 |
Description  |
|
|
BACKGROUND OF THE INVENTION
The invention relates to hearing aids, and more particularly relates to
hearing aids which can be used in noisy environments.
It has long been known to vary the characteristics of a hearing aid in
accordance with the ambient noise level so as to make it easier for the
patient to distinguish between useful information (i.e. speech) and noise.
One system for accomplishing this objective varies the gain at low
frequencies when the incoming low frequency energy (which is assumed to be
mostly noise) exceeds some predetermined quantity. It has also been
proposed to divide the incoming signal into a plurality of frequency bands
and to adjust the audio gain for each band in dependance upon the
signal-to-noise ratio in that band. Thus, where the signal-to-noise ratio
in a particular band is poor, the gain in that band is cut back. Yet
another approach involves the formation of a raw estimate of noise level,
in which noise is estimated only during pauses between speech sounds.
One problem with these approaches is that they do not take full account of
the known effects of noise on speech intelligibility. It has long been
known that the effects of low-frequency noise are not restricted to
low-frequency speech information. Rather, low-frequency noise also reduces
the intelligibility of higher--frequency speech information. While this
phenomenon--known as the upward spread of masking effect--is generally
applicable to all human beings, it is more severe in hearing-impaired
individuals.
It would be advantageous to produce a hearing aid signal processing system
which took the phenomenon of spread of masking into account and corrected
for it.
SUMMARY OF THE INVENTION
One object of the invention is to provide a hearing aid signal processing
system which takes actual account of spread of masking caused by noise.
Another object is to provide such a system which continuously estimates the
absolute quantity of noise in incoming audio information rather than using
artificial quantities such as signal-to-noise ratios.
Another object is to generally improve on known and proposed signal
processing systems for hearing aids.
In accordance with the invention, audio information (such as human speech
contaminated by ambient noise) is converted into an electrical signal and
this electrical signal is classified (preferably but not necessarily using
digital techniques) into a plurality of non-overlapping frequency bands.
In further accordance with the invention, the absolute quantity of noise
in each frequency band is determined independently, and the gain of each
frequency band except for the highest frequency band is adjusted in
accordance with the noise so determined. Advantageously, the gain in the
next-highest frequency band is adjusted first, the gain of the next lower
frequency band is adjusted next, and all of the gains are adjusted from
next-highest to lowest.
In further accordance with the invention, the absolute quantity of noise in
each frequency band is estimated based on an amplitude histogram.
BRIEF DESCRIPTION OF THE DRAWINGS
Exemplary and non-limiting preferred embodiments of the invention are shown
in the drawings, in which:
FIG. 1 is an overall block diagram of a preferred embodiment of the
invention;
FIG. 2 is a more detailed block diagram of a portion of the preferred
embodiment;
FIG. 3 is a still more detailed block diagram of a portion of the preferred
embodiment;
FIG. 4 illustrates the classification of incoming sound events by frequency
band and amplitude bin in accordance with the preferred embodiment;
FIG. 5 is a flowchart illustrating the process by which the absolute
quantity of noise is determined in each frequency band;
FIG. 6 is a flowchart illustrating the algorithm by which the preferred
embodiment corrects for upward spread of masking of noise;
FIG. 7 illustrates the results produced by the preferred embodiment in
three different situations;
FIG. 8 illustrates the concept of compression gain; and
FIG. 9 is a flowchart illustrating the method by which compression gain is
calculated in the preferred embodiment.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENT
An overall block diagram of a preferred embodiment of the invention will
first be discussed in connection with FIG. 1. A microphone 2 converts
audio information, which in this instance is assumed to be human speech
contaminated by noise, into electrical signals. In the preferred
embodiment, these signals are digitized, and to permit this, these signals
are sampled, in the preferred embodiment, at the rate of 15 kHz. (This
sampling rate is chosen for convenience, and is not part of the
invention.) The sampled signals are then directed through an anti-aliasing
filter 4. The anti-aliasing filter 4 is a low-pass filter which begins
attenuating at approximately 6000 Hz and has a attenuation rate of 18
dB/octave, but these characteristics are not part of the invention. The
function of the anti-aliasing filter 4 is to exclude high-frequency
signals that cannot be properly represented in the digital system from
passing through the rest of the circuitry. Persons skilled in the art are
familiar with filters of this type, and the details of the filter 4 will
therefore not be further discussed.
After passing through the filter 4 the sampled and filtered signals are
input to an analog-to-digital converter 6. In the preferred embodiment,
this has a digital resolution of 14 bits, but this is not part of the
invention and more or fewer (presently, a minimum of 12 bits are believed
to be required but this is not a part of the invention) bits may be used.
The digitized signals from the converter 6 are then routed through a
buffer memory 8 which accumulates enough information for subsequent signal
analysis and signal processing to take place. In the preferred embodiment,
the buffer memory 8 holds a block of 16 samples, which corresponds to
about 1 mS of information, but this is not part of the invention.
In the preferred embodiment, these signal analysis and signal processing
operations are carried out on a Texas Instruments digital signal
processing chip TMS 32020, but this is not part of the invention. The
signal analysis and signal processing will be discussed in more detail
below; for now, the signal analysis and processing functions will be
considered to be performed by polyphase signal processor 10.
After the analysis and signal processing have taken place in processor 10,
the output information is directed to a buffer memory 12 which accumulates
enough information so that the output signal can be converted back to
analog form for audio reproduction to the patient. This conversion is
carried out in a digital-to-analog converter 14, which is connected to a
smoothing filter 16. The smoothing filter 16 is a low-pass type which is
known to persons skilled in the art; it smooths out the gaps between
successive digitized audio information and in the preferred embodiment has
a slope of +18 dB/octave. The details of the smoothing filter 16 will
therefore not be described. From the smoothing filter, the information is
routed to a speaker 18 which converts the analog signal into audio
information.
FIG. 2 shows the functioning of processor 10 in more detail. While FIG. 1
and subsequent Figures and text illustrate the invention as if individual
parts of the processor 10 were embodied in hardware, most of these are
actually embodied in software and implemented by the above-mentioned Texas
Instruments chip. This is, however, not a part of the invention, and the
implementation of various elements as software functions is merely for
convenience.
In the preferred embodiment, the signals from the analog-to-digital
converter 6 are subjected to multichannel frequency analysis and
classified into a plurality of non-overlapping frequency bands in analysis
section 10A. (In this example, there are 8 such bands, but this is not
part of the invention and another number of bands could be used instead.)
Within each such band, the quantity of noise is estimated in noise
estimation section 10B as described below, and the gain of the band
computed in gain computation section 10C as described below. (The audio
gain for the highest frequency band is computed differently from the other
bands; this is explained below.) The appropriate audio gain is then
applied to signals in each of the frequency bands by gain control elements
in gain control section 10D. Then, the output signals from the gain
control section 10D are summed in synthesis section 10E, which is
connected to the buffer memory 12.
The signal processing operations which are performed in each of the seven
lowest frequency bands will now be described in connection with FIG. 3.
Initially, the absolute value of the incoming signal is computed in stage
20 and converted to a five bit base 2 logarithm in stage 22. The
resolution of this conversion is 0.1875 dB. This is chosen for convenience
and is not part of the invention; it comes from dividing 6 dB (the width
of each amplitude bin discussed hereinafter) by 32 (2.sup.5). The noise in
the signal is then estimated in stage 24 as described below and (for all
frequency bands except the highest one) suppressed in stage 26 in
accordance with an algorithm which is described below. At the same time,
the signal is averaged in stage 28 and stage 30 to compute (as described
below) the compression gain and noise suppression which is applied to
signals within the frequency band to compensate for the patient's hearing
deficiencies. This modified signal is then routed through an equalization
gain stage 32 which takes the acoustic characteristics of the ear into
account so as to avoid unnecessary coloration of the incoming speech.
Thereafter, the base 2 anti-logarithm of the equalized signal is computed
in stage 34 and used to vary the overall audio gain which is applied to
all signals in the frequency band. In the highest frequency band, as is
described below, there is no noise suppression, so stage 26 is absent from
the operation of the processor 10 on the highest frequency band. However,
compression gain and equalization gain for the highest frequency band are
computed and FIG. 3 is therefore otherwise accurate as applied to the
highest frequency band.
In the following description, the method by which noise is estimated will
be discussed in connection with FIGS. 4 and 5 and the method by which the
noise is suppressed will be discussed in connection with FIGS. 6 and 7.
As information in the form of digitized sound events comes into the
processor 10, each sound event is classified. The classification is by
frequency and by amplitude. The classification scheme used by the
preferred embodiment is illustrated in FIG. 4. As is shown there, the
frequency spectrum between 0 and 6800 Hz is advantageously divided into 8
frequency bands (band 0 through band 7, with band 7 including the highest
frequencies) and each frequency band is advantageously divided into 8
amplitude bins (bin 0 through bin 7, with bin 7 including the loudest
amplitudes). Thus, if a particular sound has a frequency of 1000 Hz and an
amplitude of 42 dB, the event will be classified in band 2, bin 2. It will
be understood that there may be more or fewer frequency bands and
amplitude bins, that the number of frequency bands and amplitude bins need
not be the same, and that the bands may have different frequency
boundaries than are shown. These quantities were chosen for convenience
and are not part of the invention. FIG. 4 also shows the audiometric
frequency which is associated with each band. These audiometric
frequencies are not part of the invention, but they are commonly used by
audiologists to measure hearing loss at various parts of the frequency
spectrum. The audiometric frequencies have been illustrated merely to
emphasize the correspondence between the eight preferred frequency bands
and the audiometric frequencies which are conventionally used to estimate
hearing loss.
In accordance with the invention, the absolute quantity of noise in each
band is estimated in accordance with known characteristics of human
speech. The amplitude histogram of noise-contaminated human speech is
known to have two peaks, one above the other, the lower peak representing
noise and the higher-amplitude peak representing speech plus noise.
In the preferred embodiment, "noise" is defined for each frequency band
individually by classifying all incoming sound events into amplitude bins
and constructing a cumulative amplitude histogram from which an electrical
definition of "noise" is derived. Then, the absolute quantity of noise in
each frequency band is determined using this definition. The definition is
continuously updated so that changing noise conditions do not interfere
with the operation of the signal processing system.
To determine the current definition of noise, incoming audio information is
classified into frequency bands and into amplitude bins within each
frequency band as is shown in FIG. 4.
Thus, in the preferred embodiment, the system is initialized for a short
period which may be as little as one second to as much as 4 seconds, with
four seconds being presently preferred and, for each frequency band, a
cumulative amplitude histogram is created and then repeatedly decayed at
closely spaced intervals (as for example by multiplying the contents of
the bin registers by a constant which is less than one). The decay of the
histogram is required because the histogram should ideally represent, in
real time, the current incoming audio information. If the histogram were
to merely be updated as new sound events entered the system, the
estimation of current noise levels would be skewed because of previous
noise levels which are obsolete when compared with current real-time
conditions.
As a new sound event enters the frequency band, it is classified into the
appropriate amplitude bin. When the event is of an amplitude which is
lower than the softest perceivable speech, the event is treated as if it
were the softest perceivable spoken sound. (This has the consequence that
such low-amplitude sound events are always treated as noise.)
When the average signal level is lower than the maximum amplitude
encompassed by bin 7, the sound event is considered to have the potential
of containing either useful speech information or noise, and it is added
to the existing cumulative histogram of amplitudes in the frequency band.
It is assumed on an a priori basis that the bins of the cumulative
histogram which encompass the lowest amplitude events contain noise. In
this preferred embodiment, this assumption is implemented by defining as
noise all sound events which are included in those bins which account for
at least the lowest 40% of the amplitudes in the cumulative histogram.
Thus, if there are a total of 1000 events in a particular cumulative
histogram at a particular time, and 400 events are located in bins 0
through 3, it is assumed that all the information in bins 0 through 3 is
noise and that all information in bins 4 through 7 contains information
which may relate to speech and which may therefore be useful for speech
perception.
In practice, this process is carried out by starting at bin 0 and then
progressively going to higher bins until the requisite 40% (or more)
figure is reached. For example, let it be assumed that the cumulative
histogram for a particular frequency band contains 1000 events, of which
100 are classified in bin 0, 200 in bin 1, 300 in bin 2, and 80 each in
bins 3-7. The noise threshold must contain the lowest 40% of the events in
the histogram or 400 events. Bin 0 contains 100 events, which is less than
400. Bin 1 plus bin 0 contains 300 events, which is likewise less than 400
events. Bin 2 plus bin 1 plus bin 0 contains 600 events, which is equal
to, or more than 400 events. Therefore, all events in bins 2, 1 and 0 are
considered to be noise and the information in the other bins is considered
to be potentially useful for speech perception.
Thus, where a particular sound event has an amplitude which corresponds to
bin 2, in this example, the event is considered to be noise and the noise
level is updated.
If the event is binned above bin 7, the event may or may not be noise, so a
determination is made whether the average signal (measured over a period
which is advantageously but not necessarily 50 mS) is louder than the
highest amplitude in bin 7. If the average signal is louder than the
highest amplitude in bin 7, the entire frequency band is assumed to
contain noise alone, and the level of noise in the frequency band is
assumed to be equal to the signal level. Thus, the noise identification
procedure used in the preferred embodiment has a feature that extremely
loud and prolonged sounds are immediately interpreted as noise and it is
unnecessary to go through the process by which the noise level is normally
determined.
In summary, the absolute quantity of noise in each of the frequency bands
is individually and continuously estimated, based on the cumulative
distribution of amplitudes in each band. This absolute quantity of noise
is used to vary the gains in each of the frequency bands, and this
variation will be described next.
In the preferred embodiment, audio gain in each individual frequency band
is determined not by the amount of noise in that band alone but rather on
the masking effect that such noise has on higher bands. Thus, where
excessive noise in band 6 can decrease the intelligibility of speech in
band 7, the audio gain in band 6 is cut back to a level at which the band
6 noise is not more than 3 dB louder than the noise in band 7 (or is cut
back to the amplitude of the softest perceivable speech sounds in band 6,
if this reduction is smaller). Then, the gain in the next lower band,
namely band 5, can be adjusted if this is necessary to prevent the noise
in band 5 from interfering with the intelligibility of speech sounds in
band 6.
The flowchart in FIG. 6 will make this clearer. It will be noted that the
audio gain of the highest band, namely band 7, is never adjusted to
compensate for noise. This is because band 7 is assumed to contain the
highest frequency speech sounds and noise in this band has no effect on
intelligibility of higher-frequency speech sounds. (The gain of band 7 is
adjusted to compensate for the patient's hearing deficiencies, as
described below.)
Beginning in this example with band 6 as the current band, the
determination is first made whether the noise level in the next higher
band (here band 7) is softer than the softest perceivable speech sound in
band 7. If so, the noise level is treated as if it is as loud as the
softest speech sounds which can be perceived in band 7. (This is not an
essential part of the invention, but is done for convenience, as is
explained below.)
The decision is then made whether noise in the current band, here band 6,
less a threshold which in this example is 3 dB, is louder than the noise
in the next higher band, here band 7. If not, there is insufficient noise
in band 6 to cause intelligibility problems in band 7 and the gain in band
6 need not be, and is not, adjusted. However, if the noise in the current
band, less the 3 dB threshold, is louder than the noise in the next higher
band, i.e. band 7, the noise level in band 6 is such as to interfere with
the intelligibility of speech sounds in band 7. In this case, the gain in
band 6 is reduced enough so that the noise in band 6, less 3 dB, is not
louder than the noise in band 7.
This process is then repeated by setting band 5 as the current band and
repeating the same process of comparing the noise in the current band (now
band 5) with the noise in the next higher band (including any gain
adjustment) (here, band 6). The same process is then repeated for
successively lower bands until the noise in band 0 has been evaluated and
the gain in band 0 adjusted if necessary.
The effect of this algorithm are illustrated in FIG. 7. In Example 1, the
noise in each frequency band is far below the amplitude of the softest
perceivable speech sounds and therefore cannot affect intelligibility of
speech sounds in any band. Therefore, no audio gain of any band is reduced
to compensate for the effects of noise. This comes about because, for
purposes of signal processing, the noise in each band is treated as if it
were at the same loudness as the softest perceivable speech sounds in the
band. Therefore, it is unimportant that, for example, the noise in band 2
exceeds the noise in band 3 by more than 3 dB. In short, noise levels
which are below the softest perceivable speech sounds are treated as if
they were all equally inaudible and only audible noise levels, i.e. noises
which are louder than such sounds, are reduced. The reduction takes place
only until the relevant noise level reaches the threshold of audibility.
In Example 2, the noise in each of the frequency bands 5, 6 and 7 is softer
than the softest Perceivable speech sounds in those bands so the audio
gains of frequency bands 5, 6 and 7 are not reduced. However, the noise
level in band 4 is more than 3 dB louder than the noise in band 5 and will
therefore cause masking in band 5. Accordingly, the audio gain applied to
signals in frequency band 4 is reduced. It will be noted that there is
more than 3 dB difference between the output (processed) noise levels in
bands 4 and 5, but the audio gain of band 4 is only cut back to the point
where the noise in band 4 becomes as loud as the softest speech sounds
which are perceivable in band 4. Further reduction is unnecessary. The
process then continues for bands 3, 2, 1 and 0. In each case, the noise
level in the band exceeds the noise in the next higher band by more than 3
dB, and the audio gain applied to signals in the frequency band is then
cut back.
In Example 3, it will be noted that the noise in band 6 is less than the
noise in band 7. As a result, the gain in band 6 is left unchanged. In all
remaining bands, the noise level in the current band exceeds the (reduced)
noise level in the next higher band by more than 3 dB, and the audio gain
in the current band is reduced to eliminate the effect of masking.
The threshold value of 3 dB is not a part of the invention. This value is
set at 3 dB because this has been determined empirically to be the value
which gives the least tonal coloration to the processed sound. However,
the threshold may be set higher or lower.
The preferred embodiment is designed to improve the intelligibility of
noise-contaminated speech and also to correct for the particular hearing
impairments of the user. This latter correction is carried out in all
frequency bands, and not only in the seven lowest ones.
In the simplest case, it would be possible to measure the degree of hearing
loss in each frequency band and to apply an appropriate amplification to
signals in each frequency band so as to compensate for that loss. However,
this solution is unsatisfactory. Whereas this would provide adequate
results for average speech levels, softer speech sounds might not be
sufficiently amplified so as to be made perceivable by the patient, and
louder speech sounds might be amplified to an uncomfortable degree.
For this reason, the preferred embodiment of the invention utilizes an
adaptive amplification system which adjusts the gain applied to sound
events in a particular frequency band in accordance with the amplitude of
such events. The principle of this adaptive amplification is illustrated
in FIG. 8.
FIG. 8 shows three individual cases in a particular frequency band of
interest. Where the patient's hearing is unimpaired in frequency band of
interest, there is a linear relationship between input and output and no
adjustment of gain is necessary or desirable. For a patient with a loss of
50 dB in the frequency band of interest, softer speech sounds are
amplified more than are louder speech sounds. Thus, the softest
perceivable speech sounds are amplified by 50 dB and the loudest speech
sounds of perhaps 100 dB are not amplified at all. In the most extreme
case illustrated, which is a loss of 80 dB in the frequency band of
interest, the variation in amplification is much greater; soft sounds are
amplified by as much as 80 dB while loud sounds of e.g. 100 dB are not
amplified at all.
In accordance with the preferred embodiment of the invention, an
audiologist measures the patient's hearing loss in each of the frequency
bands and appropriate gain vs. amplitude response curves are derived and
programmed into the stage 30. Then, the audio gain applied to the
processed signals in each frequency band is adjusted in accordance with
the flowchart illustrated in FIG. 9. In this method, the average signal is
continuously computed over a time constant of 50 mS. If a particular sound
event is substantially louder than the ambient signal level (in this case
more than 10 dB above the average signal level) the amplitude of the sound
event less the 10 dB threshold is used as the basis for computation of the
gain to be applied. Where the instantaneous signal is not much greater
than the average signal, the average signal is used as the basis for
computing gain.
Those skilled in the art will understand that changes can be made in the
preferred embodiments here described, and that these embodiments can be
used for other purposes. Such changes and uses are within the scope of the
invention, which is limited only by the claims which follow.
* * * * *
|
|
 |
|
 |
Description |
|
