Bj?rn Thor J¨®nsson

• J¨®nsson, Bj?rn Thor; Erdem, Cagri & Glette, Kyrre (2024). A System for Sonic Explorations With Evolutionary Algorithms. Journal of The Audio Engineering Society. ISSN 1549-4950. doi: 10.17743/jaes.2022.0137.
• J¨®nsson, Bj?rn Thor; Erdem, Cagri; Fasciani, Stefano & Glette, Kyrre (2024). Towards Sound Innovation Engines Using Pattern-Producing Networks and Audio Graphs. In Johnson, Colin; Rebelo, S¨¦rgio M. & Santos, Iria (Ed.), Artificial Intelligence in Music, Sound, Art and Design. Springer Nature. ISSN 9783031569920. doi: 10.1007/978-3-031-56992-0_14. Full text in Research Archive

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• J¨®nsson, Bj?rn Th¨®r (2023). Live Rendering and -Coding Evolved Sounds.
• J¨®nsson, Bj?rn Th¨®r (2023). Jukebox with research data. Show summary

  Evolution runs explorer: opening up access to current results from the application of quality diversity search algorithms to the discovery of synthesised sounds. https://synth.is/exploring-evoruns

• J¨®nsson, Bj?rn Th¨®r (2023). kromosynth CLI. Show summary

  A command line interface to kromosynth and gRPC service.

• J¨®nsson, Bj?rn Th¨®r (2023). kromosynth. Show summary

  Sonic design with evolutionary algorithms: The engine behind synth.is and kromosynth-cli for audio waveform breeding with neuro-evolution of pattern producing networks and quality diversity search.

• J¨®nsson, Bj?rn Th¨®r (2023). Live Streams From Evolutionary Search for Sounds. Show summary

  Here we present a web interface for navigating sounds discovered during runs of evolutionary processes. Those runs are performed as a part of investigations into the applicability of quality diversity search for sounds. This audible peek into the collected data supplements statistical analysis. Such a way of communicating the current results is intended to provide an engaging experience of the data. By either listening to automatic playback of the discovered sounds, or interacting with them, for example by changing their parameters, interesting, annoying, pleasing, and perhaps useful artefacts may be discovered, modified and downloaded for use in any creative work. The application can be accessed from desktops or mobile devices at: https: //synth.is/exploring-evoruns

• J¨®nsson, Bj?rn Thor (2022). Embodiment of audio evolution.
• J¨®nsson, Bj?rn Th¨®r; Hoover, Amy K. & Risi, Sebastian (2015). Interactively Evolving Compositional Sound Synthesis Networks. Show summary

  While the success of electronic music often relies on the uniqueness and quality of selected timbres, many musicians struggle with complicated and expensive equipment and techniques to create their desired sounds. Instead, this paper presents a technique for producing novel timbres that are evolved by the musician through interactive evolutionary computation. Each timbre is produced by an oscillator, which is represented by a special type of artificial neural network (ANN) called a compositional pattern producing network (CPPN). While traditional ANNs compute only sigmoid functions at their hidden nodes, CPPNs can theoretically compute any function and can build on those present in traditional synthesizers (e.g. square, sawtooth, triangle, and sine waves functions) to produce completely novel timbres. Evolved with NeuroEvolution of Augmenting Topologies (NEAT), the aim of this paper is to explore the space of potential sounds that can be generated through such compositional sound synthesis networks (CSSNs). To study the effect of evolution on subjective appreciation, participants in a listener study ranked evolved timbres by personal preference, resulting in preferences skewed toward the first and last generations. In the long run, the CSSN¡¯s ability to generate a variety of different and rich timbre opens up the intriguing possibility of evolving a complete CSSN-encoded synthesizer.

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