Govind Kannan

On maximum achievable noise reduction in ANC systems

Active noise control (ANC) is primarily a signal estimation problem. The nature of noise to be canceled, the acoustic paths, and the transfer functions of microphones/loudspeakers place fundamental limits on the performance of ANC systems. In this paper we analyze the performance of ANC systems from the maximum achievable noise attenuation level (NALmax) prospective for different ANC system structures i.e feed-forward, feedback and hybrid. We first derive NALmax for stochastic noise and then generalize the result to accommodate tonal (sinusoidal) noises. These results are invaluable in choosing an ANC design scheme.

An Efficient Feedback Active Noise Control Algorithm Based on Reduced-Order Linear Predictive Modeling of fMRI Acoustic Noise

Functional magnetic resonance imaging (fMRI) acoustic noise exhibits an almost periodic nature (quasi-periodicity) due to the repetitive nature of currents in the gradient coils. Small changes occur in the waveform in consecutive periods due to the background noise and slow drifts in the electroacoustic transfer functions that map the gradient coil waveforms to the measured acoustic waveforms. The period depends on the number of slices per second, when echo planar imaging (EPI) sequencing is used. Linear predictability of fMRI acoustic noise has a direct effect on the performance of active noise control (ANC) systems targeted to cancel the acoustic noise. It is shown that by incorporating some samples from the previous period, very high linear prediction accuracy can be reached with a very low order predictor. This has direct implications on feedback ANC systems since their performance is governed by the predictability of the acoustic noise to be cancelled. The low complexity linear prediction of fMRI acoustic noise developed in this paper is used to derive an effective and low-cost feedback ANC system.

Active optical lattice filters

Optical lattice filter structures including gains are introduced and analyzed. The photonic realization of the active, adaptive lattice filter is described. The algorithms which map between gains space and filter coefficients space are presented and studied. The sensitivities of filter parameters with respect to gains are derived and calculated. An example which is relevant to adaptive signal processing is also provided.

Active noise control of noisy periodic signals using signal separation

Periodic signals (since they can be easily predicted) can be canceled much more effectively when compared to non- periodic/stochastic signals. A large class of acoustic noise sources have an underlying periodic process that generates a periodic noise component, and thus the acoustic noise can in general be modeled as the sum of a periodic signal and a random signal (usually a background noise). In this paper we present the idea that separating the acoustic noise into periodic and random noise components and doing separate active noise control(ANC) for each tends to increase the over-all noise attenuation level (NAL). Formulae for the exact improvement in noise attenuation levels are derived. A novel signal separation and noise cancelation scheme based on adaptive filtering is developed and its effectiveness is shown for several periodic signal in white noise cases.

Composition methods for four-port couplers in photonic integrated circuitry

Planar photonic integrated circuits based on four-port couplers offer enhanced sophistication and functionality. Each four-port coupler is characterized by sixteen signal coupling coefficients governed by ten energy constraints. The ability to generate the constrained sixteen coupling coefficients is needed in the analysis of the four-port coupler. However, the energy constraint equations are nonlinear and cumbersome to solve directly. We introduce two techniques to reduce these signal coupling coefficients to a set of six free parameters. Hence we can characterize all possible couplers in terms of their sixteen constrained coupling coefficients, or either of two sets of six free parameters. This reduction in parameters has significant ramifications for the design, specification, and empirical characterization of these useful building blocks.

Lattice filter with adjustable gains and its application in optical signal processing

In this paper, we consider a class of N-th order lattice filter with gains as an extension to the traditional lattice filter structures and develop analysis and synthesis of such filters. In the analysis-synthesis parts, we present recursive methods to derive the input-output transfer function of the filter in terms of parameters of the lattice structure such as the time delay, gains, transmission and reflection coefficients and vice versa. Stability of the filter is analyzed and an algorithm to test the stability is proposed. This class of MIMO lattice filters with adjustable gains models integrated photonic devices under development in our labs for optical communication and high speed signal processing applications. Our signal processing approach to characterizing this type of lattice structures can also be used in filter realization by VLSI, FPGA, or programmable processors for acoustic or speech applications

Speech Enhancement in Functional MRI Environment using Adaptive Sub-Band Algorithms

Very high level of acoustic noise in fMRI scanner rooms disrupts speech communication between the subject and the physician/researcher. Enhancing speech in such an environment is challenging due to the broadband and dynamic nature of the noise. Sub-band adaptive methods prove to be very effective in cancelling such noise. In this paper we present the results of using sub-band adaptive methods for enhancing speech corrupted by noise from a 3-Tesla fMRI scanner. We also observe that the performance depends on the synthesis filter bank structure.

Weight Stacking Analysis of Delayless Subband Adaptive Algorithms for fMRI Active Noise Cancellation

High level acoustic noise in fMRI scanners is a source of concern to patients and health care providers. Active noise control systems employing delayless subband adaptive filters have been shown effective in fMRI acoustic noise reduction [3] [4]. In this method [5], adaptive filtering is done in subbands and the subband weights are stacked together to construct the fullband filter weights. There are two types of stacking methods called FFT and FFT-2. These stacking methods introduce distortion which limit the noise reduction level. In this paper, we model the distortion and analyze the effect of distortion when different adaptive schemes (nLMS, APA, RLS) are used. This analysis helps in selecting the appropriate adaptive scheme and determining the optimum number of subbands.

Analysis and design of active optical filter structures with two-port couplers

A signal processing approach to modeling, analyzing, and synthesizing a particular integrated photonic architecture of optical filters with tunable gains is presented. This particular architecture has two-port couplers and current-controlled semiconductor optical amplifiers (SOAs) fabricated on the same substrate. The device architecture forms a new lattice filter structure. Layer-peeling-type algorithms are developed for the analysis and synthesis of the device. The role of the adjustable gains in a lossless or lossy device is considered, and a novel stability algorithm for the filter structure is presented.

Analysis and optimal design of delayless subband active noise control systems for broadband noise

Active control of wide-band noise presents certain unique challenges many of which can be addressed using delayless subband adaptive filtering techniques. The performance of a delayless subband active noise control (DSANC) system depends on a complex interplay between the (1) choice of adaptation algorithm, (2) number of subbands, (3) weight stacking scheme, (4) input noise spectrum, and (5) primary, secondary paths. This interplay is studied in this paper for two different kinds of broadband noise. Distortion introduced by the weight stacking methods is investigated and quantified. It is shown that the computational complexity decreases and the stacking distortion increases with the number of subbands. The performance limiting effect of the non-minimum phase property of secondary path on the system performance is evaluated and analytically formulated. An upper bound for the obtainable noise attenuation level (NAL) is derived. A step by step optimal design procedure for the best performance is developed taking computational complexity into consideration. Simulation results support the analytical development and the proposed approach for optimal design of DSANC systems.