An Iterative Linearised Solution to the Sinusoidal Parameter Estimation Problem
This incremental improvement addresses real-time efficiency for applications like speech and audio coding.
The authors tackled the high computational cost of sinusoidal parameter estimation in signal processing by proposing a low-complexity iterative method, achieving O(LN) complexity and demonstrating improved accuracy over matching pursuits and time-frequency reassignment methods in experiments.
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.