Electrical audio signal processing systems and devices – Hearing aids – electrical
Reexamination Certificate
1999-06-02
2001-09-18
Tran, Sinh (Department: 2643)
Electrical audio signal processing systems and devices
Hearing aids, electrical
C381S316000, C381S071120, C381S094100
Reexamination Certificate
active
06292571
ABSTRACT:
BACKGROUND OF THE INVENTION
This invention generally relates to hearing aid digital filters, and more particularly, is directed to a hearing aid digital filter with reduced power consumption and dissipation.
Digital Signal Processors (DSP), which are known to be used in hearing aid systems hitherto, have emphasized speed and performance, and have been traditionally of a design which requires significant amounts of power. Significant power consumption in DSPs is basically because of the components and the manner of interconnection thereof.
The present invention is directed to a DSP for a hearing aid where the power consumption is minimized with consequent advantages of economy and size without sacrificing performance.
In a conventional hearing aid, a microphone converts incident sound waves into an analog electrical signal which is then processed to filter out unwanted noise etc., amplified, and coupled to a receiver or speaker which converts the electrical signal back to sound waves. The electrical signal processor may be an analog processor which operates directly upon an analog electrical signal. Alternatively, the analog signal may be converted to a digital signal and processed by a digital signal processor (DSP).
Most hearing aids now-a-days use analog signal processing. Signal processing schemes include frequency-independent linear amplification, frequency-compensated linear amplification (typically boosting the high frequencies), frequency-independent automatic gain control (AGC) systems, and finally signal level dependent, frequency compensated systems. This final class of hearing aids includes signal processing algorithms that the hearing aid community labels 2-channel systems, 3-channel systems, and multi-channel systems. These systems split the audio frequency band up into two or more sections and can control the gain of each section independently from one another. The K-Amps® circuits from Etymotic Research and the DynamEQ-I® circuits from Gennum Corporation are examples of traditional 2-channel systems.
The DynamEQ-I® has two amplifier sections, one processes the low frequencies and the other processes the high frequencies. This, in the traditional sense, is a two-channel system. The K-Amp contains only one amplifier to shape the frequency response as a function of input level. Both of these circuits implement a first order analog filter although in different ways. Therefore, one must be careful when using the term 2-channel system. A description of the complex-frequency transfer function (written in the s-domain) is a more appropriate way to describe analog filters.
The accuracy and repeatability of analog filters depend on the tolerances of physical components (e.g., resistors and capacitors). These components have initial tolerances, which vary with temperature, and can also vary with humidity, voltage, and age. It can be quite a challenge to design an analog filter to meet its intended requirements when operating over a range of environmental conditions.
Digital filters on the other hand are implemented with digital electronics that manipulate numbers. Digital filters are described by algorithms, and are represented mathematically in the z-domain. The repeatability of the digital filter from circuit to circuit depends only on the accuracy of the sampling frequency. This sampling frequency is usually derived from a crystal-controlled oscillator with a typical accuracy of 0.01 percent. The precision of the oscillator frequency is much better than the typical 1%, 5%, and 10% tolerances of resistors and capacitors used in analog filters. The accuracy, distortion, and noise characteristics of the digital filter depend on the precision with which the signal and filter coefficients are represented.
There exist several prior art US patents which relate to hearing aid technology.
U.S. Pat. No. 4,803,732, entitled Hearing Aid Amplification Method and Apparatus, to Dillon, recognizes different audiofrequency bands and handles them independently. The different frequency bands are independently amplified to match the needs and loss pattern of hearing of the user. Prior to amplification, the microphone signal is passed through an optional filter. Thus, signal discrimination for the user is increased.
U.S. Pat. No. 4,791,672 to Nunley et al is directed to a hearing aid which includes a wearable, programmable digital signal processor for processing digital samples of analog signals in real time. A hearing aid program stored in a signal processor is continuously executed, introducing noise suppression.
Prior art is replete with US patents which are, in one way or the other, directed to noise suppression in different degrees, using different arrangements or techniques. Examples of such prior art include US patents: U.S. Pat. No. 5,794,187 to Franklin, et al., U.S. Pat. No. 5,651,071 to Lindemann, et al., U.S. Pat. No. 4,185,168 to Graupe, et al., U.S. Pat. No. 5,306,560 to Arcos, et al., 5,259,033 to Goodings, et al, and U.S. Pat. No. 5,412,735 to Engebretson, et al. Generally, the aforesaid patents are directed to achieving clarity of hearing or enhanced hearing for the user without any reference to the power consumed in the amplifier and associated circuitry.
A general method for describing the action of a digital filter on a digital signal is the Z-transform method.
Z-Transform
A brief discussion of the Z-transform at this juncture is believed to assist in providing a better understanding of the invention.
Let X (k) be a digital signal that is zero for k<0. Its z-transform, denoted by X*(z), is defined to be the function of z:
X
*
(
z
)
=
X
(
0
)
+
X
(
1
)
z
-
1
+
X
(
2
)
z
-
2
+
…
=
∑
k
=
0
∞
X
(
k
)
z
-
k
Because of the shift-multiplication property which is being aimed for, z will have the same interpretation as does a phasor approach. That is, when z is on the unit circle in the complex z-plane, its angle is interpreted as a frequency variable. But the question is not how is z interpreted, but rather how is z defined. The answer is: z is an independent complex variable. It has much the same status as k, the sample number. A signal is defined to be a function of k, while its z-transform is defined to be a function of z. Thus, z is the domain of the z-transform of a signal. In fact, the z-transform can be looked at as a transformation from the time domain to the frequency domain. The following symbolism may be used for the z-transform:
F
(
k
)
→
z
F
*
(
z
)
The symbol over the arrow indicates the name of the transformation. It is noted at this point that a digital filter can be thought of as a transformation in the same way as the z-transform. For example, if X is the input filter to a filter H. and Y is the output, a relationship can be expressed as:
X
(
k
)
→
H
Y
(
k
)
Thus, a filter is a transformation that converts one function of k to another function of k, while the z-transform converts one function of k to another function of z.
Z-transforms have certain properties which make it possible to represent moving average filters and other linear time-invariant filters by multiplication, similar to an approach which deals with phasors. For a one-sided digital input signal, the z-transform of the output signal can be obtained by multiplying the z-transform of the input signal by the transfer function H(z).
Z-transforms have a property which enables easy and free movement from the time domain to the frequency domain. Filtering in the time domain can be looked at as corresponding to multiplication in the z-transform domain. A more complete understanding of the above can be had from
An Introduction to Discrete Systems
by Kenneth Steglitz, Princeton University, John Wiley & Sons, Inc. 1974.
Filter Coefficients:
A digital filter is characterized by a set of real numbers, namely its coefficients. Altering these numbers will alter the characteristics of the filter. To assess the effect of any change in the value of a coefficient on a given filter ch
Hamilton, Brook, Smith & Reynold, P.C.
Ni Suhan
Sarnoff Corporation
Tran Sinh
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