Pregled bibliografske jedinice broj: 717089
Intelligent exploration of sound spaces using decision trees and evolutionary approach
Intelligent exploration of sound spaces using decision trees and evolutionary approach // Proceedings ICMC|SMC|2014 / Georgaki, Anastasia ; Kouroupetroglou, Georgios (ur.).
Atena, 2014. str. 1263-1270 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 717089 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Intelligent exploration of sound spaces using decision trees and evolutionary approach
Autori
Kreković, Gordan ; Petrinović, Davor
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings ICMC|SMC|2014
/ Georgaki, Anastasia ; Kouroupetroglou, Georgios - Atena, 2014, 1263-1270
ISBN
978-960-466-137-4
Skup
40th International Computer Music Conference joint with the 11th Sound and Music Computing Conference
Mjesto i datum
Atena, Grčka, 14.09.2014. - 20.09.2014
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
sinteza zvuka; timbralni atributi; interaktivni genetski algoritam; strojno učenje
(sound synthesis; timbral attributes; interactive genetic algorithm; machine learning)
Sažetak
This paper describes Synthbee, an assistive tool for sound design which enables musicians to achieve desired sounds without managing parameters of a sound synthesizer manually. The system allows musicians to specify desired sound characteristics using attributes and explore the space of producible sounds by controlling the interactive evolutionary algorithm extended to take into account specified attributes. Using the interactive evolutionary approach, musicians can recombine and mutate patches towards a satisfactory result. While performing recombination of patches, the algorithm tries to maintain values of synthesis parameters which are relevant for achieving desired sound characteristics. Synthbee thereby enables efficient creation of novel sounds which possess characteristics described by input attributes. The method for finding and maintaining relevant synthesis parameters during an interactive exploration is our original algorithm which uniquely combines machine learning techniques with evolutionary computing. The results of the initial subjective evaluation of Synthbee showed that the users were generally satisfied with generated sounds, but also indicated some opportunities for improvement.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
