Adaptive filters to improve loudspeaker telephone

Electromagnetic composite field analysis of underwater sound source based on fast Fourier transform

“…The phase response is equally important, as it describes how the phase of the output signal changes with frequency, impacting the coherent summation of sound waves, particularly in multi-driver speaker systems, as illustrated in Figure 1. Harmonic distortion, which occurs when the output signal contains frequencies not present in the input signal, is a measure of unwanted frequencies introduced by nonlinearities in the loudspeaker's components [9] .…”

Recursive inverse algorithm: Mean-square-error analysis

“…In the implementation of the RI algorithm, the update equation can be reorganized to eliminate redundant computations. Substituting (4) and (5) in (6) and rearranging…”

“…To see this, let w(k) = w(k − 1) + (k), where (k) is the change in the weight vector over one time step. Substituting in (6) and simplifying we get…”

“…In finite impulse response (FIR) adaptive filtering, the filter weights are updated iteratively by minimizing the mean-square-error (MSE) between the desired response and the output of the adaptive filter. The well-known leastmean-square (LMS) adaptive algorithm has been used in many application areas, such as channel equalization [3], system identification [4], adaptive array processing [5], echo cancellation [6], etc. The LMS algorithm is not only simple in its filter weight updating but also reasonably fast in convergence if the optimal step-size is used [7].…”