  Background: Active noise cancellation has been a subject of research for
decades. Traditional techniques, like the Fast Fourier Transform, have
limitations in certain scenarios. This research explores the use of deep neural
networks (DNNs) as a superior alternative. Objective: The study aims to
determine the effect sampling rate within training data has on lightweight,
efficient DNNs that operate within the processing constraints of mobile
devices. Methods: We chose the ConvTasNET network for its proven efficiency in
speech separation and enhancement. ConvTasNET was trained on datasets such as
WHAM!, LibriMix, and the MS-2023 DNS Challenge. The datasets were sampled at
rates of 8kHz, 16kHz, and 48kHz to analyze the effect of sampling rate on noise
cancellation efficiency and effectiveness. The model was tested on a core-i7
Intel processor from 2023, assessing the network's ability to produce clear
audio while filtering out background noise. Results: Models trained at higher
sampling rates (48kHz) provided much better evaluation metrics against Total
Harmonic Distortion (THD) and Quality Prediction For Generative Neural Speech
Codecs (WARP-Q) values, indicating improved audio quality. However, a trade-off
was noted with the processing time being longer for higher sampling rates.
Conclusions: The Conv-TasNET network, trained on datasets sampled at higher
rates like 48kHz, offers a robust solution for mobile devices in achieving
noise cancellation through speech separation and enhancement. Future work
involves optimizing the model's efficiency further and testing on mobile
devices.
