An active system for controlling vibration or sound. The system includes a method for limiting the output gain G from a gradient descent algorithm, such as an LMS algorithm, according to the relationship ##EQU1## and reducing one of first and second filter weights W1 and W0 such that G≦Gmax ensuring the output canceling signal is never saturated. This eliminates the square wave shape which may impart unwanted harmonics or resonances to the dynamic system. In another aspect, the system eliminates drift of the output devices toward saturation when situations are encountered such as singularities in the quadratic performance surface. This is accomplished by applying leakage factor r(k) to a function f(k) at or near a predetermined limit to avoid saturation of the output device. The function f(k) can be the gain G or the weights W(k). Another aspect is to provide a constant, optimum, and stable adaptation rate in an LMS system by providing an optimum adaptation coefficient μopt that provides a constant, uniform, and optimum adaptation rate on a per period basis for all frequencies. Various combinations of the above are described and are useful in active control systems.

Active control of noise and vibration

A hearing compensation system for the hearing impaired comprises a plurality of bandpass filters having an input connected to an input transducer and each bandpass filter having an output connected to the input of one of a plurality of multiplicative AGC circuits whose outputs are summed together and connected to the input of an output transducer. The multiplicative AGC circuits attenuate acoustic signals having a constant background level without the loss of speech intelligibility. The identification of the background noise portion of the acoustic signal is made by the constancy of the envelope of the input signal in each of the several frequency bands. The background noise that will be suppressed includes multi-talker speech babble, fan noise, feedback whistle, fluorescent light hum, and white noise.

Hearing aid device incorporating signal processing techniques

Feedback cancellation apparatus uses a cascade of two filters along with a short bulk delay. The first filter is adapted when the hearing aid is turned on in the ear. This filter adapts quickly using a white noise probe signal, and then the filter coefficients are frozen. The first filter models parts of the hearing-aid feedback path that are essentially constant over the course of the day. The second filter adapts while the hearing aid is in use and does not use a separate probe signal. This filter provides a rapid correction to the feedback path model when the hearing aid goes unstable, and more slowly tracks perturbations in the feedback path that occur in daily use. The delay shifts the filter response to make the most effective use of the limited number of filter coefficients.

Feedback cancellation improvements

A signal enhancement system improves the understandability of speech or other audio signals. The system reinforces selected parts of the signal, may attenuate selected parts of the signal, and may increase SNR. The system includes delay logic, a partitioned adaptive filter, and signal reinforcement logic. The partitioned adaptive filter may track and enhance the fundamental frequency and harmonics in the input signal. The partitioned filter output signals may approximately reproduce the input signal, delayed by an integer multiple of the period of the fundamental frequency of the input signal. The reinforcement logic combines the input signal and the filtered signals to produce an enhanced output signal.

Periodic signal enhancement system

A new subband feedback cancellation scheme is proposed, capable of providing additional stable gain without introducing audible artifacts. The subband feedback cancellation scheme employs a cascade of two narrow-band filters Ai(Z)and Bi(Z)ng with a fixed delay, instead of a single filter Wi(Z)and a delay to represent the feedback path in each subband. The first filter, Ai(Z), is called the training filter, and models the static portion of the feedback path in ith subband, including microphone, receiver, ear canal resonance, and other relatively static parameters. The training filter can be implemented as a FIR filter or as an IIR filter. The second filter, B?I?(Z), is called a tracking filter and is typically implemented as a FIR filter with fewer taps than the training filter. This second filter tracks the variations of the feedback path in the i?th? subband caused by jaw movement or objects close to the ears of the user.

Subband acoustic feedback cancellation in hearing aids

A normalized frequency domain Least Means Square filter. The feedback coefficient for each frequency bin is adjusted separately in the filter by use of an input power estimate. The power estimate is incorporated directly into the filter algorithm as a data-dependent, time-varying stochastic feedback coefficient. The filter is particularly useful in applications in which the input signal has large spectral variations, which could lead to filter instabilities in some frequency bins if a single feedback coefficient were employed in all frequency bins.

Paul L. Feintuch

Papers

Normalized frequency domain LMS adaptive filter