An Iterative Linearised Solution to the Sinusoidal Parameter Estimation Problem

TL;DR

This paper introduces a low-complexity iterative method for sinusoidal parameter estimation that is faster and more accurate than existing methods, suitable for real-time signal processing applications.

Contribution

The paper presents a novel iterative linearised approach for sinusoidal parameter estimation with significantly reduced computational complexity and improved accuracy over prior methods.

Findings

Complexity of O(LN), lower than matching pursuits

More accurate than matching pursuits and time-frequency reassignment

Rapid convergence from initial frequency estimates

Abstract

Signal processing applications use sinusoidal modelling for speech synthesis, speech coding, and audio coding. Estimation of the model parameters involves non-linear optimisation methods, which can be very costly for real-time applications. We propose a low-complexity iterative method that starts from initial frequency estimates and converges rapidly. We show that for N sinusoids in a frame of length L, the proposed method has a complexity of O(LN), which is significantly less than the matching pursuits method. Furthermore, the proposed method is shown to be more accurate than the matching pursuits and time-frequency reassignment methods in our experiments.