1412 results about "Noise (signal processing)" patented technology

A method and apparatus extract a signal component of a measured signal using one of two methods. If the signal component in the measured signal is a periodic signal with a certain well-defined peak-to-peak intensity value, upper and lower envelopes of the measured signal are determined and analyzed to extract said signal component of the measured signal. This signal component can further be used to calculate a desired parameter of the sample. The DC component of the signal is determined as the median value of the upper envelope, and the AC component is determined as the median value of the difference between the upper and lower envelopes. If the signal component of the measured signal is a periodic signal characterized by a specific asymmetric shape, a specific adaptive filtering is applied to the measured signal, resulting in the enhancement of the signal component relative to a noise component. This adaptive filtering is based on a derivative of the Gaussian Kernel having specific parameters matching the characteristics of the signal component.

A system and method for acoustic detection of coronary artery disease (CAD) are provided. The system includes a transducer for acoustically detecting heart signals of a patient and a computer system which executes detection software for processing the detected heart signals to identify the presence of CAD from the heart signals. The software allows for the automatic definition of a diastolic “window” of the acoustic signal for analysis, and automatically edits the sampled acoustic signal to eliminate unwanted artifacts and/or noise in the acoustic signal. The edited signal is then processed by a plurality of signal processing algorithms, including spectral analysis algorithms, time-frequency algorithms, global feature algorithms, kurtosis algorithms, mutual information algorithms, negenthropy algorithms, and principal component analysis algorithms, to generate a disease vector. The disease vector is then classified to determine whether CAD is present in the patient. Classification can be accomplished using linear discriminant analysis or a support vector machine.

The embodiment of the invention discloses a microphone-array speech-beam forming method, which comprises a digital-signal converting step, a frequency-region signal-obtaining step, a time-delay obtaining step, a time-delay compensating step and a weighted stacking step, wherein time-delay estimation based on phase conversion is adopted particularly in the time-delay obtaining step, thereby a beam-forming signal pointed to a target sound-source spatial position after enhancement processing is obtained. Compared with the prior art, the positioning precision and the directivity of a three-dimensional space are enhanced, the long-distance sound-picking capability in a complicated acoustics environment is greatly enhanced, high-quality voice signals are obtained, and noise and other interferences are reduced. The embodiment of the invention also discloses a speech-signal processing device and a system.

This invention presents a noise cancellation device for improved personal face-to-face and radio communications in high noise environments. The device comprises speech acquisition components, an audio signal processing module, a loudspeaker, and a radio interface. With the noise cancellation device, the signal-to-noise ratio can be improved by as much as 30 dB.

The invention discloses a digital audio directional loudspeaker and a digital audio signal processing method. An automatic gain control module adjusts the amplitude of a sound source signal; then, a filter filters out elements beyond a audio signal belt so as to reduce noises; a signal input module changes an audio analog signal into a digital signal; the processing of approximate square root algorithm or other algorithms to the audio digital signal is finished in the digital signal processing module; two state levels are output into a switch type power amplifier to match with the network to assure impedance matching; an ultrasonic transducer is used for changing an electric signal into a sound pressure signal. The ultrasonic signal which is emitted from the transducer carries the input audio signal information, does the nonlinear interaction in the air and is demodulated into audible sound. A remote control receiving unit receives orders sent by a remote control transmission unit to control parameters such as point angle, volume, transmission distance, sound effect and point sound, etc.

In a digital hearing aid device (1) frequency modification is employed above a lower spectral bound and in accordance with a compression factor. The frequency modification is dynamically adjusted in dependence on a sound environment analysis (10) or an end-user input (30), by modifying the frequency modification parameters such as a lower spectral bound and a compression factor. The adjustment can be based on an interpolation between predefined parameters. In certain sound environments, such as loud noise, own-voice and telephone conversations, frequency modification is reduced or switched off. The proposed solutions have the advantage that the occurrence of disturbing noise and of distortions of harmonic relationships at the end-user's ear is reduced and signal processing resources as well as battery resources are saved.

A home appliance including a communication device configured to communicate with another home appliance, a microphone configured to receive a voice from a user, and a processor configured to perform signal processing on first voice data obtained from the microphone and perform voice recognition using the signal-processed first voice data. Wherein the processor generates noise data using second voice data received from the other home appliance and performs the signal processing on the first voice data using the generated noise data.

The invention discloses a method for suppressing transient noise in voice, and belongs to the technical field of signal processing. The method for suppressing transient noise in voice is characterized by comprising a gamma through frequency cepstrum coefficient extraction module, a transient noise detection module and a voice signal reconstruction module, wherein the input end of the gamma through frequency cepstrum coefficient extraction module receives a voice signal containing noise, the output end of the gamma through frequency cepstrum coefficient extraction module is connected with the input end of the transient noise detection module, the output end of the transient noise detection module is connected with the input end of the voice signal reconstruction module, the input end of the voice signal reconstruction module receives the voice signal containing noise and is also connected with the output end of the transient noise detection module, and the voice signal reconstruction module outputs the voice with noise removed.

A method for detecting demand fault of motor vehicle includes setting sound transducer near to key parts of motor vehicle operation, comparing sound detected by said transducer with original sound information stored in noise signal databank by noise signal processing unit for knowing information that detected parts is damaged or not then informing said information to motor vehicle driver through information display unit.

The invention discloses an edge detection method based on fractional-order signal processing, aiming at solving edge detection which is a traditional trouble in the pattern recognition field. The method is a novel algorithm which can conduct gradient operation to all target pixel points in an image by using the fractional-order signal processing to obtain an edge, and comprises that a gray-level matrix is generated from any image, the gradient operation is conducted respectively to each pixel point in the matrix by adopting detection operators to obtain the gradient amplitude of each pixel point, then non-maximum value suppression is conducted to the gradient image and finally a double-threshold method is adopted for judging whether the target pixel points are edge points and are connected with the edge or not. The invention omits the process of smooth filtering preprocessing conducted to the image and utilizes a novel derivative algorithm based on the fractional-order signal processing to conduct the gradient operation. Fractional integrals in the algorithm can suppress the interference introduced during derivation. The entire method has the advantages that the signal-to-noise ratio is good, the edge positioning is accurate and the false edge can be effectively suppressed. The algorithm can be used in the fields such as the automatic target recognition and the like.

The invention discloses a noised voice end point robustness detection method. The method comprises the following steps of constructing an estimation method of a noise power spectrum of each frame of acoustical signals in filtering and providing a time-varying updating mechanism of a noise spectrum; firstly, carrying out iterative wiener filtering on a frequency spectrum of each frame of voices; then, dividing into several sub-band and calculating a frequency spectrum entropy of each sub-band; and then making successive several frames of sub-band frequency spectrum entropies pass through one group of median filters so as to acquire each frame of the frequency spectrum entropies; according to values of the frequency spectrum entropies, classifying input voices. By using the algorithm, the voices and noises, and a voice state and a voiceless state can be effectively distinguished. Under different noise environment conditions, robustness is possessed. The algorithm has low calculating cost, is simple, is easy to realize and is suitable for application of real-time voice signal processing system of various kinds of systems needing voice end point detection. The method is a real-time voice end points detection algorithm which adapts to a complex environment, and voice end point detection and voice filtering enhancement are completed together in a one-time state.

The invention relates to an interference noise matrix reconstitution-based self-adaptive wave beam forming method and belongs to the field of array signal processing. The method comprises the steps of firstly establishing the signal model of a minimum variance distortionless response wave beam forming problem, then under the condition that the angle range of an expected signal wave arrival direction is known, utilizing a multiple signal sorting spatial spectrum to reconstitute an interference noise covariance matrix in a region without containing expected signals, based on the matrix, solving the estimation value of a guide vector of the real expected signal by using the maximizing of an array output power and the constrain condition that the estimation value of the guide vector of the expected signal is prevented from being converged to the guide vector of any interference or the linear combination of the interference. According to the method, a simulation result shows that when random pointing errors and local scattering of the expected signal and a interference source exist, the output signal to interference plus noise power ratio in a very large input signal-to-noise ratio range is still close to a theoretical value and is superior to that of other self-adaptive wave bean forming methods.

A digital-to-analog converter (“DAC”) system utilizes chopping modulation technology to remove 1/f and other baseband noise from a baseband of a signal of interest. Chopping modulation and demodulation circuitry of the DAC operate at a chopping frequency equal to approximately one-half of a digital input signal sampling frequency. Chopping at one-half the sampling frequency allows fold back into the baseband of the input signal's frequency components and reduces fold back of noise, such as quantization noise, residing outside the baseband. In a further embodiment, a notch filter attenuates signals having frequencies around the chopping frequency prior to chopping to reduce fold back of noise into the baseband due to parasitic modulation. Coordination of chopping timing also reduces noises in the output of the DAC system.

Systems, apparatuses and methods for neural network signal processing of microseismic events. A series of sensors are disposable in at least one first well positioned about a second well disposed in a subterranean formation. The series of sensors obtain a data signal measurement including noise events and microseismic acoustic emission events. A processor includes a first neural network. The processor may remove the noise events from the data signal measurement and determine with the first neural network an arrival time for each microseismic acoustic emission event. An interface can output the arrival time for each microseismic acoustic emission event.

The invention belongs to the technical field of signal processing, and relates to a speech enhancement method based on speech presence probability and phase estimation. The method comprises the following steps: (1) estimating the speech presence probability; (2) estimating the pure voiced phase; (3) estimating the pure speech magnitude spectrum; and (4) estimating pure speech signals. The Q value is estimated through a multivariable linear regression technology, and the accuracy of speech presence probability estimation is improved. The pure voiced phase is estimated between adjacent bands using a harmonic model of short-time Fourier transform domain. The pure speech magnitude spectrum is estimated based on the phase difference, and the compensation effect of phase on the pure speech magnitude spectrum is fully utilized. The speech enhancement method of the method is a single-channel speech enhancement method. Speech signals with noise are collected using one microphone. The method is easy to implement. The problem that estimation of speech presence probability is inaccurate can be well solved. The compensation effect of the voiced phase and the phase difference on the pure speech magnitude spectrum is fully utilized. Thus, the performance of the speech enhancement method is improved.

The invention provides a blind source signal denoising method based on ensemble empirical mode decomposition, and belongs to the technical field of signal processing. By means of the method, definitions on the white noise amplitude and the number of iterations in an original algorism are rectified. False component discrimination is conducted on the IMP component obtained after IEEMD is decomposed through a classic stepwise regression analysis method, features of original signals are effectively reserved, the false component generated by the IEEMD algorism is eliminated, and interference to the subsequent denoising algorism by the false component is eliminated. Finally, for the non-convergence phenomenon generated occasionally when the ICA algorism processes the high-frequency signals, a high-order TFastICA method is provided, features of the IEEMD and the TFastICA are combined, and rear-end processing is conducted on the IEEMD through the TFastICA method. The blind source signal denoising method based on the ensemble empirical mode decomposition has wide application prospects in the fields of removing mechanical vibration noise, voice signal noise, instantaneous underwater noise and other signal processing fields.