Machine-learning-based estimation of reverberation time using room geometry for room effect rendering

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

1 Sitaatiot (Scopus)
265 Lataukset (Pure)


This work presents a machine-learning-based method to estimate the reverberation time of a virtual room for auralization purposes. The models take as input geometric features of the room and output the estimated reverberation time values as function of frequency. The proposed model is trained and evaluated using a novel dataset composed of real-world acoustical measurements of a single room with 832 different configurations of furniture and absorptive materials, for multiple loudspeaker positions. The method achieves a prediction accuracy
of approximately 90% for most frequency bands. Furthermore, when comparing against the Sabine and Eyring methods, the proposed approach exhibits a much higher accuracy, especially at low frequencies.
OtsikkoProceedings of the 23rd International Congress on Acoustics : integrating 4th EAA Euroregio 2019 : 9-13 September 2019 in Aachen, Germany
KustantajaDeutsche Gesellschaft für Akustik
ISBN (painettu)978-3-939296-15-7
TilaJulkaistu - 13 syysk. 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Congress on Acoustics - Aachen, Saksa
Kesto: 9 syysk. 201913 syysk. 2019
Konferenssinumero: 23


ConferenceInternational Congress on Acoustics


Sukella tutkimusaiheisiin 'Machine-learning-based estimation of reverberation time using room geometry for room effect rendering'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä