Hybrid feedback active noise control headset based on binaural signal utilization

A standard feedback active noise control (FBANC) headset utilizes the estimate of a primary disturbance at the left-ear (right-ear) error microphone to control noise only at the left (right) ear-cup, i.e., each ear’s controller works independently. In contrast to the FBANC headset, in this paper, a binaural hybrid feedback active noise control (HFBANC) headset is designed that uses the estimate of the primary disturbances at both the left and right-ear error microphones to achieve improved noise control at both the left and right ear-cups. To further improve noise cancellation performance, the nearest Kronecker product decomposition technique is incorporated into the algorithm. The performance of the proposed HFBANC headset is compared to the standard FBANC headset under a variety of different sound field conditions. Experimental results show an improvement of 3–5 dB in the noise cancellation using the proposed algorithms, where the benefits are more prominent for noise sources originating from the side of the user (left and right)

Swarm and evolutionary computing algorithms for system identification and filter design: a comprehensive review

An exhaustive review on the use of structured stochastic search approaches towards system identification and digital filter design is presented in this paper. In particular, the paper focuses on the identification of various systems using infinite impulse response adaptive filters and Hammerstein models as well as on the estimation of chaotic systems. In addition to presenting a comprehensive review on the various swarm and evolutionary computing schemes employed for system identification as well as digital filter design, the paper is also envisioned to act as a quick reference for a few popular evolutionary computing algorithms.by Akhilesh Gotmare, Sankha Subhra Bhattacharjee, Rohan Patidar and Nithin V. Georg

Speech enhancement in digital hearing aids: an active noise control approach

by Somanath Pradhan, Sankha Subhra Bhattacharjee, Vinal Patel and Nithin V. Georg

Widely linear complex-valued hyperbolic secant adaptive filtering algorithm and its performance analysis

To achieve good estimation in the impulsive noise environment, some hyperbolic functions have been introduced as cost functions to develop robust adaptive filtering algorithms in recent years. However, the hyperbolic functions have rarely been studied for complex-valued signals. In this paper, a robust adaptive algorithm is introduced for processing both circular and non-circular complex-valued signals by utilizing the complex hyperbolic secant cost functions. Moreover, its performance is thoroughly analyzed, including mean convergence, mean-square transient behavior, and mean-square steady-state behavior. Finally, the simulation results show the advantages of the proposed algorithm for complex-valued signal processing in non-Gaussian environments and verify the accuracy of the theoretical performance analysis