Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Electrical signal parameter measurement system
Reexamination Certificate
1998-08-25
2001-01-30
Hoff, Marc S. (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Electrical signal parameter measurement system
C702S057000, C702S189000, C073S602000, C324S076120
Reexamination Certificate
active
06182018
ABSTRACT:
The present invention relates in general to separating an impulsive sound component from a composite sound signal and then classifying the sound according to its possible source or potential cause, and more specifically to using wavelet transforms and sorting of wavelet coefficient sets to separate the impulsive component and comparing the impulsive component with previously collected reference signals.
Time-domain signals or waveforms may often include impulsive and non-impulsive components even though only one of these components may be of interest. For example, in either wireless or wired transmission of electrical or electromagnetic signals, interfering signals and background noise contaminate the signal as it travels through the wireless or wired transmission channel. The transmitted signal contains information, and therefore has primarily an impulsive character. The interference and background noise tends to be random and broadband, and therefore has primarily a non-impulsive character. After transmission, it would be desirable to separate the components so that the additive noise can be removed.
In other applications, sound waves may be converted to electrical signals for transmission or for the purpose of analyzing the sound to determine conditions that created the sound. If the sound is a voice intended for transmission, the picked-up sound may include an impulsive voice component and a non-impulsive background noise component. If the picked-up sound is created by operation of a machine or other environmental noise, the nature of the impulsive and/or non-impulsive sound components can be analyzed to identify specific noise sources or to diagnose or troubleshoot fault conditions of the machine, for example.
Prior art attempts to reduce unwanted noise and interference most often treat a signal as though the impulsive and non-impulsive components occupy different frequency bands. Thus, lowpass, highpass, and bandpass filtering have been used to try to remove an undesired component. However, significant portions of the components often share the same frequencies. Furthermore, these frequency bands of interest are not known or easily determined. Therefore, frequency filtering is unable to separate the components sufficiently for many purposes. Fourier analysis and various Fourier-based frequency-domain techniques have also been used in attempts to reduce undesired noise components, but these techniques also cannot separate components which share the same frequencies.
More recently, wavelet analysis has been used to de-noise signals. Wavelet transforms are similar in some ways to Fourier transforms, but differ in that the signal decomposition is done using a wavelet basis function over the plurality of time-versus-frequency spans, each span having a different scale. In a discrete wavelet transform, the decomposed input signal is represented by a plurality of wavelet coefficient sets, each set corresponding to a respective time-versus-frequency span. De-noising signals using wavelet analysis has been done in the prior art by adjusting the wavelet coefficient sets by thresholding and shrinking the wavelet coefficients prior to recovering a time-domain signal via an inverse wavelet transform. However, this technique has not resulted in the desired signals being separated to the degree necessary for many applications.
When attempting to identify the source of a sound, such as when diagnosing operation of a machine or detecting a potential cause of an unwanted noise, the accuracy of identification depends upon how well the sound is separated from other components of the composite sound. For example, in finding and fixing squeak and rattle problems when servicing or developing automotive vehicles, an impulsive sound created by some imperfection in the vehicle must be classified according to its probable cause while in the presence of background noises. Inadequate suppression of these background noises may prevent easy classification of the impulsive sound, especially when trying to automate the identification.
SUMMARY OF THE INVENTION
The present invention has the advantage of accurately identifying a source of sound in a composite sound signal.
In one aspect of the invention, a method of identifying a sound in a composite sound signal includes converting the composite sound signal to an electrical signal. The electrical signal is decomposed using a wavelet transform to produce a plurality of sets of wavelet coefficients, each set of wavelet coefficients corresponding to a respective time-versus-frequency span. A respective statistical parameter is determined for each set of wavelet coefficients. An impulsive signal is re-synthesized using an inverse wavelet transform applied to selected ones of the sets of wavelet coefficients, the selected ones being selected in response to the respective statistical parameters. The impulsive signal is compared with a plurality of reference signals to classify the sound with respect to the reference signals.
REFERENCES:
patent: 5461655 (1995-10-01), Vuylsteke et al.
patent: 5497777 (1996-03-01), Abdel-Malek et al.
patent: 5619998 (1997-04-01), Abdel-Malek et al.
S. Lei, Wavelet Transform and Frequency Domain Kurtosis: Application To Assessment Of Hearing Hazard From Noise Exposure, 1995 IEEE ASSP Workshop on Applications Of Signal Processing To Audio And Acoustics.
A. Graps, An Introduction To Wavelets, IEEE Computational Science & Engineering, Summer, 1995, pp. 50-61.
Charette Francois
Hsueh Keng D.
Lei Sheau-Fang
Tran Vy
Ford Global Technologies Inc.
Hoff Marc S.
Mollon Mark L.
Vo Hien
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