Generating Artificial Reverberation via Genetic Algorithms for Real-Time Applications
We introduce a Virtual Studio Technology (VST) 2 audio effect plugin that performs convolution reverb using synthetic Room Impulse Responses (RIRs) generated via a Genetic Algorithm (GA). The parameters of the plugin include some of those defined under the ISO 3382-1 standard (e.g., reverberation ti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/11/1309 |
| _version_ | 1797547618752004096 |
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| author | Edward Ly Julián Villegas |
| author_facet | Edward Ly Julián Villegas |
| author_sort | Edward Ly |
| collection | DOAJ |
| description | We introduce a Virtual Studio Technology (VST) 2 audio effect plugin that performs convolution reverb using synthetic Room Impulse Responses (RIRs) generated via a Genetic Algorithm (GA). The parameters of the plugin include some of those defined under the ISO 3382-1 standard (e.g., reverberation time, early decay time, and clarity), which are used to determine the fitness values of potential RIRs so that the user has some control over the shape of the resulting RIRs. In the GA, these RIRs are initially generated via a custom Gaussian noise method, and then evolve via truncation selection, random weighted average crossover, and mutation via Gaussian multiplication in order to produce RIRs that resemble real-world, recorded ones. Binaural Room Impulse Responses (BRIRs) can also be generated by assigning two different RIRs to the left and right stereo channels. With the proposed audio effect, new RIRs that represent virtual rooms, some of which may even be impossible to replicate in the physical world, can be generated and stored. Objective evaluation of the GA shows that contradictory combinations of parameter values will produce RIRs with low fitness. Additionally, through subjective evaluation, it was determined that RIRs generated by the GA were still perceptually distinguishable from similar real-world RIRs, but the perceptual differences were reduced when longer execution times were used for generating the RIRs or the unprocessed audio signals were comprised of only speech. |
| first_indexed | 2024-03-10T14:47:34Z |
| format | Article |
| id | doaj.art-3edf49b7167a41c9afe010229c190f64 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T14:47:34Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-3edf49b7167a41c9afe010229c190f642023-11-20T21:15:58ZengMDPI AGEntropy1099-43002020-11-012211130910.3390/e22111309Generating Artificial Reverberation via Genetic Algorithms for Real-Time ApplicationsEdward Ly0Julián Villegas1Computer Arts Laboratory, University of Aizu, Aizu-Wakamatsu 965-0006, JapanComputer Arts Laboratory, University of Aizu, Aizu-Wakamatsu 965-0006, JapanWe introduce a Virtual Studio Technology (VST) 2 audio effect plugin that performs convolution reverb using synthetic Room Impulse Responses (RIRs) generated via a Genetic Algorithm (GA). The parameters of the plugin include some of those defined under the ISO 3382-1 standard (e.g., reverberation time, early decay time, and clarity), which are used to determine the fitness values of potential RIRs so that the user has some control over the shape of the resulting RIRs. In the GA, these RIRs are initially generated via a custom Gaussian noise method, and then evolve via truncation selection, random weighted average crossover, and mutation via Gaussian multiplication in order to produce RIRs that resemble real-world, recorded ones. Binaural Room Impulse Responses (BRIRs) can also be generated by assigning two different RIRs to the left and right stereo channels. With the proposed audio effect, new RIRs that represent virtual rooms, some of which may even be impossible to replicate in the physical world, can be generated and stored. Objective evaluation of the GA shows that contradictory combinations of parameter values will produce RIRs with low fitness. Additionally, through subjective evaluation, it was determined that RIRs generated by the GA were still perceptually distinguishable from similar real-world RIRs, but the perceptual differences were reduced when longer execution times were used for generating the RIRs or the unprocessed audio signals were comprised of only speech.https://www.mdpi.com/1099-4300/22/11/1309convolution reverbgenetic algorithmsimpulse responsesroom acousticssignal processing |
| spellingShingle | Edward Ly Julián Villegas Generating Artificial Reverberation via Genetic Algorithms for Real-Time Applications Entropy convolution reverb genetic algorithms impulse responses room acoustics signal processing |
| title | Generating Artificial Reverberation via Genetic Algorithms for Real-Time Applications |
| title_full | Generating Artificial Reverberation via Genetic Algorithms for Real-Time Applications |
| title_fullStr | Generating Artificial Reverberation via Genetic Algorithms for Real-Time Applications |
| title_full_unstemmed | Generating Artificial Reverberation via Genetic Algorithms for Real-Time Applications |
| title_short | Generating Artificial Reverberation via Genetic Algorithms for Real-Time Applications |
| title_sort | generating artificial reverberation via genetic algorithms for real time applications |
| topic | convolution reverb genetic algorithms impulse responses room acoustics signal processing |
| url | https://www.mdpi.com/1099-4300/22/11/1309 |
| work_keys_str_mv | AT edwardly generatingartificialreverberationviageneticalgorithmsforrealtimeapplications AT julianvillegas generatingartificialreverberationviageneticalgorithmsforrealtimeapplications |