1. Field of the Invention
The present invention relates to apparatus and methods for feedback cancellation adapted to the detection of changes in the feedback path in audio systems such as hearing aids.
2. Prior Art
Mechanical and acoustic feedback limits the maximum gain that can be achieved in most hearing aids. System instability caused by feedback is sometimes audible as a continuous high frequency tone or whistle emanating from the hearing aid. Mechanical vibrations from the receiver in a high power hearing aid can be reduced by combining the outputs of two receivers mounted back to back so as to cancel the net mechanical moment; as much as 10 dB additional gain can be achieved before the onset of oscillation (or whistle) when this is done. But in most instruments, venting the BTE earmold or ITE shell establishes an acoustic feedback path that limits the maximum possible gain to less than 40 dB for a small vent and even less for large vents. The acoustic feedback path includes the effects of the hearing aid amplifier, receiver, and microphone as well as the vent acoustics.
The traditional procedure for increasing the stability of a hearing aid is to reduce the gain at high frequencies. Controlling feedback by modifying the system frequency response, however, means that the desired high frequency response of the instrument must be sacrificed in order to maintain stability. Phase shifters and notch filters have also been tried, but have not proven to be very effective.
A more effective technique is feedback cancellation, in which the feedback signal is estimated and subtracted from the microphone signal. Feedback cancellation typically uses an adaptive filter that models the dynamically changing feedback path within the hearing aid. Particularly effective feedback cancellation schemes are disclosed in patent application Ser. No. 08/972,265, entitled xe2x80x9cFeedback Cancellation Apparatus and Methods,xe2x80x9d incorporated herein by reference and patent application Ser. No. 09/152,033 entitled xe2x80x9cFeedback Cancellation Improvements,xe2x80x9d incorporated herein by reference (by the present inventors). Adaptive feedback cancellation systems, however, can generate a large mismatch between the feedback path and the adaptive filter modeling the feedback path when the input signal is narrow band or sinusoidal. Thus some adaptive feedback cancellation systems have combined an adaptive filter for feedback cancellation with a mechanism for reducing the hearing aid gain when a periodic input signal is detected (Wyrsch, S., and Kaelin, A., xe2x80x9cA DSP implementation of a digital hearing aid with recruitment of loudness compensation and acoustic echo cancellationxe2x80x9d, Proc. 1997 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, N.Y., Oct. 19-22, 1997). This approach, however, may reduce the hearing aid gain even if the adaptive filter is behaving correctly, thus reducing the audibility of desired sounds.
A feedback cancellation system should satisfy several performance objectives: The system should respond quickly to a sinusoidal input signal so that xe2x80x9cwhistlingxe2x80x9d due to hearing aid instability is stopped as soon as it occurs. The system adaptation should be constrained so that steady state sinusoidal inputs are not canceled and audible processing artifacts and coloration effects are prevented from occurring. The system should be able to adapt to large changes in the feedback path that occur, for example, when a telephone handset is placed close to the aided ear. And the system should provide an indication when significant changes have occurred in the feedback path and are not just due to the characteristics of the input signal.
The preferred feedback cancellation system satisfies the above objectives. The system uses constrained adaptation to limit the amount of mismatch that can occur between the hearing aid feedback path and the adaptive filter being used to model it. The constrained adaptation, however, allows a limited response to a sinusoidal signal so that the system can eliminate xe2x80x9cwhistlingxe2x80x9d when it occurs in the hearing aid. The constraints greatly reduce the probability that the adaptive filter will cancel a sinusoidal or narrow band input signal, but still allow the system to track the feedback path changes that occur in daily use. The constrained adaptation uses a set of reference filter coefficients that describe the most accurate available model of the feedback path.
Two procedures have been developed for LMS adaptation with a constraint on the norm of the adaptive filter used to model the feedback path. Both approaches are designed to prevent the adaptive filter coefficients from deviating too far from the reference coefficients. In the first approach, the distance of the adaptive filter coefficients from the reference coefficients is determined, and the norm of the adaptive filter coefficient vector is clamped to prevent the distance from exceeding a preset threshold. In the second approach, a cost function is used in the adaptation to penalize excessive deviation of the adaptive filter coefficients from the reference coefficients.
Adaptation with Clamp: The feedback cancellation uses LMS adaptation to adjust the FIR filter that models the feedback path (FIGS. 3 and 7 illustrate the LMS adaptation). The processing is most conveniently implemented in block time domain form, with the adaptive coefficients updated once for each block of data.
Conventional LMS adaptation adapts the filter coefficients wk(m) over the block of data to minimize the error signal given by                               ϵ          ⁡                      (            m            )                          =                                            ∑                              n                =                0                                            N                -                1                                      ⁢                          xe2x80x83                        ⁢                                          e                n                2                            ⁡                              (                m                )                                              =                                    ∑                              n                =                0                                            N                -                1                                      ⁢                          xe2x80x83                        ⁢                          (                                                                    (                                                                  [                                                                                                            s                              n                                                        ⁡                                                          (                              m                              )                                                                                -                                                                                    v                              n                                                        ⁡                                                          (                              m                              )                                                                                                      ]                                            )                                        )                                    2                                ,                                                                        (        1        )            
where sn(m) is the microphone input signal and vn(m) is the output of the FIR filter modeling the feedback path for data block m, and there are N samples per block. The LMS coefficient update is given by                                                         w              k                        ⁡                          (                              m                +                1                            )                                =                                                    w                k                            ⁡                              (                m                )                                      +                          2              ⁢                              xe2x80x83                            ⁢              μ              ⁢                                                ∑                                      n                    =                    0                                                        N                    -                    1                                                  ⁢                                  xe2x80x83                                ⁢                                                                            e                      n                                        ⁡                                          (                      m                      )                                                        ⁢                                                            g                                              n                        -                        k                                                              ⁡                                          (                      m                      )                                                                                                          ,                            (        2        )            
where gnxe2x88x92k(m) is the input to the adaptive filter, delayed by k samples, for block m.
In general, one wants the tightest bound on the adaptive filter coefficients that still allows the system to adapt to expected changes in the feedback path such as those caused by the proximity of a telephone handset. The bound is needed to prevent coloration artifacts or temporary instability in the hearing aid which can often result from unconstrained growth of the adaptive filter coefficients in the presence of a sinusoidal or narrow band input signal. The measurements of the feedback path indicate that the path response changes by about 10 dB in magnitude when a telephone handset is placed near the aided ear, and that this relative change is independent of the type of earmold used. The constraint on the norm of the adaptive filter coefficients can thus be expressed as                                                                         ∑                                  k                  =                  0                                                  K                  -                  1                                            ⁢                              xe2x80x83                            ⁢                              "LeftBracketingBar"                                                                            w                      k                                        ⁡                                          (                      m                      )                                                        -                                                            w                      k                                        ⁡                                          (                      0                      )                                                                      "RightBracketingBar"                                                                    ∑                                  K                  =                  0                                                  K                  -                  1                                            ⁢                              xe2x80x83                            ⁢                              "LeftBracketingBar"                                                      w                    k                                    ⁡                                      (                    0                    )                                                  "RightBracketingBar"                                               less than           γ                ,                            (        3        )            
where wk(m) are the current filter coefficients, Wk(0) are the filter coefficients determined during initialization in the hearing aid dispenser""s office, the FIR filter consists of K taps, and xcex3xcx9c2 to give the desired headroom above the reference condition. The clamp given by Eq (3) allows the adaptive filter coefficients to adapt freely when they are close to the initial values, but prevents the filter coefficients from growing beyond the clamp boundary.
Adaptation with Cost Function: The cost function algorithm minimizes the error signal combined with a cost function based on the magnitude of the adaptive coefficient vector:                                           ϵ            ⁡                          (              m              )                                =                                                    ∑                                  n                  =                  0                                                  N                  -                  1                                            ⁢                              xe2x80x83                            ⁢                                                [                                                                                    s                        n                                            ⁡                                              (                        m                        )                                                              -                                                                  v                        n                                            ⁡                                              (                        m                        )                                                                              ]                                2                                      +                          β              ⁢                                                ∑                                      k                    =                    0                                                        K                    -                    1                                                  ⁢                                  xe2x80x83                                ⁢                                                      [                                                                                            w                          k                                                ⁡                                                  (                          m                          )                                                                    -                                                                        w                          k                                                ⁡                                                  (                          0                          )                                                                                      ]                                    2                                                                    ,                            (        4        )            
where xcex2 is a weighting factor. The new constraint is intended to allow the feedback cancellation filter to freely adapt near the initial coefficients, but to penalize coefficients that deviate too far from the initial values.
The LMS coefficient update for the cost function algorithm is given by                                           w            k                    ⁡                      (                          m              +              1                        )                          =                                            w              k                        ⁡                          (              m              )                                -                      2            ⁢                          xe2x80x83                        ⁢            μ            ⁢                          xe2x80x83                        ⁢                          β              ⁢                              xe2x80x83                            [                                                                    w                    k                                    ⁡                                      (                    m                    )                                                  -                                                      w                    k                                    ⁡                                      (                    0                    )                                                              ]                                +                      2            ⁢                          xe2x80x83                        ⁢            μ            ⁢                          xe2x80x83                        ⁢                                          ∑                                  n                  =                  0                                                  N                  -                  1                                            ⁢                              xe2x80x83                            ⁢                                                                    e                    n                                    ⁡                                      (                    m                    )                                                  ⁢                                                                            g                                              n                        -                        k                                                              ⁡                                          (                      m                      )                                                        .                                                                                        (        5        )            
The modified LMS adaptation uses the same cross correlation operation as the conventional algorithm to update the coefficients, but combines the update with an exponential decay of the coefficients toward the initial values. At low input signal or cross correlation levels the adaptive coefficients will tend to stay in the vicinity of the initial values. If the magnitude of the cross correlation increases, the coefficients will adapt to new values that minimize the error as long as the magnitude of the adaptive coefficients remains close to that of the initial values. However, large deviations of the adaptive filter coefficients from the initial values are prevented by the exponential decay which is constantly pushing the adaptive coefficients back towards the initial values. Thus the exponential decay greatly reduces the occurrence of processing artifacts that can result from unbounded growth in the magnitude of the adaptive filter coefficients.
A need remains in the art for apparatus and methods to eliminate xe2x80x9cwhistlingxe2x80x9d in unstable hearing aids while providing an accurate estimate of the feedback path.
The present invention comprises a new approach to improved feedback cancellation in hearing aids. The approach adapts a first filter that. models the quickly varying portion of the hearing aid feedback path, and adapts a second filter that is used either as a reference filter for constrained adaptation or to model more slowly varying portions of the feedback path. The first filter that models the quickly varying portion of the feedback path is adaptively updated on a continuous basis. The second filter is updated only when the hearing aid signals indicate that an accurate estimate of the feedback path can be obtained. Changes in the second filter are then monitored to detect changes in the hearing aid feedback path.
An audio system, such as a hearing aid, according to the present invention, comprises a microphone or the like for providing an audio signal, feedback cancellation means which includes means for estimating a physical feedback signal of the audio system and means for modelling a signal processing feedback signal to compensate for the estimated physical feedback signal, an adder connected to the microphone and the output of the feedback cancellation for subtracting the signal processing feedback signal from the audio signal to form a compensated audio signal, audio system processing means, connected to the output of the subtracting means, for processing the compensated audio signal, and means for estimating the condition of the audio signal and generating a control signal based upon the condition estimate. The feedback cancellation means forms a feedback path from the output of the audio system processing means to the input of the subtracting means and includes a reference filter and a current filter, wherein the reference filter varies only when the control signal indicates that the audio signal is suitable for estimating physical feedback, and wherein the current filter varies at least when the control signal indicates that the signal is not suitable for estimating physical feedback.
In some embodiments, the current filter varies more frequently than the reference filter, usually continuously. This occurs in embodiments wherein the feedback signal is filtered through the current filter and the current filter is constrained by the reference filter.
The current filter may only be adapted when the control signal indicates that the signal is not suitable for estimating physical feedback, in embodiments wherein the feedback signal is filtered through the current filter and the reference filter, and the current filter represents a deviation applied to the reference filter.
Frequently the means for estimating the condition of the audio signal comprises means for detecting whether the signal is broadband, and the reference filter varies only when the control signal indicates that the signal is broadband. For example, the audio system processing means computes the signal spectrum of the audio signal, the means for estimating computes the ratio of the minimum to the maximum input power spectral density and generates a control signal based upon the ratio,and the control signal indicates the audio signal is suitable when the ratio exceeds a predetermined threshold. As another example, the audio system processing means computes the correlation matrix of the audio signal, the means for estimating computes the condition number of the correlation matrix and generates a control signal based upon the condition number, and the control signal indicates the audio signal is suitable when the condition number falls below a predetermined threshold.
In the preferred embodiment, the reference filter is monitored to detect significant changes in the feedback path of the audio system. Also, constraining means prevents the current filter (or the reference filter combined with the deviation filter) from deviating excessively from the reference filter.