202 results about "Noise power spectrum" patented technology

There is a provided a method for managing a multiple frequency assignment using a terminal's RX performance in an orthogonal frequency division multiple access (OFDMA) wireless regional area network (WRAN) system, including: a) measuring a bit energy / noise power spectral density (Eb / No) and a received signal strength indicator (RSSI) which are received in the terminal; b) informing the measured Eb / No and RSSI of a base station; c) calculating a round trip delay (RTD) in the base station; d) determining a terminal performance level based on the measured Eb / No, RSSI and the calculated RTD; and e) determining a frequency channel and modulation scheme based on the determined performance level.

The invention discloses a speech enhancement method using a speech existence probability, after the processing of the invention, the speech quality is higher, and the noise reduction amount is larger.The method is realized by the following technical scheme that: on the basis of a MIS measure speech enhancement method, the speech existence probability is used, an inputted noisy speech is sampled,framed and windowed, and then a noisy speech spectrum is obtained through fast Fourier transform (FFT); the noise estimation of the obtained speech spectrum is carried out, based on a non-stationary noise minimum search algorithm of statistical information, the smoothing between front and back frame noise estimation values is carried out by using inter-frame correlation, and a noise power spectrumis estimated; at the same time, speech prior signal-to-noise ratio estimated values obtained in several front and back frames are smoothened; then the speech existence probability and a MIS measure gain factor are combined, the obtained noisy speech spectrum is multiplied by a new gain factor, and a spectrum of the enhanced speech is obtained; and the inverse fast Fourier transform (IFFT) is performed to obtain an enhanced time domain speech signal.

The invention discloses a microphone array multi-target voice enhancement method based on blind source separation and spectral subtraction. The method comprises: a multi-channel multi-target signals are collected through a microphone array; band-pass filter processing is carried out on the collected single-channel signals respectively to shield non-voice noises and interference, and pre-emphasis processing is carried out; voice windowing and framing processing is carried out to obtain frame signals, short-time Fourier transform is carried out to transform all frames into a frequency domain, and amplitude spectrums and phase spectrums of all frames are extracted; a starting end point and an ending end point of a voice signal are detected and a noise power spectrum is estimated; on the basis of spectral subtraction, background noises of a voice frame are reduced; the signal outputted after spectral subtraction is combined with the phase spectrum to carry out short-time Fourier inverse transform, thereby obtaining a voice signal of a time domain; and then blind source separation is carried out to obtain all target signals. The method can be realized simply; the resource requirement is low; the computing complexity is low; and multi-target signal enhancement can be realized.

A method and system for compensating for atmospheric fading is used in a communication system wherein communication signals are exchanged between first and second earth stations via a satellite link, without increasing power of the satellite link. The method includes determining, at one or both of the earth stations, the signal power of a received signal and a signal-to-noise ratio of the received signal; calculating the difference in noise power spectral density in the received signal from that under clear sky conditions; calculating the downlink attenuation; determining the uplink attenuation, and commanding the transmitting earth station to increase its transmit power by an amount to compensate for the uplink attenuation. A corresponding system carries out the method.

The invention relates to a twitter identification method based on self-adaption energy detection under a complex environment. The method is characterized by comprising the following steps of firstly, dividing twitter into three frequency bands according to the frequency distribution condition of the twitter; secondly, detecting twitter acoustical signals containing noise through a self-adaption energy detection method based on noise power spectrum estimation and the foreground sound existence probability, and screening out the useful twitter signals; thirdly, extracting Mel-scale WPSCC features from the useful twitter signals according to distribution of the Mel scales; fourthly, conducting modeling, classification and identification on the extracted Mel-scale WPSCC features and extracted Mel-scale MFCC features through an SVM classifier. The WPSCC features extracted through the method have a better anti-noise function and are better in identification performance after undergoing self-adaption energy detection, the method is more suitable for twitter identification under the complex environment and has a good classification and identification effect on the twitter sound containing noise under the complex environment.

The invention discloses a voice enhancement method for fusing phase estimation and human ear hearing characteristics in a digital hearing aid, comprising steps of obtaining a frequency domain expression mode containing noise through Fourier transformation, adopting a minimum value control recursive average method to obtain a noise power spectrum, obtaining initial enhancement voice and a noise amplitude spectrum, obtaining the initial enhancement voice and noise through correcting the phase of the voice and the noise through improving the phase estimation of the voice distortion under the low signal-to-noise ratio environment, performing filtering processing on the initial enhancement voice and the noise through a gammatone filter assembly which simulates the working mechanism of the artificial cochlea, performing analysis on the time frequency of the gammatone filter assembly to obtain the time frequency expression form consisting of time frequency units, using the hearing characteristics of the human ear to calculate binary mast containing noise in the time frequency domain, and using the mask value to obtain the enhanced voice after synthesis.

A method for providing a reduced exposure value for radiographic imaging obtains a set of flat-field images at two or more exposure values and measures the noise power spectra using the flat field images. At least one noise table is generated according to interpolated noise power spectra for a set of predetermined exposure values. Values from the at least one noise table are applied to a clinical image to form a reduced exposure simulation image. A noise mask is generated according to at least one noise table and the exposure values of the reduced exposure simulation image and added to the reduced exposure simulation image. The reduced exposure simulation image is assessed to generate a desired dose reduction factor.

The invention relates to a method of estimating noise power spectral density PSD in an input sound signal comprising a noise signal part and a target signal part. The invention further relates to a system to its use. The object of the present invention is to provide a scheme for estimating the noise PSD in an acoustic signal consisting of a target signal contaminated by acoustic noise. The problem is solved by a method comprising the steps of d) providing a digitized electrical input signal to a control path and performing; d1) storing a number of time frames of the input signal each comprising a predefined number N2 of digital time samples xn (n=1, 2, . . . , N2), corresponding to a frame length in time of L2=N2 / fs; d2) performing a time to frequency transformation of the stored time frames on a frame by frame basis to provide corresponding spectra Y of frequency samples; d3) deriving a periodogram comprising the energy content |Y|2 for each frequency sample in a spectrum, the energy content being the energy of the sum of the noise and target signal; d4) applying a gain function G to each frequency sample of a spectrum, thereby estimating the noise energy level |Ŵ|2 in each frequency sample, |Ŵ|2=G·|Y|2; d5) dividing the spectra into a number Nsb2 of sub-bands, each sub-band comprising a predetermined number nsb2 of frequency samples, and assuming that the noise PSD level is constant across a sub-band; d6) providing a first estimate |{circumflex over (N)}|2 of the noise PSD level in a sub-band based on the non-zero noise energy levels of the frequency samples in the sub-band; d7) providing a second, improved estimate |Ñ|2 of the noise PSD level in a sub-band by applying a bias compensation factor B to the first estimate, |Ñ|2=B·|{circumflex over (N)}|2. The invention may e.g. be used in listening devices, e.g. hearing aids, mobile telephones, headsets, active earplugs, etc.

The present invention relates to a method, preprocessor, speech recognition system, and program product for extracting a target speech by removing noise. In an embodiment of the invention target speech is extracted from two input speeches, which are obtained through at least two speech input devices installed in different places in a space, applies a spectrum subtraction process by using a noise power spectrum (Uω) estimated by one or both of the two speech input devices (Xω(T)) and an arbitrary subtraction constant (α) to obtain a resultant subtracted power spectrum (Yω(T)). The invention further applies a gain control based on the two speech input devices to the resultant subtracted power spectrum to obtain a gain-controlled power spectrum (Dω(T)). The invention further applies a flooring process to said resultant gain-controlled power spectrum on the basis of arbitrary Flooring factor (β) to obtain a power spectrum for speech recognition (Zω(T)).

The invention provides a real time voice denoising method and device; the method comprises the following steps: generating a frequency domain zone noise voice signal according to a voice input received by a voice receiver; calculating a logarithm spectrum posterior signal to noise ratio according to the frequency domain zone noise voice signal, wherein the logarithm spectrum posterior signal to noise ratio refers to a ratio between logarithm of a power spectrum of a present frame frequency domain zone noise voice signal and a logarithm of a previous frame noise power estimation value; obtaining a noise power spectrum estimation value according to the logarithm spectrum posterior signal to noise ratio and based on a weight noise estimation algorithm; generating a Wiener filtering gain function according to the noise power spectrum estimation value, and filtering the frequency domain zone noise voice signal according to the gain function, thus generating a frequency domain denoising voice signal; generating a time domain denoising voice signal according to the frequency domain denoising voice signal, and the time domain denoising voice signal is further processed by the voice receiver. Correspondingly, the invention also provides the real time voice denoising device.

A method and for optimizing bit rate throughput in a digital communication system is provided. The method includes establishing a relationship between signal to noise ratio and plural symbol rates for a particular constellation size. The method also includes determining noise power spectral density (N(f)), wherein N(f) is determined during a silent period of line probing; determining Xk(f), wherein Xk(f) is determined by turning on a remote station transmit signal, after N(f) has been measured and determining residual echo Ek(f), wherein Ek(f) is determined by turning on a central station echo canceller.

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.

Embodiments of the invention disclose a speech de-noising method and a speech de-noising device. The method comprises the following steps: detecting the speech of a speech signal with noise to distinguish between speech frames and non-speech frames; estimating the noise of the speech frames and the noise of the non-speech frames to get a noise power spectrum fused estimated value, wherein the noise power spectrum fusion estimated value is the fused value of the noise power spectrum estimated value of the speech frames and the noise power spectrum estimated value of the non-speech frames; and de-noising the speech signal with noise according to the noise power spectrum fusion estimated value. According to the technical scheme provided by the embodiments of the invention, the noise of the speech frames and the noise of the non-speech frames are estimated, and the speech signal with noise is de-noised based on the noise estimation results of the speech frames and the non-speech frames. Thus, the de-noising effect of the existing speech de-noising scheme is improved effectively, and the quality of speech is improved.

A method and system is provided for enhancing an audio signal based on spectral subtraction. The noise power spectrum for each frame of an audio signal is dynamically estimated based on a plurality of signal power spectrum values computed from a corresponding plurality of adjacent frames. An over-subtraction factor is then dynamically computed for each frame based on the noise power spectrum estimated for the frame. The signal power spectrum of the audio signal at each frame is then reduced in accordance with the over-subtraction factor computed for the corresponding frame.

The invention discloses a two-channel beam forming speech enhancement method based on noise mixed coherence. Adaptive beam forming can effectively inhibit directional noise signals under the reverberation-free condition, but the inhibiting effect of the adaptive beam forming is greatly reduced in the presence of reverberation. Aiming at the problem, the invention provides the two-channel beam forming method based on noise mixed coherence. Considering that coherence and scattering noises exist simultaneously in a sound field, in the method provided by the invention, the hypothesis of replacing the traditional scattering sound field with a mixed noise field is provided, specifically, the method comprises the following steps: firstly, estimating the noise coherence in the mixed noise field, estimating a power spectrum of the noises by utilizing the noise coherence, and calculating a gain function of a frequency domain filtering by using the estimated result for the power spectrum of the noises; and carrying out frequency domain filtering processing on the noises and reverberant signals, and carrying out further processing on the residual noises by adopting a minimum variance distortionless response beam forming device. The experiment proves that the quality of the speech enhanced by adopting the method provided by the invention is obviously improved compared with that enhanced by adopting the traditional method.

The invention provides a voice processing method and an apparatus thereof. The method comprises the following steps of firstly, converting a sound signal into a frequency domain signal, through calculating a signal-to-noise ratio of the frequency domain signal, acquiring an adaptive updating step size of a noise power spectrum, and according to the step size, updating a noise power spectrum density; then, detecting whether a voice activity exists in the sound signal, and if the voice activity exists in the sound signal, using adaptive Kalman filtering to process the frequency domain signal and acquiring a reverberation power spectrum density; after the noise power spectrum density and the reverberation power spectrum density are determined, calculating an optimization and estimation voice spectrum; and finally, carrying out inverse Fourier transform on the optimization and estimation voice spectrum so as to restore an optimized sound signal. In the invention, collected sound signal quality under a remote speaking condition can be effectively optimized and a voice identification rate is increased.

The invention relates to a method for estimating the noise power spectrum and the voice activity. According to the method, the appear probability of a voice on a frequency sub band and power spectrum information of noise can be finally deduced according to the time sequence relevance of a sequential hidden markov model (SHMM) description language based on first-order regression on each frequency component. The method comprises the following steps of: 1) extracting a logarithmic amplitude spectrum envelop for a voice signal on each frequency component, and constructing a corresponding binary hidden markov model, wherein each state is represented by Gaussian distribution; 2) for a field of voice data, setting M frames of caches, storing the previous M frames of input signals into the caches, extracting M frames of logarithmic amplitude spectrums from the caches, and constructing an initialization model by adopting a maximum likelihood estimation algorithm; and 3) after the initialization model lambdaM is obtained, starting from the (M+1)th frame, gradually updating the HMM of each frequency band by adopting an incremental learning method, and sequentially performing recurrence to obtain a noise value and the appear probability of a voice signal.

Disclosed herein is a noise reduction method for automatic speech recognitionl. A noise reduction method for automatic speech recognition, including: computing a magnitude spectrum of a noisy speech containing a clean speech to be recognized and noise affecting the clean speech; computing a power spectrum of the noisy speech; computing an estimate of a power spectrum of the clean speech; computing an estimate of a power spectrum of the noise; computing an estimate of an a priori signal-to-noise ratio as a function of the estimate of the power spectrum of the clean speech and the estimate of the power spectrum of the noise; computing an estimate of an a posteriori signal-to-noise ratio as a function of the power spectrum of the noisy speech and the estimate of the power spectrum of the noise; computing an attenuation gain as a function of the estimate of the a priori signal-to-noise ratio and the estimate of the a posteriori signal-to-noise ratio; and computing an estimate of a magnitude spectrum of the clean speech as a function of the magnitude spectrum of the noisy speech and the attenuation gain. Computing the estimates of the a priori and the a posteriori signal-to-noise ratios includes computing a noise weighting factor for weighting the estimate of the power spectrum of the noise in the computation of the estimates of the a priori and the a posteriori signal-to-noise ratios; computing a spectral flooring factor for flooring the estimates of the a priori and the a posteriori signal-to-noise ratios; and computing the estimates of the a priori and the a posteriori signal-to-noise ratios also as a function of the noise weighting factor and the spectral flooring factor

A method and system is provided for enhancing an audio signal based on spectral subtraction. The noise power spectrum for each frame of an audio signal is dynamically estimated based on a plurality of signal power spectrum values computed from a corresponding plurality of adjacent frames. An over-subtraction factor is then dynamically computed for each frame based on the noise power spectrum estimated for the frame. The signal power spectrum of the audio signal at each frame is then reduced in accordance with the over-subtraction factor computed for the corresponding frame.

In a system for estimating the power spectral density of acoustical background noise when the level of a smoothed power spectral density signal increases, an increment value is increased, starting from a minimum increment value, by a predetermined amount until a maximum increment value is reached if at the same time the value of the power spectral density currently determined in a new calculation cycle is larger than the estimate value of the power spectral density of the background noise determined in the previous calculation cycle. For cases in which the level of the smoothed power spectral density decreases, the amplitude of the decrement value is increased, starting from a minimum decrement value, by a predetermined amount until a maximum decrement value is reached if at the same time the value of the power spectral density currently determined in a new calculation cycle is smaller than the estimate value of the power spectral density of the background noise determined in the previous calculation cycle.

The invention discloses a microphone array postfiltering sound enhancement method based on multi-models and hearing characteristic, aiming at two important factors influencing the postfiltering sound enhancement performance of a microphone array, i.e. accurate estimation for signal parameters and suitable compromise between increasing noise reduction performance and reducing voice distortion. Thescheme of the invention comprises the following steps of carrying out time domain alignment on signals collected by the microphone array, and carrying out short-time Fourier transform and characteristic value analysis based of power spectrum; determining the dimensionality of a signal subspace through the existence probability of target voice signal in maximation noise-carried voice signals; self-adaptively selecting a distribution model of a noise power spectrum in the noise-carried voice signals; estimating noise power spectrum by utilizing a conditional probability; estimating an auditory masking threshold value based on the signal subspace; and estimating a postfilter by combining Lagrange multipliers according to the auditory sensing characteristics.

The invention provides a bird voice recognition technology based on novel noise-proof feature extraction by aiming at the problem of bird voice recognition in various kinds of background noise in ecological environment. The bird voice recognition technology comprises the following steps of firstly, obtaining noise power spectrums by a noise estimation algorithm suitable for highly nonstationary environment; secondly, performing the noise reduction on the voice power spectrums by a multi-band spectral subtraction method; thirdly, extracting anti-noise power normalization cepstrum coefficients (APNCC) by combining the voice power spectrums for noise reduction; and finally, performing contrast experiments under the conditions of different environments and signal to noise ratios (SNR) on the voice of 34 species of birds by means of extracted APNCC, power normalization cepstrum coefficient (PNCC) and Mel frequency cepstrum coefficients (MFCC) by a support vector machine (SVM). The experiments show that the extracted APNCC have a better average recognition effect and higher noise robustness and are more suitable for bird voice recognition in the environment with less than 30 dB of SNR.

The invention discloses a voice enhancement method and system based on phase compensation. The method comprises steps that a to-be-processed noisy voice signal is obtained; short-time Fourier transform of the noisy voice signal is carried out to obtain the amplitude spectrum and the phase spectrum of the noisy voice signal; a phase spectrum compensation function is obtained, and a Sigmoid type function whose factor is correspondingly changed with the change of the noisy voice signal-to-noise ratio is compensated; the phase spectrum of the noisy voice signal is compensated according to the phase spectrum compensation function to obtain the compensated phase spectrum; the amplitude of a pure voice signal is obtained according to the amplitude spectrum of the noisy voice signal; the compensated phase spectrum and the amplitude of the pure voice signal are reconstructed to obtain an enhanced voice signal. The method is advantaged in that compared with a traditional phase compensation voiceenhancement method, estimation of the noise is closer to the real noise power spectrum, the noise in an audio signal can be effectively inhibited, and intelligibility of the voice signal is improvedwhile the quality of the voice signal is enhanced.

The invention relates to a noise spectrum estimation and voice mobility detection method based on unsupervised learning, which comprises the steps of: 1, establishing a GMM (Gaussian Mixture Model) model aiming at logarithm amplitude features of a voice signal on each frequency point; 2, setting M frames of buffers for one section of voice data, storing the former M frames of input signals into the buffers, extracting a logarithm amplitude spectrum of M frames in the buffers, and substituting into the GMM mode of the step 1 for initializing to obtain an initialized model Lambda0,k; and 3, updating the GMM model by frames by adopting an incremental learning mode from the (M+1)th frame after the initialize model Lambda0,k is obtained, and carrying out sequential recursion to obtain chances of occurrence of the noise value and the voice signals on the kth frequency point of the ith frame. The invention is a tight coupling solution of spectrum estimation and voice mobility detection, which can enhance the adaptability of the voice application system to the noise environment. The invention is independent from the hypothesis of the noise initialization, and can provide the description of the voice mobility on the time frequency two-dimensional space.

The invention discloses a system and a method for testing laser frequency noise power spectrum density based on a Mach-Zehnder interferometer. A laser central frequency is locked at an orthogonal point of an interference curve of the Mach Zehnder interferometer through a feedback loop, so the system works in a good linearity range, converts laser frequency noise into light intensity, and fulfillsan aim of performing direct test through an electronic frequency spectrograph; and a frequency observation point avoids 1 / f noise of the test system through the frequency shift by an acousto-optic frequency shifter. The invention provides the stable testing method which is used for testing laser frequency noise power spectrum density, is applied to an electronic frequency spectrograph, provides ameans for analyzing the influence of laser frequency noise on an optical system, and has significant scientific meaning and application value.

The invention relates to the technical field of speech processing and specifically provides a method and a device for estimating a noise power spectral density of a speech signal. The method comprisesthe following steps: extracting a time context window feature from a noise speech signal, inputting the time context window feature to a pre-trained speech existence probability estimator which outputs an estimated speech existence probability corresponding to a current time frame; correcting the estimated speech existence probability according to a Bayes rule to determine a speech existence probability; determining the noise power spectral density corresponding to the corresponding time frame based on the speech existence probability according to a recursive smoothing formula. Through the technical scheme of the invention, the estimation accuracy of the noise power spectral density is improved in the case of small computing resources, thereby being beneficial to effectively eliminating noise signals, minimizing distortion in the speech processing process and improving speech enhancement performance.

The invention provides a method for enhancing the robustness of a voice recognition system. The method comprises the following steps of: updating a long term average value of power spectrum of a voice signal and a minimum valve of the long term average value of the power spectrum of the voice signal according to transient power spectrum of a current signal segment; calculating the signal-to-noise ratio, of the voice signal, in a first frequency domain according to the minimum value of the long term average value of the power spectrum; judging whether an effective voice exists in different frequency distribution areas according to the signal-to-noise ratio in the first frequency domain; estimating transient power spectrum of a first noise based on the judgment; updating the long term average value of the power spectrum of a noise and the minimum value of the long term average value of the power spectrum of the noise according to the estimated power spectrum of the first noise; re-estimating the signal-to-noise ratio, of the voice signal, in a second frequency domain according to the updated minimum value of the long term average value of the power spectrum of the noise; estimating the probability of the existence of the voice in a frequency domain according to the secondarily-estimated signal-to-noise ratio in the second frequency domain; and estimating the power spectrum of a second noise based on the probability of the existence of the voice.