Sinusoidal Frequency Estimation by Gradient Descent

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 gr...

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Bibliographic Details
Main Authors: Hayes, Ben, Saitis, Charalampos, Fazekas, Gyorgy
Format: Conference Proceeding
Language:English
Subjects:
differentiable signal processing
Frequency estimation
machine learning
Neural networks
Signal processing
sinusoidal parameter estimation
Speech processing
Task analysis
Training
Online Access:Request full text
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10095188