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.

A Review of Finite Element Methods for Room Acoustics

“…Currently, the FEM can be used to generate training and test data for machine learning approaches in room acoustics. For example, in the context of data-driven methods for room acoustics, Tuna et al [158] used finite element data to train a machine learning approach that improves loudspeaker equalization. The current impetus to make use of machine learning algorithms to realize more efficient models is already starting to have an effect on physical modeling approaches, e.g., physical constraints are being built into cost functions, and neural networks are being used to solve differential equations.…”