We used a deep learning network to find the frequency of a noisy sinusoidal wave. A three-layer neural network was designed to extract the frequency of sinusoidal waves that had been combined with white noise at a signal-to-noise ratio of 25 dB. One hundred thousand waves were prepared for training and testing the model. We designed a neural network that could achieve a mean squared error of 4 × 10−5 for normalized frequencies. This model was written for the range 1 kHz ≤ f ≤ 10 kHz but also shown how to easily be generalized to other ranges. The algorithm is easy to rewrite and the final results are highly accurate. The trained model can find frequency of any previously-unseen noisy wave in less than a second.

中文翻译：

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通过深度学习，从嘈杂的正弦波中准确而即时地估算出频率。

我们使用了深度学习网络来发现正弦波的频率。设计了一个三层神经网络，以提取与白噪声相结合的正弦波频率，信噪比为25 dB。准备了十万波用于训练和测试该模型。我们设计了一个神经网络，对于归一化的频率，它可以实现4×10−5的均方误差。该模型的编写范围是1 kHz≤f≤10 kHz，但也展示了如何轻松地推广到其他范围。该算法易于重写，最终结果非常准确。经过训练的模型可以在不到一秒钟的时间内找到任何先前未见到的噪声波的频率。