Tuna C, Zevering A, Prinn AG, Götz P, Walther A, Habets E (2022)
Publication Type: Journal article
Publication year: 2022
Book Volume: 152
Pages Range: 3635-3647
Journal Issue: 6
DOI: 10.1121/10.0016592
Multi-point room equalization (EQ) aims to achieve a desired sound quality within a wider listening area than single-point EQ. However, multi-point EQ necessitates the measurement of multiple room impulse responses at a listener position, which may be a laborious task for an end-user. This article presents a data-driven method that estimates a spatially averaged room transfer function (RTF) from a single-point RTF in the low-frequency region. A deep neural network (DNN) is trained using only simulated RTFs and tested with both simulated and measured RTFs. It is demonstrated that the DNN learns a spatial smoothing operation: notches across the spectrum are smoothed out while the peaks of the single-point RTF are preserved. An EQ framework based on a finite impulse response filter is used to evaluate the room EQ performance. The results show that while not fully reaching the level of multi-point EQ performance, the proposed data-driven local average RTF estimation method generally brings improvement over single-point EQ. (C) 2022 Author(s
APA:
Tuna, C., Zevering, A., Prinn, A.G., Götz, P., Walther, A., & Habets, E. (2022). Data-driven local average room transfer function estimation for multi-point equalization. Journal of the Acoustical Society of America, 152(6), 3635-3647. https://doi.org/10.1121/10.0016592
MLA:
Tuna, Cagdas, et al. "Data-driven local average room transfer function estimation for multi-point equalization." Journal of the Acoustical Society of America 152.6 (2022): 3635-3647.
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