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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Department of Signal Processing and Acoustics

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Language

en

Pages

5

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44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings, pp. 231-235, 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.

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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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DOI

10.1109/ICASSP.2019.8682951

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

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https://urn.fi/URN:NBN:fi:aalto-201906033327

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[article-cris] Sähkötekniikan korkeakoulu / ELEC