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.

翻译：背景：主动降噪技术已历经数十年研究。传统方法（如快速傅里叶变换）在某些场景中存在局限性。本研究探索了深度神经网络作为更优替代方案的可行性。目标：旨在探究训练数据采样率对轻量化高效DNN的影响，此类网络需在移动设备处理能力限制下运行。方法：选用ConvTasNET网络，因其在语音分离与增强任务中具有已验证的高效性。使用WHAM!、LibriMix及MS-2023 DNS挑战赛数据集，分别以8kHz、16kHz和48kHz采样率进行训练，以分析采样率对降噪效能的影响。模型在2023款酷睿i7处理器上测试，评估其滤除背景噪声并生成清晰音频的能力。结果：较高采样率（48kHz）训练的模型在总谐波失真与生成式神经语音编解码器质量预测指标上表现更优，表明音频质量得到提升。但需权衡处理时间随采样率升高而增加的问题。结论：基于高采样率（如48kHz）数据集训练的ConvTasNET网络，为移动设备通过语音分离与增强实现降噪提供了稳健解决方案。未来工作将着重于模型效率优化及移动端部署测试。