Blockwise Weighted Least Square Active Noise Control for CPU-GPU Architecture

Abstract

Active noise control (ANC) is a technology which lowers the noise level by using the principle of destructive interference of sound wave. Even though recent developments in digital signal processing (DSP) made it possible to implement ANC algorithms in real-time, insufficient computational power is still one of the challenges to solve. In the previous research, as a way of overcoming the lack of computational power, CPU-GPU architecture was proposed so that ANC algorithms utilize the massive computing power of GPU without suffering from the block data transfer between CPU and GPU memories. However, for the feasibility test of the proposed CPU-GPU architecture in the previous research, a conventional block ANC algorithm was used, and ANC algorithm which can fully utilize the massive computing power of GPU has not been developed. In this article, ANC algorithm, which directly derives blockwise least square solution through GPU computation while generating control signals through CPU computation, is proposed. Based on the observation about speaker saturation and increase of noise level after applying the conventional blockwise least square solution, a new cost function for preventing such problems is also proposed. Therefore, blockwise weighted least square ANC (BWLS-ANC) algorithm, which derives blockwise least square solution minimizing the proposed cost function through GPU computation while generating control signals through CPU computation, is proposed throughout this research. Problems of conventional blockwise least square solution upon ANC applications are observed through simulations and experiments. The feasibility of the proposed BWLS-ANC algorithm is verified through experiments.