An Approach for Snore Signal Masking Using Interval Adaptive Filter

soumya S patil


This paper proposes a unique approach to the active noise cancellation system, which provides an efficient and effective non-intrusive solution for reducing the disturbing snore signal in the room. An interval analysis based adaptive algorithm is developed which is optimized for the different kinds of snore signals. In this work, we are replacing the input signals auto-correlation matrix with an approximate estimate. The assumption is made that the input signal’s auto-correlation matrix is Toeplitz. The calculation of the inverse of the auto-correlation matrix is replaced with multiplication in the frequency domain. The stability of the algorithm is increased as interval matrices handles the bounded values. The main objective of this work is to increase stability by reducing the mean square error. The results obtained prove to show that the implementation with interval arithmetic is more accurate ruling out the rounding errors which are unavoidable in the traditional floating-point approach. On the other hand, as there are two values (infimum, supremum) in interval analysis, the computational complexity increases.


Interval Analysis (IA) ANC adaptive filter machine epsilon LMS algorithm quasi-Newton method



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