Sinusoidal parameter estimation is a fundamental task in applications from spectral analysis to time-series forecasting. Estimating the sinusoidal frequency parameter by gradient descent is, however, often impossible as the error function is non-convex and densely populated with local minima. The growing family of differentiable signal processing methods has therefore been unable to tune the frequency of oscillatory components, preventing their use in a broad range of applications. This work presents a technique for joint sinusoidal frequency and amplitude estimation using the Wirtinger derivatives of a complex exponential surrogate and any first order gradient-based optimizer, enabling end to-end training of neural network controllers for unconstrained sinusoidal models.

翻译：线性参数估计是从光谱分析到时间序列预报的应用中的一项基本任务。然而,由于误差函数是非隐形的,而且以当地微型为人口稠密,因此通常无法按梯度下降来估计正弦性频率参数。因此,越来越多的不同信号处理方法无法调和脉冲元件的频率,从而无法在广泛的应用中加以使用。这项工作是一种使用复合指数性超升加速器和任何一级梯度优化器的韦丁格衍生物来联合对正弦性频率和振幅进行估计的技术,使神经网络控制器的终端培训能够用于未受控制的正弦性模型。