Neural personal sound zones with flexible bright zone control
This addresses the practical deployment challenges of PSZ systems for virtual reality applications, though it appears incremental as it builds on existing PSZ technology with a neural network adaptation.
The paper tackled the inconvenience and cost of traditional personal sound zone (PSZ) systems by proposing a 3D convolutional neural network that uses virtual target scenes as inputs to generate PSZ pre-filters, enabling flexible control microphone grids and varied reproduction targets with only one training session.
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.