Blind Room Volume Estimation from Single-channel Noisy Speech
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A4 Artikkeli konferenssijulkaisussa
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Date
2019-05-01
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Language
en
Pages
5
231-235
231-235
Series
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
Abstract
Recent work on acoustic parameter estimation indicates that geometric room volume can be useful for modeling the character of an acoustic environment. However, estimating volume from audio signals remains a challenging problem. Here we propose using a convolutional neural network model to estimate the room volume blindly from reverberant single-channel speech signals in the presence of noise. The model is shown to produce estimates within approximately a factor of two to the true value, for rooms ranging in size from small offices to large concert halls.Description
Keywords
Acoustics, Volume measurement, Training, Solid modeling, Noise measurement, Data models, Acoustic measurements, Room acoustics, room size, non-intrusive parameter estimation, signal processing, convolutional neural network
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Citation
Genovese, A F, Gamper, H, Pulkki, V, Raghuvanshi, N & Tashev, I J 2019, Blind Room Volume Estimation from Single-channel Noisy Speech . in 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings ., 8682951, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, pp. 231-235, IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom, 12/05/2019 . https://doi.org/10.1109/ICASSP.2019.8682951
