242 results about "Source separation" patented technology

The invention discloses a mixed tower structure full-life damage intelligent sensing array early warning system, which relates to the field of mixed tower structure detection and comprises a multi-modal data collection module, an array topology optimization module, a self-adaptive signal processing module, a digital twin life prediction module, a grading early warning module and a visualization system. The multi-modal data collection module comprises a multi-modal sensor array, a self-powered module and a wireless transmission module. According to the invention, a full-scale sensing network is constructed, full-dimension damage perception from distributed monitoring to sudden damage capture and structural modal analysis is realized, wavelet transform and blind source separation are combined to eliminate environmental noise interference, a damage characteristic ultrasonic attenuation coefficient, an acoustic emission energy spectrum peak value, optical fiber strain gradient anomaly and vibration modal frequency deviation are extracted, and the detection accuracy is improved. And classification and positioning of damage types and intelligent diagnosis of severity levels are realized through a convolutional neural network and long and short memory neural network hybrid model, and a closed-loop processing flow from data acquisition to feature analysis is formed.

The invention relates to the related technical field of vibration noise control, in particular to an intelligent vibration noise control method and system using frequency spectrum characteristics, and the method comprises the steps: collecting rail transit vibration noise signals, extracting time-frequency domain mixed characteristics, carrying out multi-source separation, generating a noise source contribution degree matrix, configuring a strategy set, and monitoring changes to generate feedback. And the active damper is adjusted in real time for noise reduction. The technical problems that the noise suppression effect is limited and the noise reduction requirement of a complex time-varying scene cannot be accurately matched due to the fact that vibration noise control is difficult to adapt to a dynamically changing vibration noise environment are solved, time-frequency domain mixed features are extracted, overlapped noise sources are identified and separated more accurately, complex characteristics of vibration and noise are comprehensively considered, and the noise reduction effect is improved. And the active damper is dynamically adjusted so as to adapt to changes of vibration and noise, and the accuracy and effectiveness of dynamic adjustment are improved.

The invention relates to the technical field of voice recognition, in particular to a multi-channel voice conversion and synchronous transmission system applied to simultaneous interpretation. Multi-language voice signals are collected through a multi-microphone array, background noise is dynamically eliminated by adopting a noise suppression technology combining spectral subtraction and deep learning, and a dynamic information source verification symbol is generated to ensure data synchronization integrity; a blind source separation technology is combined with time-frequency analysis and time delay estimation to realize multi-language signal separation and synchronization, and a time sequence is dynamically adjusted through voice activity detection; an end-to-end ASR-NMT-TTS model is constructed to realize voice real-time translation and synthesis, and low-delay transmission is carried out based on a 5G network; the real-time monitoring module is adopted to dynamically adjust the output delay and the signal-to-noise ratio, and the translation delay and the synchronization precision are optimized in combination with user feedback. According to the method, dynamic noise reduction, multi-source synchronization and a self-adaptive feedback mechanism are integrated, the problems of distortion and delay of multi-language simultaneous transmission in a complex noise environment are solved, and the obvious technical synergistic effect and practicability are achieved.

Systems and methods includes receiving a single-track audio input stream having a mixture of audio signals generated from a plurality of sources, training an audio source separation model using, at least in part, the received single-track audio input stream, and separating audio sources, using the audio source separation model, from the audio input stream in accordance with one or more processing recipes to generate a plurality of source separated output stems. The audio separation model is trained to receive the single-track audio input stream and generate a plurality of audio stems corresponding to one or more audio sources of the plurality of sources.

The invention provides a GIS equipment detection system and method based on partial discharge-vibration signal fusion, and relates to the technical field of GIS equipment detection. The method has the flexibility of adapting to different data conditions, and vibration signals and partial discharge signals at a contact and a basin-type insulator are respectively obtained through blind source separation of collected multi-source signals for GIS equipment fault detection; the respective sequential relationship of the vibration signal and the partial discharge signal is considered, the multi-scale fuzzy entropy of the vibration signal and the partial discharge signal is calculated, the richness represented by the vibration characteristic and the partial discharge characteristic is further enhanced, the problems of poor reliability and large detection error of a fault detection method based on a single technology are effectively solved, and the fault detection efficiency is improved. The problems that the characteristics of an existing vibration diagnosis system and the characteristics of an existing partial discharge diagnosis system are prone to omission and low in accuracy are solved. Meanwhile, a three-dimensional digital twinborn visual model of the GIS equipment is also established, and the operation of the GIS equipment can be monitored in real time in combination with fault information.

The invention discloses an acoustic automatic recognition system and method for birds in a wetland environment, relates to the technical field of ecological monitoring and acoustic signal recognition, and provides a four-step flow of multi-source noise modeling, blind source separation and noise reduction, multi-label recognition and multi-modal fusion feedback for a wetland complex acoustic environment. The method comprises the following steps: 1, constructing an acoustic dictionary and mixing noise and twitter by using a contribution coefficient; 2, performing multi-sound-source unmixing by using a self-supervision method, and outputting a quasi-pure orbit; step three, identifying and labeling overlapped twitter by combining multi-label classification and dynamic spectrum enhancement time enhancement factors and frequency enhancement factors; and 4, judging the noise type through the microphone array and external environment parameter fusion, updating the noise template and the birdsong template to form closed-loop iteration, maintaining high recognition precision in a noisy and multi-species chorus scene, and providing efficient support for wetland ecological monitoring and protection decision.

The present disclosure relates to a method and audio processing system (1) for performing source separation. The method comprises obtaining (S1) an audio signal (Sin) including a mixture of speech content and noise content, determining (S2a, S2b, S2c), from the audio signal, speech content (formula A), stationary noise content (formula C) and non-speech content (formula B). The stationary noise content (formula C) is a true subset of the non-speech content (formula B) and the method further comprises determining (S3), based on a difference between the stationary noise content (formula C) and the non-speech content (formula B) a non-stationary noise content formula D), obtaining (S5) a set of weighting factors and forming (S6) a processed audio signal based on a combination of the speech content (formula A), the stationary noise content (formula C), and the non-stationary noise content (formula D) weighted with their respective weighting factor.(Ŝ1)  formula A({circumflex over (N)}1)  formula B({circumflex over (N)}2)  formula C({circumflex over (N)}NS)  formula D

The invention discloses a music source separation method and wearable equipment, and relates to the technical field of signal processing, music source separation is performed by adopting a neural network model of a UNet architecture, the neural network model at least comprises an encoder and a decoder, and the method comprises the following steps: obtaining an original signal of a to-be-separated music source, and converting the original signal to a frequency domain to obtain a frequency domain signal; performing feature coding processing on the frequency domain signal through an encoder to obtain coded feature data, the feature coding processing including feature extraction, and the encoder performing feature extraction processing at least by using a TFC-TDF block of causal convolution; and performing feature decoding processing on the coded feature data through a decoder, and outputting to obtain a music source separation result. According to the method and the device, low-delay music source separation is realized in the end side equipment with limited resources.

The invention relates to the technical field of signal reconstruction, in particular to a complex electromagnetic environment-oriented millimeter wave signal blind source separation and reconstruction system, which comprises a frequency spectrum trend division module, a path fading construction module, an initial cluster label generation module, a multi-solution path screening module and a fusion reconstruction execution module. According to the method, power spectral density sequence processing is carried out on millimeter wave frequency domain data, a multi-dimensional feature group is formed according to a path loss factor, an angle of arrival and the like, and an initial feature cluster is screened through an Euclidean distance, so that the accuracy of signal source classification is effectively improved; after the frequency domain response and the phase contour are continuously subjected to point comparison, path screening is completed by integrating a mean square error residual value, the path misjudgment probability is reduced, time domain resampling and phase frequency offset standardization are executed in fusion reconstruction, the consistency and fidelity of signal reconstruction are enhanced, and the reconstruction precision is improved. The whole process improves the separation accuracy and reconstruction precision of mixed signals in a complex electromagnetic environment.

The invention relates to the technical field of geodetic survey and structural health monitoring, and discloses a GNSS-RTK coordinate domain error correction method, which comprises the following steps: acquiring GNSS-RTK dynamic observation data to form a mixed signal time sequence; executing an improved adaptive noise complete empirical mode decomposition algorithm on the sequence to obtain an intrinsic mode function component; identifying and eliminating high-frequency components representing Gaussian white noise based on an energy coefficient, and reconstructing residual components into a signal sequence after primary noise reduction; inputting the noise reduction sequence into a rapid independent component analysis model for blind source separation; and finally, carrying out sorting and phase and amplitude uncertainty correction on the separated independent components, and outputting a multi-path error model and a structure dynamic deformation signal. According to the method, the problem of blind source separation failure or low precision caused by strong noise covering source signal statistical characteristics is solved through a strategy of first noise reduction and then separation, and an effective physical signal can be accurately extracted from a strong noise background.

A source separation apparatus includes circuitry configured to create a second observation signal vector by using (i) a first observation signal vector representing an observation signal that is obtained by mixing target signals from a plurality of signal sources and (ii) a time delay set in which a time delay until a part of the target signals is observed is used as an element, the second observation signal vector being obtained by extending the first observation signal vector in consideration of the time delay; optimize, by using a predetermined algorithm, a parameter for a correlation matrix representing a transmission characteristic in consideration of the time delay for the target signals, and power of a given signal source at each time; and separate the target signals by using an optimized parameter and the second observation signal vector.

The present disclosure describes techniques for implementing improved audio source separation. A complex spectrum X is split into a plurality of K bands along a frequency axis by applying band-split operations on the complex spectrum X. The complex spectrum is a time-frequency representation of audio signals. Each of the plurality of K bands is denoted as Xk, k=1, . . . , K. Each band Xk comprises one or more frequency bins. Each individual multilayer perceptron is applied to each band Xk to extract latent representations and obtain outputs Hk0. A time-domain transformer and a frequency-domain transformer are applied on a stacked representation H0. Time-domain and frequency-domain transformers are repeatedly applying in an interleaved manner for L times to obtain HL output from the transformer blocks. The HL is input into a multi-band mask estimation sub-model. A complex ideal ratio mask is generated based on outputs from the multi-band mask estimation sub-model.

The invention discloses a robot motion control method and system based on voice interaction, and relates to the technical field of robot motion control, and the method comprises the steps: extracting each segment of independent audio signal and keywords conforming to a preset voice library from mixed multi-channel audio based on a blind source separation technology; and selecting the keyword with the highest confidence coefficient as an execution command word, obtaining a standard track of an action corresponding to the execution command word, generating an executable simulation track of the execution command word in combination with real-time environment information, and controlling the robot to execute motion according to the simulation track after judging that the simulation track is safe and feasible. According to the method, the multi-channel mixed audio is separated into the independent signals, the execution command word is determined after the voice sources of different users are distinguished, the motion trail generated by simulating the conditions of the corresponding preset action and the enforceable environment is utilized, and the multi-dimensional characteristics of the motion trail and the standard trail of the preset action are compared and analyzed; and evaluating the feasibility of the motion trail corresponding to the execution command in the environment.

Various embodiments of the present disclosure provide methods, apparatuses, systems, and / or devices that are configured to separate multi-source audio signal samples into discrete source separated channel audio samples using trained multi-modal audio source channelization models. Multi-source audio signal samples are difficult to separate because they can include multiple target audio sources (e.g., individual speakers) that are often inter-mixed and overlaid with other audio sources such as noise, music, reverberations, and other audio artifacts. The multi-modal audio source channelization models discussed herein are trained to generate source separated channel audio samples based on audio signal samples and on video signal samples.

The invention provides a GIS partial discharge positioning method based on deep learning and acoustoelectric combination, and relates to the field of data processing. The method comprises the following steps: firstly, acquiring an original ultrahigh frequency signal and an original ultrasonic signal of a target detection entity, constructing an acoustoelectric signal matrix, and performing short-time Fourier transform to obtain a complex spectrum tensor and a power spectrum tensor; and then, extracting a high-dimensional feature embedding tensor by using a double-branch deep learning network, and generating a time-frequency masking tensor and a source confidence tensor through a multi-source sub-ion network to complete source-by-source spectrum reconstruction. And a time difference of arrival matrix is calculated by using a phase transformation generalized cross-correlation method, a sensor space coordinate is combined to be input into the depth regression positioning sub-network, and a discharge source is output to predict three-dimensional coordinates and regression uncertainty. And finally, fusing the regression uncertainty and the source confidence tensor to construct a confidence interval, and generating a visual positioning result with an ellipsoid confidence boundary. By implementing the technical scheme provided by the invention, the accuracy of partial discharge positioning is improved.

The invention relates to an array microphone noise reduction recording method based on cascade noise reduction and blind source separation, which belongs to the technical field of voice signal processing and recording, and comprises the following steps: configuring a multi-channel array microphone, ensuring that the amplitude and phase of a channel signal are consistent, and collecting an original multi-channel voice signal; a weighted kernel function blind source separation algorithm is adopted, signal-to-noise ratio distribution characteristics of signals are extracted, kernel function weights are given, and target voice and interference signal components are obtained through decoupling of an independent component analysis model; executing target-oriented adaptive cascade noise reduction, locking the voice of a keynote speaker through directional pickup, reducing noise, filtering out reverberation, and enhancing the voice of a far-field target by combining a voice mask neural network with a far-field pickup algorithm in sequence; and processing the target voice through voice feature perception lossless coding and storing the target voice. According to the invention, stable acquisition of multi-channel signals, accurate separation of mixed signals and layered suppression of noise reverberation are realized, the signal-to-noise ratio and definition of far-field voice are significantly improved, and the method is suitable for single-person speaking or multi-person dialogue scenes.

The invention relates to the technical field of smart home control, and particularly discloses a smart home central control system and method based on multi-mode perception, and the system comprises the steps: synchronously collecting a voice audio signal, a gesture image signal, an infrared thermal imaging signal and a millimeter wave radar signal through a plurality of groups of sensors; performing blind source separation processing on the voice and gesture signals, extracting a voice command component and a gesture action component which are independent in statistics, and performing space-time alignment and Kalman filtering fusion on the infrared and radar signals to generate a dynamic environment sensing graph; voice intention features, gesture track features and environment anomaly features are extracted through Mel frequency cepstrum coefficient analysis, skeleton key point tracking and multi-level convolution processing; constructing a three-dimensional decision matrix based on the features, performing weighted evaluation through a fuzzy logic rule base to generate a control instruction priority sequence, and dynamically adjusting an equipment operation mode according to the priority; according to the invention, the problems of control conflict and response delay caused by multi-mode signal coupling are solved.

The invention relates to the technical field of electric power operation and maintenance, in particular to a transformer fault diagnosis method, device and equipment and a storage medium, and the method comprises the steps: collecting a transformer acoustic monitoring signal based on an acoustic microphone array, carrying out blind source separation and decoupling, and constructing an acoustic signal feature set; transformer operation condition parameters are extracted, mechanical state degradation evaluation is carried out according to the acoustic signal feature set, and a mechanical state health degree index is generated; and calculating an acoustic signal receiving timestamp of the acoustic microphone array, performing sound source distribution mapping, and constructing a sound source spatial distribution diagram. According to the method, blind source separation decoupling, mechanical state degradation evaluation, sound source distribution mapping and fault situation diagnosis are performed by acquiring the acoustic signals and the operation condition parameters, so that tiny mechanical faults in the transformer can be accurately identified, intelligent maintenance decision is realized, and the method has the advantages that the tiny mechanical faults in the transformer can be detected earlier; the real-time performance and reliability of fault diagnosis are improved, and the accident risk caused by diagnosis lag is reduced.

The invention discloses an underdetermined blind source separation method based on improved single source point detection, and the method comprises the steps: firstly, carrying out the initial single source point extraction of a mixed signal through employing the cosine angle criterion of a real part and an imaginary part of a time-frequency matrix column vector; then, abnormal points are removed according to whether the linear relation between the single source points and the abnormal points exists or not, the influence of the abnormal points on single source point detection is reduced, and therefore the estimation precision of a hybrid matrix is improved; then, clustering is carried out on the obtained single source points by using a K-means algorithm, so that an estimated hybrid matrix is obtained; and finally, recovering a source signal by using a least square method. The method provided by the invention not only considers the absolute direction of the real part and the imaginary part in the single source point, but also considers the linear relation between different single source points to remove the abnormal points, so that the estimation precision of the hybrid matrix can be improved, the noise immunity of the method is enhanced, and finally, a high-quality recovery signal is obtained.

A conferencing system includes a plurality of microphones and an audio processing system that performs blind source separation operations on audio signals to identify different audio sources. The system processes the separated audio sources to identify or classify the sources and generates an output stream including the source separated content.

The invention discloses a voice signal blind source separation method, device and equipment and a storage medium, and relates to the technical field of voice separation, and the method comprises the steps: collecting a mixed sound signal of a plurality of source signals through a plurality of sound collection devices, and obtaining a plurality of time domain observation signals; converting the time domain observation signal into a frequency domain observation signal, and creating a frequency domain separation signal model based on the frequency domain observation signal and a preset separation matrix; obtaining the direction of arrival of the source signal to obtain direction of arrival information, and performing iterative updating on a preset separation matrix in the frequency domain separation signal model based on an independent vector analysis algorithm of an auxiliary function to perform blind source separation on the frequency domain observation signal to obtain a frequency domain separation signal; the geometric constraint of the iterative update of the preset separation matrix is the constraint created based on the direction of arrival information; and converting the frequency domain separation signal into a time domain separation signal. According to the invention, the efficiency of voice signal blind source separation can be improved, and the accuracy of blind source separation is remarkably improved.

The invention provides a login method and system based on a 5G message voiceprint verification code, and the method comprises the steps: generating a 5G message, receiving a message receiving confirmation feedback of a user, obtaining a composite audio stream through combining with environment sound collected based on a local voiceprint collection device, carrying out the processing through a blind source separation algorithm, obtaining a preliminary composite voiceprint sample, and carrying out the verification of the voiceprint verification code. A target composite voiceprint sample is obtained after optimization; performing encryption processing based on a preset encryption protocol, obtaining a decrypted target composite voiceprint sample in combination with a quantum key distribution mechanism and a cloud authentication platform, and calculating a preliminarily adjusted comprehensive score by using a convolutional neural network, a recurrent neural network and a generative adversarial network combination model; performing processing by utilizing a behavior analysis algorithm to obtain a final adjusted comprehensive score, and performing access permission confirmation to obtain an access confirmation result; according to the invention, the security and accuracy of user identity verification are enhanced, efficient user experience is provided, and the security and reliability of the user login process are ensured.

The invention provides a monitoring method and system for a cutting penetration state in a water-jet guided laser processing process based on sound signal blind source separation, and the monitoring method comprises the steps: S1, in the water-jet guided laser processing process, collecting a plurality of observation sound signals in real time, the observation sound signals being mixed sound signals composed of a plurality of independent sound source signals; s2, estimating and solving a plurality of independent sound source signals by using a blind source separation algorithm according to the plurality of collected observation sound signals; s3, the frequency spectrums of the multiple independent sound source signals are compared with sound signals generated during material laser processing, so that sound signals generated during water-guided laser processing due to material removal are determined from the frequency spectrums; and S4, the water-jet guided laser machining process is judged according to the relation that sound signals generated due to material removal change along with time. Compared with a traditional coaxial visual monitoring scheme, the method is not affected by factors such as water flow and light intensity, and can be applied to monitoring of the water-guided laser machining process.

The embodiment of the invention discloses a music source separation method and device, a network training method and device, equipment and a storage medium. The method comprises the steps that a to-be-processed first mixed music source is acquired; performing music source separation on the first mixed music source by adopting a target music source separation network to obtain prediction results of at least two sound components separated from the first mixed music source; wherein the target music source separation network is obtained by taking the first music source separation network as a teacher model, taking the second music source separation network as a student model, and performing knowledge distillation on the second music source separation network; a parameter amount of the first music source separation network is greater than a parameter amount of the second music source separation network. According to the method, knowledge distillation is carried out on the second music source separation network with smaller parameter quantity by using the first music source separation network with larger parameter quantity through a knowledge distillation technology, so that the target music source separation network with better performance and lower time delay can be obtained.

A method comprises: obtaining softmask values for frequency bins of time-frequency tiles representing an audio signal; reducing, or expanding and limiting, the softmask values; and applying the reduced, or expanded and limited, softmask values to the frequency bins to create a time-frequency representation of an estimated target source. An alternative method comprises, for each time-frequency tile: obtaining softmask values; applying the softmask values to the frequency bins to create a time-frequency domain representation of an estimated target source; obtaining a panning parameter and a source concentration estimates for the target source; determining, using the panning parameter estimate and the softmask values, a magnitude for the time-frequency representation of the estimated target source; determining, using the panning parameter estimate and the source phase concentration estimate, a phase for the time-frequency representation of the estimated target source; and combining the magnitude and the phase.

The invention discloses a voice privacy protection method and device based on high-frequency sound signal mixing, and the method comprises the steps: firstly collecting user voice data, and constructing a personalized user corpus; then a user customizes a scene demand, selects a text theme synthesized by speech, and carries out user corpus extension; then, the user selects a noise theme related to the interference from the selected text theme according to the specific living situation where the user is located, and voice segments are randomly extracted from a user corpus corresponding to the noise theme to generate interference noise; modulating the interference noise to a preset frequency band interval; and finally, when it is detected that the user is talking, the controller sends a modulated interference signal to an interference unit, and the interference unit injects the interference signal into a nearby microphone. The method has practicability and security, can effectively protect user privacy, and can resist attacks of various denoising technologies such as speech enhancement and blind source separation.

The invention relates to the technical field of power equipment fault monitoring, in particular to a distribution box fault monitoring method, and aims to solve the problem that a traditional monitoring method in the prior art depends on a single filtering or fixed threshold strategy and is difficult to effectively separate fault features. According to the method, mechanical vibration and electromagnetic interference signals can be effectively separated through an adaptive blind source separation algorithm, the problem of early fault signal identification in a mixed interference environment is solved by combining multi-scale feature fusion and a dynamic environment compensation mechanism, and the method has the advantages of improving monitoring accuracy and realizing accurate quantification of fault energy.

The invention discloses a leakage monitoring method based on an LNG fuel gas system. The leakage monitoring method comprises the steps that a self-adaptive frequency modulation continuous wave active acoustic scanning network is established; blind source separation is carried out on mixed signals in acoustic scanning network abnormal events by adopting self-adaptive kernel independent component analysis; constructing a leakage feature mapping model of the physical information neural network; leakage source accurate positioning and quantification based on acoustic tomography and Bayesian reasoning are carried out; multi-modal decision fusion is carried out based on the multi-dimensional data sources received in parallel, a false alarm suppression mechanism is set, and time continuity verification and space consistency verification are carried out; establishing a reinforcement learning model for autonomously optimizing a monitoring strategy according to environment change and system state, and realizing adaptive optimization; the strategy network after self-adaptive optimization is deployed at the cloud, actions are generated regularly according to the current state, the actions are issued to the regional gateway and the edge node for execution, and iterative updating is carried out, so that the monitoring accuracy in a complex environment is improved, and the false alarm rate is reduced.

An audio processing system and method are provided, the audio processing system comprising: an input interface that receives an input audio mix and transforms it into a time-frequency representation defined by values of time-frequency units; a processor that maps values of the time-frequency units into a hyperbolic space by executing an embedded neural network trained to associate the respective time-frequency units to high-dimensional inserts and projecting the respective high-dimensional inserts into the hyperbolic space; and an output interface that accepts a selection of at least a portion of the hyperbolic space and renders a selected hyperbolic embedding that falls within the selected portion of the hyperbolic space.

The invention discloses a sound compatibility-oriented audio frequency integrated management method, which comprises the following steps of: when any receiving end receives an external sonar signal, determining an enabled audio frequency function based on the working state of the receiving end, and searching an audio frequency characteristic under the corresponding audio frequency function from an audio frequency data management library; performing signal separation on the external sonar signals based on the audio frequency characteristics to obtain first-class audio frequency signals under audio frequency integrated management, and performing subtraction of the first-class audio frequency signals on the received external sonar signals to obtain second-class audio frequency signals; and carrying out signal filtering and separation on the second type of audio signals by adopting a blind source separation technology in a prior communication mode to obtain third type of audio signals sent by the friend sonar equipment which does not establish communication connection. According to the invention, the integrated management technology of audio frequency characteristics is utilized, and the accurate audio frequency characteristic matching and signal separation technology is adopted, so that the interference of external sonar signals is effectively reduced, and the definition and reliability of the sonar signals are improved.