Electrical audio signal processing systems and devices – Noise or distortion suppression – Spectral adjustment
Reexamination Certificate
2000-01-12
2004-06-29
Harvey, Minsun Oh (Department: 2644)
Electrical audio signal processing systems and devices
Noise or distortion suppression
Spectral adjustment
C704S226000, C704S233000
Reexamination Certificate
active
06757395
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to electronic hearing devices and electronic systems for sound reproduction. More particularly the present invention relates to noise reduction to preserve the fidelity of signals in electronic hearing aid devices and other electronic sound systems. According to the present invention, the noise reduction devices and methods utilize digital signal processing techniques.
The current invention can be used in any speech communication device where speech is degraded by additive noise. Without limitation, applications of the present invention include hearing aids, telephones, assistive listening devices, and public address systems.
2. The Background Art
This invention relates generally to the field of enhancing speech degraded by additive noise as well as its application in hearing aids when only one microphone input is available for processing. The speech enhancement refers specifically to the field of improving perceptual aspects of speech, such as overall sound quality, intelligibility, and degree of listener fatigue.
Background noise is usually an unwanted signal when attempting to communicate via spoken language. Background noise can be annoying, and can even degrade speech to a point where it cannot be understood. The undesired effects of interference due to background noise are heightened in individuals with hearing loss. As is known to those skilled in the art, one of the first symptoms of a sensorineural hearing loss is increased difficulty understanding speech when background noise is present.
This problem has been investigated by estimating the Speech Reception Threshold (“SRT”), which is the speech-to-noise ratio required to achieve a 50% correct recognition level, usually measured using lists of single-syllable words. In most cases, hearing impaired people require a better speech-to-noise ratio in order to understand the same amount of information as people with normal hearing, depending on the nature of the background noise.
Hearing aids, which are one of the only treatments available for the loss of sensitivity associated with a sensorineural hearing loss, traditionally offer little benefit to the hearing impaired in noisy situations. However, as is known to those skilled in the art, hearing aids have been improved dramatically in the last decade, most recently with the introduction of several different kinds of digital hearing aids. These digital hearing aids employ advanced digital signal processing technologies to compensate for the hearing loss of the hearing impaired individual.
However, as is known to those skilled in the art, most digital hearing aids still do not completely solve the problem of hearing in noise. In fact, they can sometimes aggravate hearing difficulties in noisy environments. One of the benefits of modern hearing aids is the use of compression circuitry to map the range of sound associated with normal loudness into the reduced dynamic range associated with a hearing loss. The compression circuitry acts as a nonlinear amplifier and applies more gain to soft signals and less gain to loud signals so that hearing impaired individuals can hear soft sounds while keeping loud sounds from becoming too loud and causing discomfort or pain. However, one of the consequences of this compression circuitry is to reduce the signal-to-noise ratio (“SNR”). As more compression is applied, the signal-to-noise ratio is further degraded. In addition, amplification of soft sounds may make low-level circuit noise audible and annoying to the user.
As is known to those skilled in the art, the general field of noise reduction, i.e., the enhancement of speech degraded by additive noise, has received considerable attention in the literature since the mid-1970s. The main objective of noise reduction is ultimately to improve one or more perceptual aspects of speech, such as overall quality, intelligibility, or degree of listener fatigue.
Noise reduction techniques can be divided into two major categories, depending on the number of input signal sources. Noise reduction using multi-input signal sources requires using more than one microphone or other input transducer to obtain the reference input for speech enhancement or noise cancellation. However, use of multi-microphone systems is not always practical in hearing aids, especially small, custom devices that fit in or near the ear canal. The same is true for many other small electronic audio devices such as telephones and assistive listening devices.
Noise reduction using only one microphone is more practical for hearing aid applications. However, it is very difficult to design a noise reduction system with high performance, since the only information available to the noise reduction circuitry is the noisy speech contaminated by the additive background noise. To further aggravate the situation, the background may be itself be speech-like, such as in an environment with competing speakers (e.g., a cocktail party).
Various noise reduction schemes have been investigated, such as spectral subtraction, Wiener filtering, maximum likelihood, and minimum mean square error processing. Spectral subtraction is computationally efficient and robust as compared to other noise reduction algorithms. As is known to those skilled in the art, the fundamental idea of spectral subtraction entails subtracting an estimate of the noise power spectrum from the noisy speech power spectrum. Several publications concerning spectral subtraction techniques based on short-time spectral amplitude estimation have been reviewed and compared in Jae S. Lim & Alan V. Oppenheim, “
Enhancement and Bandwidth Compression of Noisy Speech
,” P
ROC
. IEEE, Vol. 67, No. 12, pp. 1586-1604, December 1979.
However, as is known to those skilled in the art, there are drawbacks to these spectral subtraction methods, in that a very unpleasant residual noise remains in the processed signal (in the form of musical tones), and in that speech is perceptually distorted. Since the review of the literature mentioned above, some modified versions of spectral subtraction have been investigated in order to reduce the residual noise. This is described in S
AEED
V. V
ASEGHI
, A
DVANCED
S
IGNAL
P
ROCESSING AND
D
IGITAL
N
OISE
R
EDUCTION
(John Wiley & Sons Ltd., 1996).
According to these modified approaches, the noisy received audio signal may be modeled in the time domain by the equation:
x
(
t
)=
s
(
t
)+
n
(
t
),
where x(t), s(t) and n(t) are the noisy signal, the original signal, and the additive noise, respectively. In the frequency domain, the noisy signal can be expressed as:
X
(ƒ)=
S
(ƒ)+
N
(ƒ),
where X(ƒ), S(ƒ), and N(ƒ) are the Fourier transforms of the noisy signal, of the original signal, and of the additive noise, respectively. Then, the equation describing spectral subtraction techniques may be generalized as:
|
Ŝ
(ƒ)|=|
H
(ƒ)|·|
X
(ƒ)|,
where |S{circumflex over ( )}(ƒ)| is an estimate of the original signal spectrum |S(ƒ)|, and |H(ƒ)| is a spectral gain or weighting function for adjustment of the noisy signal magnitude spectrum. As is known to those skilled in the art, the magnitude response |H(ƒ)| is defined by:
|
H
(ƒ)|=
G
(
R
(ƒ))=[1−&mgr;(
R
(ƒ))
&agr;
]
&bgr;
,
R
(
f
)
=
&LeftBracketingBar;
N
^
(
f
)
&RightBracketingBar;
&LeftBracketingBar;
X
(
f
)
&RightBracketingBar;
,
where N{circumflex over ( )}(ƒ) is the estimated noise spectrum. Throughout this document, the signal-to-noise ratio (“SNR”) is defined as the reciprocal of R(ƒ). For magnitude spectral subtraction techniques, the exponents used in the above set of equations are &agr;=1, &bgr;=1, &mgr;=1, and for power spectral subtraction techniques, the exponents used are &agr;=2, &bgr;=0.5, &mgr;=1. The pa
Fang Xiaoling
Nilsson Michael J.
Pendleton Brian
Sonic Innovations, Inc.
Thelen Reid & Priest LLP
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