Filter Bank Design based on Minimization of Individual Aliasing Terms for Minimum Mutual Information Subband Adaptive Beamforming

This paper presents new filter bank design methods for sub- band adaptive beamforming. In this work, we design analysis and synthesis prototypes for modulated filter banks so as to minimize each aliasing term individually. We then drive the total response error to null by constraining these prototypes to be Nyquist(M) filters. Thereafter those modulated filter banks are applied to a speech separation system which extracts a target speech signal. In our system, speech signals are first transformed into the subband domain with our filter banks, and the subband components are then processed with a beamforming algorithm. Following beamforming, post-filtering and binary masking are further performed to remove residual noises. We show that our filter banks can suppress the residual aliasing distortion more than conventional ones. Furthermore, we demonstrate the effectiveness of our design techniques through a set of automatic speech recognition experiments on the multi-channel speech data from the PASCAL Speech Separation Challenge. The experimental results prove that our beamforming system with the proposed filter banks achieves the best recognition performance, a 39.6 % word error rate (WER), with half the amount of computation of that of the conventional filter banks while the perfect reconstruction filter banks provided a 44.4 % WER

Filter Bank Design for Subband Adaptive Beamforming and Application to Speech Recognition

\begin{abstract} We present a new filter bank design method for subband adaptive beamforming. Filter bank design for adaptive filtering poses many problems not encountered in more traditional applications such as subband coding of speech or music. The popular class of perfect reconstruction filter banks is not well-suited for applications involving adaptive filtering because perfect reconstruction is achieved through alias cancellation, which functions correctly only if the outputs of individual subbands are \emph{not} subject to arbitrary magnitude scaling and phase shifts. In this work, we design analysis and synthesis prototypes for modulated filter banks so as to minimize each aliasing term individually. We then show that the \emph{total response error} can be driven to zero by constraining the analysis and synthesis prototypes to be \emph{Nyquist($M$)} filters. We show that the proposed filter banks are more robust for aliasing caused by adaptive beamforming than conventional methods. Furthermore, we demonstrate the effectiveness of our design technique through a set of automatic speech recognition experiments on the multi-channel, far-field speech data from the \emph{PASCAL Speech Separation Challenge}. In our system, speech signals are first transformed into the subband domain with the proposed filter banks, and thereafter the subband components are processed with a beamforming algorithm. Following beamforming, post-filtering and binary masking are performed to further enhance the speech by removing residual noise and undesired speech. The experimental results prove that our beamforming system with the proposed filter banks achieves the best recognition performance, a 39.6\% word error rate (WER), with half the amount of computation of that of the conventional filter banks while the perfect reconstruction filter banks provided a 44.4\% WER. \end{abstract

A before-after implementation trial of smoking cessation guidelines in hospitalized veterans

Abstract Background Although most hospitalized smokers receive some form of cessation counseling during hospitalization, few receive outpatient cessation counseling and/or pharmacotherapy following discharge, which are key factors associated with long-term cessation. US Department of Veterans Affairs (VA) hospitals are challenged to find resources to implement and maintain the kind of high intensity cessation programs that have been shown to be effective in research studies. Few studies have applied the Chronic Care Model (CCM) to improve inpatient smoking cessation. Specific objectives The primary objective of this protocol is to determine the effect of a nurse-initiated intervention, which couples low-intensity inpatient counseling with sustained proactive telephone counseling, on smoking abstinence in hospitalized patients. Key secondary aims are to determine the impact of the intervention on staff nurses' attitudes toward providing smoking cessation counseling; to identify barriers and facilitators to implementation of smoking cessation guidelines in VA hospitals; and to determine the short-term cost-effectiveness of implementing the intervention. Design Pre-post study design in four VA hospitals Participants Hospitalized patients, aged 18 or older, who smoke at least one cigarette per day. Intervention The intervention will include: nurse training in delivery of bedside cessation counseling, electronic medical record tools (to streamline nursing assessment and documentation, to facilitate prescription of pharmacotherapy), computerized referral of motivated inpatients for proactive telephone counseling, and use of internal nursing facilitators to provide coaching to staff nurses practicing in non-critical care inpatient units. Outcomes The primary endpoint is seven-day point prevalence abstinence at six months following hospital admission and prolonged abstinence after a one-month grace period. To compare abstinence rates during the intervention and baseline periods, we will use random effects logistic regression models, which take the clustered nature of the data within nurses and hospitals into account. We will assess attitudes of staff nurses toward cessation counseling by questionnaire and will identify barriers and facilitators to implementation by using clinician focus groups. To determine the short-term incremental cost per quitter from the perspective of the VA health care system, we will calculate cessation-related costs incurred during the initial hospitalization and six-month follow-up period. Trial number NCT00816036http://deepblue.lib.umich.edu/bitstream/2027.42/112349/1/13012_2009_Article_190.pd