Data-driven local average room transfer function estimation for multi-point equalization

Abstract

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.