Hardware accelerated Active Noise Cancellation system using Haar wavelets

Abstract

Active Noise Cancellation (ANC) systems are widely used to mitigate unwanted noises in several applications, such as automotive environments and high-end headsets. Multi-Channel (MC) ANC systems have shown promise in creating improved silent zones. Typically, these systems are implemented on FPGA platforms due to the systolic nature and granularity of optimization of these devices. This article describes the design, implementation, and evaluation of a wavelet-based MC ANC Filtered-x Normalized Least Mean Square (FxNLMS) on an FPGA platform.The use of wavelet transform enables the decomposition of complex noise signals into spectrally more compact signals (i.e., easier to process). In this work, for each decomposed signal, an independent NLMS is applied. The system implements 64 parallel NLMS with 1000 coefficients. Additionally, the static FIR filters employed for secondary and tertiary path estimations are of the 2047th order. The system adopts an integer arithmetic architecture and operates at a sampling rate of 47.97 kHz. To assess the performance of the wavelet-based approach, benchmark tests were conducted by comparing it against a similar implementation without the wavelet transform. The evaluation was performed using noise reduction (NR) tests with spectrally rich (20 Hz to 10 kHz) and high dynamic range noises. The experimental setup involved two error microphones and two secondary sources.The results show that the wavelet-based version has overall better performance than the traditional implementation, particularly in the higher frequency band of the spectrum (1 kHz to 8 kHz). For instance, in the case of city ambient noise (a realistic noise with high dynamic range), the relative NR achieved was 8.23 dB.To the authors’ knowledge, this is the first time that the implementation and field-test of a wavelet-based MC ANC on an FPGA platform was conducted. Moreover, the obtained results show that the novel approach is better in reducing complex noises than the traditional implementation – without wavelets.