99 results about "Stationary noise" patented technology

An imaging device of the present invention includes an image capturing unit, a noise obtaining unit, a fixed noise calculating unit, and a noise eliminating unit. The image capturing unit generates image data by photoelectrically converting, pixel by pixel, a subject image formed on an available pixel area of a light-receiving surface. The noise obtaining unit reads a noise output from a partial area of the available pixel area. The fixed noise calculating unit calculates an estimation of fixed pattern noise of the available pixel area based on the noise output read from the partial area. The noise eliminating unit subtracts the fixed pattern noise from the image data.

A two microphone noise reduction system is described. In an embodiment, input signals from each of the microphones are divided into subbands and each subband is then filtered independently to separate noise and desired signals and to suppress non-stationary and stationary noise. Filtering methods used include adaptive decorrelation filtering. A post-processing module using adaptive noise cancellation like filtering algorithms may be used to further suppress stationary and non-stationary noise in the output signals from the adaptive decorrelation filtering and a single microphone noise reduction algorithm may be used to further provide optimal stationary noise reduction performance of the system.

A noise reduction device is configured by use of: means for calculating a predetermined constant, and a predetermined reference signal Rω(T) in the frequency domain, respectively by use of adaptive coefficients Wω(m), and for thereby obtaining estimated values Nω and Qω(T) respectively of stationary noise components, and non-stationary noise components corresponding to the reference signal, which are included in a predetermined observed signal Xω(T) in the frequency domain; means and for applying a noise reduction process to the observed signal on the basis of each of the estimated values, and for updating each of the adaptive coefficients on the basis of a result of the process; and an adaptive learning means and for repeating the obtaining of the estimated values and the updating of the adaptive coefficients, and for thereby learning each of the adaptive coefficients.

An electronic device for suppressing noise in an audio signal is described. The electronic device includes a processor and instructions stored in memory. The electronic device receives an input audio signal and computes an overall noise estimate based on a stationary noise estimate, a non-stationary noise estimate and an excess noise estimate. The electronic device also computes an adaptive factor based on an input Signal-to-Noise Ratio (SNR) and one or more SNR limits. A set of gains is also computed using a spectral expansion gain function. The spectral expansion gain function is based on the overall noise estimate and the adaptive factor. The electronic device also applies the set of gains to the input audio signal to produce a noise-suppressed audio signal and provides the noise-suppressed audio signal.

A In a noise suppression apparatus, an extractor extracts a noise component from an audio signal. A stationary noise estimator estimates stationary noise included in the noise component. A first noise suppressor removes a spectrum of the stationary noise from a spectrum of the audio signal to an extent determined by a subtraction factor. A non-stationary noise estimator estimates a spectrum of non-stationary noise by subtracting the spectrum of the stationary noise from the spectrum of the noise component. A factor setter generates a filtering factor for emphasizing a target sound component contained in the audio signal from the spectrum of the non-stationary noise. A second noise suppressor performs a filtering process using the filtering factor on the audio signal after processing of the first noise suppressor. An index calculator calculates a kurtosis change index representing an extent of change of kurtosis in a frequence distribution of magnitude of the audio signal between the kurtosis observed when processing of the first noise suppression part is performed and the kurtosis observed when processing of the second noise suppression part is performed. A factor adjuster variably controls the subtraction factor according to the kurtosis change index.

Provided is an audio decoding device which can adjust the high-range emphasis degree in accordance with a background noise level. The audio decoding device includes: a sound source signal decoding unit (204) which performs a decoding process by using sound source encoding data separated by a separation unit (201) so as to obtain a sound source signal; an LPC synthesis filter (205) which performs an LPC synthesis filtering process by using a sound source signal and an LPC generated by an LPC decoding unit (203) so as to obtain a decoded sound signal; a mode judging unit (207) which determines whether a decoded sound signal is a stationary noise section by using a decoded LSP inputted from the LPC decoding unit (203); a power calculation unit (206) which calculates the power of the decoded audio signal; an SNR calculation unit (208) which calculates an SNR of the decoded audio signal by using the power of the decoded audio signal and a mode judgment result in the mode judgment unit (207); and a post filter (209) which performs a post filtering process by using the SNR of the decoded audio signal.

A digital electronic stethoscope includes an acoustic sensor assembly that includes a body sensor portion and an ambient sensor portion, the body sensor portion being configured to make acoustically coupled contact with a subject while the ambient sensor portion is configured to face away from the body sensor portion so as to capture environmental noise proximate the body sensor portion; a signal processor and data storage system configured to communicate with the acoustic sensor assembly so as to receive detection signals therefrom, the detection signals including an auscultation signal comprising body target sound and a noise signal; and an output device configured to communicate with the signal processor and data storage system to provide at least one of an output signal or information derived from the output signal. The signal processor and data storage system includes a noise reduction system that removes both stationary noise and non-stationary noise from the detection signal to provide a clean auscultation signal substantially free of distortions. The signal processor and data storage system further includes an auscultation sound classification system configured to receive the clean auscultation signal and provide a classification thereof as at least one of a normal breath sound or an abnormal breath sound.

Method for recovering target speech by extracting signal components falling in a speech segment, which is determined based on separated signals obtained through the Independent Component Analysis, thereby minimizing the residual noise in the recovered target speech. The present method comprises: the first step of receiving target speech emitted from a sound source and a noise emitted from another sound source and extracting estimated spectra Y* corresponding to the target speech by use of the Independent Component Analysis; the second step of separating from the estimated spectra Y* an estimated spectrum series group y* in which the noise is removed by applying separation judgment criteria based on the kurtosis of the amplitude distribution of each of estimated spectrum series in Y*; the third step of detecting a speech segment and a noise segment of the total sum F of all the estimated spectrum series in y* by applying detection judgment criteria based on a predetermined threshold value T that is determined by the maximum value of F; and the fourth step of extracting components falling in the speech segment from the estimated spectra Y* to generate a recovered spectrum group of the target speech for recovering the target speech.

Measurements made by a transducer assembly for downhole imaging are affected by reverberations between the transducer and the window on the outside of the assembly. The reverberations result in a stationary noise on the image. Hardware solutions to improve signal-to-noise ratio includes using a composite transducer, adjusting the distance between the transducer and the window. SNR can also be improved by processing techniques that include stacking, fitting a sinusoid to the reverberation, by envelope peak detection, and by applying a notch filter.

A method detects events in an accoustic signal subject to cyclostationary background noise by first segmenting the signal into cycles. Features with a fixed dimension are derived from the cycles, such that the timing of the features is relative to a cycle time. The features are normalized using an estimate of the cyclostationary background noise. Then, after the normalizing, a classifier is applied to the features to detect the events.

A speech enhancement method and a speech enhancement system are provided. The speech enhancement method performs two-stage noise suppression by using digital signal processing and neural network approach. The first-stage noise suppression generates artifact signals by reducing stationary noise in the digital audio signals. The second-stage noise suppression performs voice activity detection and further reduces non-stationary noise in the artifact signals. The result of the voice activity detection is fed back to establish or update a noise model used in the first-stage noise suppression.

The invention provides a time-of-flight depth camera and a single-frequency modulation-demodulation noise-reducing distance measuring method. The time-of-flight depth camera comprises a transmitting module, an acquisition module and a processing circuit, wherein the transmitting module comprises a light source which is used for transmitting a pulse beam to an object to be measured; the acquisitionmodule comprises an image sensor composed of at least one pixel, each pixel comprises at least three taps, and the taps are sued for acquiring a charge signal generated by a reflected pulse beam which is reflected by means of the object to be measured and/or a charge signal of background light; and the processing circuit is used for controlling the at least three taps to acquire the charge signals alternatively between at least three frame periods of a macro period, and receiving data of the charge signals to calculate a time of flight of the pulse beam and/or a distance of the object to be measured. The expansion of the measurement distance is no longer limited by the pulse width, and the fixed-pattern noise caused by mismatch between the taps or read-out circuits due to process manufacturing errors and the like is reduced or eliminated by adopting a tap alternative acquisition method.

A first determiner 121 tentatively determines whether the current processing unit represents a stationary noise period, based on stationary properties of a decoded signal. Based on the tentative determination result and a determination result of the periodicity of the decoded signal, a second determiner 124 determines whether the current processing unit represents a stationary noise period, thereby distinguishing a decoded signal including a stationary speech signal such as a stationary vowel from stationary noise and correctly identifying the stationary noise period.

The invention discloses a method for enhancing voice based on signal to noise ratio soft masking. The method comprises: establishing noise power spectrum updates of a sub-band time varying coefficient, using different threshold update smooth spectrums for different frequency points, enhancing voice and restraining noise; determining a posterior signal to noise ratio from a noise power spectrum, performing iterative computation to obtain a prior signal to noise ratio of a present frame according to the posterior signal to noise ratio and the prior signal to noise ratio of a previous frame, and obtaining that each frequency point is in a masking region or in a target signal region according to values of the prior signal to noise ratio. A masking value is calculated by probability distribution obtained by hypothesis testing. The method uses correlation of adjacent frames to extract information to realize enhancement of voice spectrum smooth iteration estimation. For non-stationary noise and strong background noise, a voice enhancement algorithm based on signal to noise ratio soft masking is provided. A rapid tracking noise algorithm performs smooth update on the non-stationary noise frame by frame, preferably estimating a noise spectrum. The algorithm can effectively restrain background noise and improve voice quality and speech intelligibility after denoising.