Adaptive noise cancellation: a practical study of the least-mean square (LMS) over recursive least-square (RLS) algorithm

Vehicle Cabin Noise Cancellation Model Using Pre-Filter for Improved Convergence Rate and Better Stability

“…to minimize the mean square error as defined as error vector magnitude between the desired response and the actual one [17]. In the case of multiple noise sources, the multiple channel reference signal is required for efficient active noise cancellation [18].…”

Adaptive Noise Cancellation System Using a Recursive Least Squares Filter

“…In (8) it is seen that the a priori error results in an expression of the general form, in which the a priori error is calculated to determine the optimal coefficients of an RLS filter, as shown in (9).…”

“…On the other hand, Block Least Mean Square (BLMS) have been developed, with a fast convergence speed and greater robustness than previously related algorithms [2]. LMS and RLS are described in [9]. An analysis is made to implement both algorithms using a microphone, two speakers, and a sound card.…”

Characteristics and properties of audio signal and noise cancellation techniques: A theoretical review

“…The main purpose of this adaptive algorithm is to minimize the man square error by adjusting the weights of the adaptive linear combiner. To minimize the number of iterations performed in LMS algorithm an advanced LMS known as RLS algorithm can also be used [1] [2]. Besides this algorithm namely BLMS (Block least man square algorithm) having the advantage of simple design and more adaptability, higher complex ratio and fast convergence rate are highly suitable for use in real time processing applications [3].…”