Neural personal sound zones with flexible bright zone control

TL;DR

This paper introduces a 3D CNN approach for personal sound zones that allows flexible control point placement and target reproduction, reducing costs and increasing practicality for virtual reality applications.

Contribution

A novel 3D CNN method for PSZ that handles varied control points and targets with a single training, enabling more flexible and cost-effective virtual sound zones.

Findings

Handles varied reproduction targets with one training session

Supports flexible control microphone grid placement

Learns global spatial information from sparse points

Abstract

Personal sound zone (PSZ) reproduction system, which attempts to create distinct virtual acoustic scenes for different listeners at their respective positions within the same spatial area using one loudspeaker array, is a fundamental technology in the application of virtual reality. For practical applications, the reconstruction targets must be measured on the same fixed receiver array used to record the local room impulse responses (RIRs) from the loudspeaker array to the control points in each PSZ, which makes the system inconvenient and costly for real-world use. In this paper, a 3D convolutional neural network (CNN) designed for PSZ reproduction with flexible control microphone grid and alternative reproduction target is presented, utilizing the virtual target scene as inputs and the PSZ pre-filters as output. Experimental results of the proposed method are compared with the traditional method, demonstrating that the proposed method is able to handle varied reproduction targets on flexible control point grid using only one training session. Furthermore, the proposed method also demonstrates the capability to learn global spatial information from sparse sampling points distributed in PSZs.