@inproceedings{salimiCSMC2020-virtual-listeners-drums,
 abstract = {Can we generate drum synthesizers automatically? We present an approach for the automatic generation of synthesizer programs for one-shot percussive sounds. Recent advancements in digital synthesis, heuristic search, and neural networks can be utilized for sound generation. Yet the need for data, the problem of open set recognition, and high computational costs persist as barriers towards the expansion of sound libraries using these techniques. We generate quick, scalable, percussion synthesizers using classical signal processing. We train drum classifiers to find and classify synthesizer programs that mimic percussive sounds. We use features from Fourier transformations and autoencoder embeddings to train machine learning classifiers. Manual listening tests of the generated sounds demonstrates the system can successfully generate drum synthesizers and categorize drum sounds. To facilitate future research, we share our curated dataset of free percussive sounds.},
 accepted = {2020-09-17},
 author = {Amir Salimi and Abram Hindle},
 authors = {Amir Salimi and Abram Hindle},
 booktitle = {Proceedings of the 2020 AI Music Creativity Conference, 2020},
 code = {salimiCSMC2020-virtual-listeners-drums},
 date = {2020-10-21},
 funding = {NSERC Discovery},
 isbn = {978-91-519-5560-5},
 location = {Stockholm, Sweden},
 pagerange = {1--8},
 pages = {1--8},
 rate = {},
 role = {Co-Author},
 title = {Make your own audience: virtual listeners can filter generated drum programs},
 type = {inproceedings},
 url = {http://softwareprocess.ca/pubs/salimiCSMC2020-virtual-listeners-drums.pdf},
 venue = {Proceedings of the 2020 AI Music Creativity Conference, 2020},
 year = {2020}
}