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1、VeteransAdministrationJournal of Rehabilitation Researchand Development Vol . 24 No . 4Pages 65-74Application of adaptive digital signal processingspeech enhancement for the hearing impairedDOUGLAS M . CHABRIES, RICHARD W . CHRISTIANSEN, ROBERT H . BREY, MARTIN S.ROBINETTE, and RICHARD W . HARRIS*Br
2、igham Young University, Provo, Utah and Mayo Clinic Audiology Department, Rochester, Minn( sotcAbstract-A major complaint of individuals with normalhearing and hearing impairments is a reduced ability tounderstand speech in a noisy environment . This paperdescribes the concept of adaptive noise canc
3、elling forremoving noise from corrupted speech signals . Applica-tion of adaptive digital signal processing has long beenknown and is described from a historical as well astechnical perspective . The Widrow-Hoff LMS (least meansquare) algorithm developed in 1959 forms the introduc-tion to modern ada
4、ptive signal processing . This methoduses a primary input which consists of the desiredspeech signal corrupted with noise and a second ref-erence signal which is used to estimate the primarynoise signal . By subtracting the adaptively filtered esti-mate of the noise, the desired speech signal is obt
5、ained.Recent developments in the field as they relate to noisecancellation are described . These developments includemore computationally efficient algorithms as well asalgorithms that exhibit improved learning performance.A second method for removing noise from speech, foruse when no independent re
6、ference for the noise exists,is referred to as single channel noise suppression . Bothadaptive and spectral subtraction techniques have beenapplied to this problemoften with the result of decreasedspeech intelligibility . Current techniques applied to this* Douglas M . Chabries and Richard W. Christ
7、iansen are with theElectrical Engineering Department, Brigham Young University, Provo,Utah 84602.Robert H . Brey and Richard W . Harris are with the Department ofEducational Psychology (Audiology), Brigham Young University, Provo,Utah 84602.Martin S . Robinette is with the Mayo Clinic Audiology Depa
8、rtment,Rochester, Minnesota 55905 .problem are described, including signal processing tech-niques that offer promise in the noise suppression appli-cation.INTRO?DUCTIONHearing impairment is not only the most prevalentcommunicative disorder, it is also the number onechronic disability affecting peopl
9、e in the UnitedStates . A major complaint of those with hearingimpairments is a reduced ability to understand speechin everyday communication in a noisy environment.Even with the absence of hearing impairment, theaddition of background noise can signifiantly reducethe intelligibility of speech . In
10、1956, Widrow pro-posed an adaptive filter as shown in Figure 1 whichcan be used to reduce interference when a secondsample of the noise is available . This technique wasdeveloped at Stanford University in 1959 and appliedto a pattern-recognition scheme known as Adaline.In 1965 the first adaptive noi
11、se cancelling systemwas built by two students at Stanford University.In 1972, the first all-digital adaptive filter was builtby McCool and Widrow at the Naval UnderseaCenter in Pasadena, California . In 1975, severalapplications of the LMS algorithm were presentedwhich included adaptive noise cancel
12、ling and noisesuppression (32) . The LMS algorithm was simpleboth in the number of calculations required for itsupdate and in its derivationand robust in a numberof applications . An adaptive feedback constant, p ,6567Section II. Noise Reduction : Chabries et al.SIGNAL + INTERFERENCEFigure 2.An adap
13、tive LMS filter.tion from the desired optimal filter results . Byreducing the size of the feedback coefficient inEquation 3, the misadjustment can be made arbi-trarily small . However, since the adaptation time isinversely proportional to , a large for fast learningis required in applications where
14、the statistics of theinput signals vary with time . This selection, how-ever, results in increased misadjustment or residualerror. One may optimize the choice of in casesof these nonstationary inputs by selecting a feedbackconstant such that the error due to tracking thenonstationarity in the signal
15、 just equals the misad-justment or error residual that occurs because ofthe constant updates that occur after the filtercoefficients, wp(k), have converged to their desiredvalue (31).Since the filter parameters adapt in such a wayas to provide an estimate of np, (k) from the referencesignal n,.ef (k
16、), the task in applying the algorithm tonoise cancellation becomes one of providing suffi-cient degrees of freedom that an acceptable solutionmay be obtained . The following design or selectioncriteria must be followed:1. The number of digital filter stages (N in Equation1 should be selected so that N times T, thesample period of the digital system, is larger thanthe impulse response or reverbertion time of theacoustic environment . For small rooms, typicalfilter lengt
