103 results about "Stationary noise" patented technology

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.

An audio decoding device can adjust the high-range emphasis degree in accordance with a background noise level. The audio decoding device includes: a sound source signal decoder which performs a decoding process by using sound source encoding data separated by a separator so as to obtain a sound source signal; an LPC synthesis filter which performs an LPC synthesis filtering process by using a sound source signal and an LPC generated by an LPC decoder so as to obtain a decoded sound signal; a mode judger which determines whether a decoded sound signal is a stationary noise period by using a decoded LSP inputted from the LPC decoder a power calculator which calculates the power of the decoded audio signal; an SNR calculator 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 judger and a post filter which performs a post filtering process by using the SNR of the decoded audio signal.

A method for removing a stationary noise pattern from digital images uses an adaptive noise estimation algorithm to calculate a prediction of the fixed pattern noise and a confidence estimate for the prediction. In one embodiment, a predicted noise value is obtained from the captured image and a predicted image derived from spatial and temporal pixel value prediction techniques. The predicted noise value is used to update a fixed pattern noise estimate only when the confidence estimate for the predicted image is high. In another embodiment, the confidence estimate is used as a weight factor for blending the noise prediction into the noise estimate. In yet another embodiment, the adaptive noise estimation algorithm is applied to a prediction area in the image for calculating scaling parameters which scaling parameters are used to calculate a noise estimate for the entire image based on a reference noise image.

A directivity control section (10) calculates a main beam signal the directivity of which is directed toward the direction of an object sound and a side beam signal the blind spot of which is directed toward the direction of the object sound from the output signals of a plurality of microphones (2, 3) by signal processing, which a frequency analyzing section (20) converts into spectrums. A sound source discriminating section (30) discriminates whether the sound source is a sound, a stationary noise, or a non-stationary noise from the spectrums of the main beam signal and the side beam signal to output the result of the discrimination of the sound source and calculates the average spectrum which is the statistic of a noise with respect to the main beam signal. A disturbance sound cancellation section (50) subtracts the average spectrum from the spectrum of the main beam signal to cancel the disturbance sound.

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.

First determiner 121 provisionally determines whether a current processing unit is a stationary noise region based on a determination result on stationary characteristics of a decoded signal. Based on the provisional determination result and a determination result on periodicity of the decoded signal, second determiner 124 further determines whether the current processing unit is a stationary noise region, whereby a decoded signal including a stationary speech signal such as a stationary vowel is distinguished from a stationary noise, and thus the stationary noise region is detected accurately.

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.

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.