Frequency-domain blind source separation (BSS) is shown to be equivalent to two sets of frequency-domain adaptive beamformers (ABFs) under certain conditions. The zero search of the off-diagonal components in the BSS update equation can be viewed as the minimization of the mean square error in the ABFs. The unmixing matrix of the BSS and the filter coefficients of the ABFs converge to the same solution if the two source signals are ideally independent. If they are dependent, this results in a bias for the correct unmixing filter coefficients. Therefore, the performance of the BSS is limited to that of the ABF if the ABF can use exact geometric information. This understanding gives an interpretation of BSS from a physical point of view
In the presence of tonal noise generated by periodic noise source like rotating machines, the filtered-X LMS (FXLMS) algorithm is used for active control of such noises. However, the algorithm is derived under the assumption of slow adaptation limit and the exact analysis of the algorithm is restricted to the case of one real sinusoid in the literature. In this paper, for the general case of arbitrary number of sources, the characteristic polynomial of the equivalent linear system describing the FXLMS algorithm is derived and a method for calculating the stability limit is presented. Also, a related new algorithm free from the above assumption, which is nonlinear with respect to the tap weights, is proposed. Simulation results show that in the early stage of adaptation the new algorithm gives faster decay of errors.Index Terms-Active noise control, convergence analysis, filtered-X LMS (FXLMS) algorithm, multitonal noise.
Hideaki Sakai
Frequency domain Blind Source Separation (BSS) is shown to be equivalent to two sets of frequency domain adaptive microphone arrays, i.e., Adaptive Beamformers (ABF). The minimization of the off-diagonal components in the BSS update equation can be viewed as the minimization of the mean square error in the ABF. The unmixing matrix of the BSS and the filter coefficients of the ABF converge to the same solution in the mean square error sense if the two source signals are ideally independent. Therefore, we can conclude that the performance of the BSS is upper bounded by that of the ABF. This understanding clearly explains the poor performance of the BSS in a real room with long reverberation.
An exact analysis is presented for the LMS algorithm with tonal reference signals in the presence of frequency mismatch. First, the time-varying linear system describing the LMS algorithm is converted into a time-invariant linear system. Then, a necessary and sufficient condition about the step sizes for convergence of the algorithm is derived using the Lyapunov function method and a transient behavior is analyzed. Finally, the effects of observation noise and frequency mismatch are examined without any approximations. The validity of the obtained results is shown by simulations.
The objective of this letter is to analyze the effects of frequency mismatch for an adaptive algorithm that becomes the Filtered-X LMS algorithm when the reference signals are purely sinusoidal. The Filtered-X LMS algorithm is often used for active control of acoustic noise. For the case of sinusoidal noise sources, if there is a deviation between the frequency used in the adaptive algorithm and its true value (frequency mismatch), the performance of the Filtered-X LMS algorithm might degrade considerably. In this letter, using the equivalent transfer function method, the effects of frequency mismatch are analyzed precisely. Finally, computer simulations are presented to demonstrate the obtained results.
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