126 results about "Time–frequency representation" patented technology

A system and method for monitoring a patient includes a sensor configured to acquire physiological data from a patient and a processor configured to receive the physiological data from the at least one sensor. The processor is also configured to apply a spectral estimation framework that utilizes structured time-frequency representations defined by imposing, to the physiological data, a prior distributions on a time-frequency plane that enforces spectral estimates that are smooth in time and sparse in a frequency domain. The processor is further configured to perform an iteratively re-weighted least squares algorithm to perform yield a denoised time-varying spectral decomposition of the physiological data and generate a report indicating a physiological state of the patient.

A method for visualizing a directional sound activity of a multichannel audio signal, wherein the multichannel audio signal comprises time-dependent input audio signals, comprising determining a directional sound activity vector from virtual sound sources determined from an active directional vector and a reactive directional vector determined from time-frequency representations of different input audio signals for each one of a plurality of time-frequency tiles; determining a contribution of each one of said directional sound activity vectors within sub-divisions of space on the basis of directivity information related to each sub-divisions of space and directional sound activity level within said sub-division of space by summing said contributions; displaying a visualization of the directional sound activity of the multichannel audio signal by a graphical representation of directional sound activity level within said sub-division of space.

This invention pertains to the extraction of the slowness dispersion characteristics of acoustic waves received by an array of two or more sensors by the application of a continuous wavelet transform on the received array waveforms (data). This produces a time-frequency map of the data for each sensor that facilitates the separation of the propagating components thereon. Two different methods are described to achieve the dispersion extraction by exploiting the time frequency localization of the propagating mode and the continuity of the dispersion curve as a function of frequency. The first method uses some features on the modulus map such as the peak to determine the time locus of the energy of each mode as a function of frequency. The second method uses a new modified Radon transform applied to the coefficients of the time frequency representation of the waveform traces received by the aforementioned sensors. Both methods are appropriate for automated extraction of the dispersion estimates from the data without the need for expert user input or supervision

A speech recognition system for transforming an acoustic signal into a stream of phonetic estimates includes a frequency analyzer for generating a short-time frequency representation of the acoustic signal. A novelty processor separates background components of the representation from region of interest components of the representation. The output of the novelty processor includes the region of interest components of the representation according to the novelty parameters. An attention processor produces a gating signal as a function of the novelty output according to attention parameters. A coincidence processor produces information regarding co-occurrences between samples of the novelty output over time and frequency. The coincidence processor selectively gates the coincidence output as a function of the gating signal according to one or more coincidence parameters. A vector pattern recognizer and a probability processor receives the gated coincidence output and produces a phonetic estimate stream representative of acoustic signal.

The invention discloses a method for intelligently diagnosing a rotating machine fault feature based on a deep CNN model. The method comprises: (1) acquiring rotating machine fault vibration signal data, segmenting the data and performing de-trend item preprocessing; (2) performing short-time Fourier time-frequency transform analysis on the signal data to obtain the time-frequency representation of each vibration signal, and displaying the time-frequency representation with a pseudo-color map; (3) reducing the image resolution by an interpolation method and superimposing respective images to form a training sample and a test sample as inputs of the CNN; (4) constructing the deep CNN model including an input layer, two convolution layers, two pooling layers, a fully connected layer, and a softmax classification layer and an output layer; and (5) introducing the training sample into the model for training, obtaining a convolution feature, a pooling feature and a neural network structuralparameter, and diagnosing unknown fault signals according to the constructed deep CNN. The method has better accuracy and stability than an existing time-domain or frequency-domain method.

A method expands a bandwidth of an audio signal by determining a magnitude time-frequency representation |G(ω,t) for example audio signals g(t). A set of frequency marginal probabilities P_G(ω|z) 221 are estimated from |G(ω,t)|, and a magnitude time-frequency representation |X(ω,t)| is determined from an input signal audio signal x(t). Probabilities P(z), P_X(z) and P_X(t|z) are determined using P_G(ω|z)|X(ω,t)|. |Ŷ(ω,t)| is reconstructed according to P_zP_X(z)P_G(ω|z)P_X(t|z), and Ŷ(ω,t)| is transformed to a time domain to obtain a high-quality output audio signal ŷ(t) corresponding to the input audio signal x(t).

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.

The invention relates to a radar moving target detection method based on short-time sparse fractional Fourier transform (ST-SFRFT), and belongs to the technical field of radar signal processing and detection. The method comprises the steps of: performing radar echo demodulation and pulse compression to realize high range resolution, and selecting a range unit to be detected; performing spare time-frequency analysis parameter initialization, comprising selection of a short-time window function and a window length and design of a sparse decomposition dictionary; performing ST-SFRFT operation for spare optimization solution, thus completing high-resolution spare time-frequency representation of moving target echo; traversing all range search units, performing moving target signal spare domain detection and estimating a target moving parameter. By adopting a high-resolution spare representation method, high-resolution and low-complexity time-frequency representation of time-varying signals is realized at the time frequency-spare domain; and the method is suitable for analyzing the time varying non-stability of signals, suppresses background clutter while improving energy concentration of moving target signals, and significantly improves the radar moving target detection and parameter estimation capability.

In accordance with an embodiment, a time-frequency post-processing method of improving perceptual quality of a decoded audio signal, the method includes determining a time-frequency representation (such as filter bank analysis and synthesis) of an audio signal, estimating a time-frequency energy distribution of an audio signal from a time-frequency filter bank, computing a modification gain for each time-frequency representation point to have a modified time-frequency representation, and outputting audio signal from a modified time-frequency representation.

Systems, methods, and computer-readable storage media for editing the frequency content of an image may provide an intuitive interface for examining and/or modifying the frequency content. The methods may include accessing image data, performing a wavelet transform of the image data (or another MRA technique) to produce a time-frequency representation (TFR) of the image representing frequency content in multiple frequency bands, and displaying an indication of the frequency content of the image by displaying a sub-image of the TFR or numerical data representing the frequency content at selected pixels. In response to receiving input specifying a desired frequency content modification, the methods may include performing a Fourier transform of the image data to produce frequency content data, modifying the frequency content data, and performing an inverse transformation of the modified data to produce modified image data. The modification may be applied globally or to a selected portion of the image.

The invention discloses a synchronous extrusion and transformation-based earthquake weighting average instantaneous frequency extracting method. On the basis of three-parameter wavelet transformation, energy rearrangement is conducted by evaluating a rearrangement rule (instantaneous frequency) to obtain more accurate and sparser time frequency representation, so that an effective signal energy distribution space is determined accurately; on this basis, threshold de-noising is introduced to perform noise suppression; finally a method for evaluating noise-containing signal weighing instantaneous frequency is provided. The calculation result of the method has high noise-resistant performance and precision by calculating the instantaneous frequency of noise-free and noise-containing synthetic seismic record and comparing the noise-resistant performance of different methods; when the method is applied to actual data, the characteristic of a reservoir stratum can be clearly described.

The application relates to an audio processing system and a method of processing a noisy (e.g. reverberant) signal comprising first (v) and optionally second (w) noise signal components and a target signal component (x), the method comprising a) Providing or receiving a time-frequency representation Y i (k,m) of a noisy audio signal y i at an i th input unit, i=1, 2, ..., M, where M‰¥2; b) Providing (e.g. predefined spatial) characteristics of said target signal component and said noise signal component(s); and c) Estimating spectral variances or scaled versions thereof » v, »x of said first noise signal component v (representing reverberation) and said target signal component x, respectively, said estimates of »v and »x being jointly optimal in maximum likelihood sense, based on the statistical assumptions that a) the time-frequency representations Y i (k,m), X i (k,m), and V i (k,m) (and W i (k,m) ) of respective signals y i (n), and signal components x i , and v i (and w i ) are zero-mean, complex-valued Gaussian distributed, b) that each of them are statistically independent across time m and frequency k, and c) that X i (k,m) and V i (k,m) (and W i (k,m)) are uncorrelated. An advantage of the invention is that it provides the basis for an improved intelligibility of an input speech signal. The invention may e.g. be used for hearing assistance devices, e.g. hearing aids.

The problem of operationally analyzing spread spectrum frequency-hopping transmissions has been known since the invention of frequency-hopping radios. A frequency-hopping radio transmits a communication using small signal segments of data making up the transmission in accordance to a set of predetermined parameters. In this type of transmission each small segment of data is transmitted at a different frequency, one after another, until the transmission is completed. In some cases, it may be desirable to analyze these radio transmissions and to be able to determine radio spectrum usage and provide a mean to report on interference in frequency bands where multiple types of transmissions are possible. A novel method of isolating multiple transmission signals that are transmitted using frequency-hopping and apparatus is thus proposed which receives at least a transmission and performs operations within frequency bands of the received transmission in order to monitor and characterize signal energy therein.

The invention discloses a seismic data attenuation compensation method in a synchronous extrusion transform domain. In the synchronous extrusion transform domain, the attenuation compensation process is represented as an inverse problem with L1 norm regularization, solving is carried out through an iterative re-weighting algorithm, and thus, stabilized attenuation compensation is realized. Synchronous extrusion transform can suppress diffusion of time frequency energy, a more sparse and highly-localized time frequency representation is obtained, and thus, attenuation compensation on nonstationary seismic signals is facilitated. The method uses the sparse properties of signals in the synchronous extrusion transform domain, the L1 norm regularization method is introduced to stabilize the attenuation compensation process, and a noise amplification problem is thus avoided. The disclosed method is used to realize compensation on seismic attenuation effects, the seismic data resolution can be enhanced, and convenience is brought to subsequent seismic inversion and reservoir description.

Audio processing methods may involve receiving audio data corresponding to a plurality of audio channels. The audio data may include a frequency domain representation corresponding to filterbank coefficients of an audio encoding or processing system. A decorrelation process may be performed with the same filterbank coefficients used by the audio encoding or processing system. The decorrelation process may be performed without converting coefficients of the frequency domain representation to another frequency domain or time domain representation. The decorrelation process may involve selective or signal-adaptive decorrelation of specific channels and/or specific frequency bands. The decorrelation process may involve applying a decorrelation filter to a portion of the received audio data to produce filtered audio data. The decorrelation process may involve using a non-hierarchal mixer to combine a direct portion of the received audio data with the filtered audio data according to spatial parameters.

The invention discloses a test method of periodic transient characteristics in signals, uses a sensing device for inputting and implementing analog-to-digital conversion, obtains a signal x(t), and detects whether the transient characteristics with a period T1, T2 or Tm exist in the signal x(t), where m is a positive integer. The test method is characterized in that the test method comprises the following steps: the time frequency decomposition of the signal x(t) is implemented, and the time frequency representation TFRX(t, f) of the signal characteristics is obtained; m polar coordinate mappings are respectively built according to the periods T1, T2 or Tm; each mapping is represented on a polar coordinate figure; when enhanced characteristic representation appears in the polar coordinate figure that is corresponding to the period Tn (n lies in a closed interval between 1 and m), whether the transient characteristics with the period Tn exist in the detected signal is decided. The test method of the periodic transient characteristics in signals conveniently realizes period deciding, improves the efficiency and the accuracy of period deciding, can realize automatic detection of the periodic transient characteristics, and is particularly applicable to automatic failure identification of mechanical equipment.