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.

A new delayless sub-band filtering method for cancelling the effect of feedback path in hearing aid systems.

Performance of commonly used Hearing aid systems is degraded by the presence of acoustic feedback between loudspeaker and microphone. Prediction Error Method Adaptive Feedback Canceller (PEM-AFC) has been proposed recently that could attenuate the feedback effect. In this paper, we present a new delayless frequency-based sub-band filtering method for alleviating the effect of feedback path for the Hearing aid systems. The proposed method avoids sub-band distortions and has low computational complexity making it suitable for low-power cost-effective hearing aid system designs. Performances of the two methods are compared and simulation results are presented.

Performance enhancement of adaptive Active Noise Control systems for FMRI machines.

Active Noise Control (ANC) of fMRI acoustic noise using the conventional Filtered-X LMS (FXLMS) approach results in poor cancelation performance and slow convergence due to its broadband nature and the need for high order adaptive filters. High order adaptive filters are needed to effectively model the long acoustic impulse responses. Existing methods to improve the performance of FXLMS based broadband ANC systems are either computationally expensive or need elaborate implementation. In this paper we show a practical method to enhance the performance of FXLMS based algorithms, by deriving a crude estimate of the causalWiener filter and initializing the adaptive filter with the estimated Wiener filter. We observe that very fast convergence to the global minimum can be achieved along with huge gains in the noise cancelation performance. We call this method Wiener initialized FXLMS (WI-FXLMS).We show the effectiveness of the proposed approach for the active noise control of functional MRI acoustic noise and several other realistic noise sources.

Objective and subjective evaluation of adaptive speech enhancement methods for functional MRI.

PURPOSE: To recover speech corrupted by functional magnetic resonance imaging (fMRI) acoustic noise using two-channel adaptive speech enhancement techniques. MATERIALS AND METHODS: Speech corrupted by noise generated from a 3 T MRI scanner was recorded using diffuse-field microphones and a data acquisition board. Multiband and subband adaptive speech enhancement methods are used to recover the speech signal from the recordings. Normalized least mean squares (NLMS) algorithm was used for updating the filter coefficients in each band. RESULTS: The methods are successful in enhancing the speech quality. They are successful in improving the convergence rate of the adaptive filter. Multiband and subband methods have a similar performance in terms of noise reduction and in the subjective tests. The subband method introduces less speech distortion compared to the multiband method. The subband method requires a lower number of computations per sample. CONCLUSION: Adaptive speech enhancement techniques are effective in reducing fMRI background noise in the recordings. Based on the analysis, we conclude that subband-based methods are more suited for enhancing speech corrupted by fMRI noise.

Integrated speech enhancement for functional MRI environment.

This paper presents an integrated speech enhancement (SE) method for the noisy MRI environment. We show that the performance of SE system improves considerably when the speech signal dominated by MRI acoustic noise at very low SNR is enhanced in two successive stages using two-channel SE methods followed by a single-channel post processing SE algorithm. Actual MRI noisy speech data are used in our experiments showing the improved performance of the proposed SE method.

A multichannel speech enhancement method for functional MRI systems using a distributed microphone array.

Multichannel speech enhancement has been shown to be an effective method to decrease speech distortion introduced during speech enhancement, especially in environments like MRI (magnetic resonance imaging) which have a distributed noise source. However, these methods suffer from high computational complexity which makes them almost impractical. The use of subband filtering has been suggested to reduce this complexity but the performance of the existing subband methods deteriorate as the number of subbands increases. In this paper we introduce a new multichannel speech enhancement algorithm based on subband adaptive filtering that works for higher number of subbands at a lower complexity. The real-world experiments demonstrate the performance of the new scheme in an MRI room.

Equalizing secondary path effects using the periodicity of fMRI acoustic noise.

Non-minimum phase secondary path has a direct effect on achieving a desired noise attenuation level in active noise control (ANC) systems. The adaptive noise canceling filter is often a causal FIR filter which may not be able to sufficiently equalize the effect of a non-minimum phase secondary path, since in theory only a non-causal filter can equalize it. However a non-causal stable filter can be found to equalize the non-minimum phase effect of secondary path. Realization of non-causal stable filters requires knowledge of future values of input signal. In this paper we develop methods for equalizing the non-minimum phase property of the secondary path and improving the performance of an ANC system by exploiting the periodicity of fMRI acoustic noise. It has been shown that the scanner noise component is highly periodic and hence predictable which enables easy realization of non-causal filtering. Improvement in performance due to the proposed methods (with and without the equalizer) is shown for periodic fMRI acoustic noise.

Distortion analysis of subband adaptive filtering methods for FMRI active noise control systems.

Delayless subband filtering structure, as a high performance frequency domain filtering technique, is used for canceling broadband fMRI noise (8 kHz bandwidth). In this method, adaptive filtering is done in subbands and the coefficients of the main canceling filter are computed by stacking the subband weights together. There are two types of stacking methods called FFT and FFT-2. In this paper, we analyze the distortion introduced by these two stacking methods. The effect of the stacking distortion on the performance of different adaptive filters in FXLMS algorithm with non-minimum phase secondary path is explored. The investigation is done for different adaptive algorithms (nLMS, APA and RLS), different weight stacking methods, and different number of subbands.

LMS-based active noise cancellation methods for fMRI using sub-band filtering.

We present application of adaptive LMS-based method using two different sub-band filtering techniques for active reduction of 3T-fMRI acoustic noise. Analysis and design of the sub-band filters are discussed based on the characteristics of the noise. Using the fMRI-brain scanner acoustic noise, performance of the methods are analyzed and compared for different number of sub-band filters.