on scalability to longer patterns required for audio. A novel method of sound synthesis based on conceptors is introduced. Conceptular Synthesis is based on granular synthesis; sets of conceptors are trained to recall varying patterns from a single RNN, then a runtime mechanism switches between them, generating short patterns which are recombined into a longer sound. The quality of sound resynthesis using this technique is experimentally evaluated. Conceptor models are shown to resynthesise audio with a comparable quality to a close equivalent technique using echo state networks with stored patterns and output feedback. Conceptor models are also shown to excel in their malleability and potential for creative sound manipulation, in comparison to echo state network models which tend to fail when the same manipulations are applied. Examples are given demonstrating creative sonic possibilities, by exploiting conceptor pattern morphing, boolean conceptor logic and manipulation of RNN dynamics. Limitations of conceptor models are revealed with regards to reproduction quality, and pragmatic limitations are also shown, where rises in computation and memory requirements preclude the use of these models for training with longer sound samples. The techniques presented here represent an initial exploration of the sound synthesis potential of conceptors, demonstrating possible creative applications in sound design; future possibilities and research questions are outlined. PeerJ Inc. 2019-07-08 /pmc/articles/PMC7924416/ /pubmed/33816858 http://dx.doi.org/10.7717/peerj-cs.205 Text en ©2019 Kiefer http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Kiefer, Chris Sample-level sound synthesis with recurrent neural networks and conceptors |
title | Sample-level sound synthesis with recurrent neural networks and conceptors |
title_full | Sample-level sound synthesis with recurrent neural networks and conceptors |
title_fullStr | Sample-level sound synthesis with recurrent neural networks and conceptors |
title_full_unstemmed | Sample-level sound synthesis with recurrent neural networks and conceptors |
title_short | Sample-level sound synthesis with recurrent neural networks and conceptors |
title_sort | sample-level sound synthesis with recurrent neural networks and conceptors |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924416/ https://www.ncbi.nlm.nih.gov/pubmed/33816858 http://dx.doi.org/10.7717/peerj-cs.205 |
work_keys_str_mv | AT kieferchris samplelevelsoundsynthesiswithrecurrentneuralnetworksandconceptors |