Lightweighted FPGA Implementation of Even-Odd-Buffered Active Noise Canceller with On-Chip Convolution Acceleration Units

Park, Seung-Hyun; Park, Daejin

doi:10.1109/iceic57457.2023.10049949

Cited by 2 publications

(2 citation statements)

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“…However, there is a bottleneck in the data transfer process because the buffer cannot hold long windows. Our previous study [15] confirmed that an even-odd buffer sampling method is more efficient when the sampled frame is large. Experiments comparing other models' performance are possible because they are synthesizable to the register transfer level (RTL) schematics and feasible on the FPGA.…”

Section: Even-odd Buffersupporting

confidence: 56%

“…Our previous work (2023) [15] focused on designing a simple even-odd buffer prototype and researching how to manage the audio signal stream while using the LMS algorithm as a noise control. The windowing effect, FFT calculation, and other buffering-related issues were thoroughly investigated to build a basic model for high-performance and low-power ANC systems.…”

Section: Related Workmentioning

confidence: 99%

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Low-Power FPGA Realization of Lightweight Active Noise Cancellation with CNN Noise Classification

Park

2023

Electronics

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Active noise cancellation (ANC) is the most important function in an audio device because it removes unwanted ambient noise. As many audio devices are increasingly equipped with digital signal processing (DSP) circuits, the need for low-power and high-performance processors has arisen because of hardware resource restrictions. Low-power design is essential because wireless audio devices have limited batteries. Noise cancellers process the noise in real time, but they have a short secondary path delay in conventional least mean square (LMS) algorithms, which makes implementing high-quality ANC difficult. To solve these problems, we propose a fixed-filter noise cancelling system with a convolutional neural network (CNN) classification algorithm to accommodate short secondary path delay and reduce the noise ratio. The signal-to-noise ratio (SNR) improved by 2.3 dB with CNN noise cancellation compared to the adaptive LMS algorithm. A frequency-domain noise classification and coefficient selection algorithm is introduced to cancel the noise for time-varying systems. Additionally, our proposed ANC architecture includes an even–odd buffer that efficiently computes the fast Fourier transform (FFT) and overlap-save (OLS) convolution. The simulation results demonstrate that the proposed even–odd buffer reduces processing time by 20.3% and dynamic power consumption by 53% compared to the single buffer.

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Section: Even-odd Buffersupporting

confidence: 56%

Section: Related Workmentioning

confidence: 99%