55 results about "Sound separation" patented technology

The invention relates to a power equipment fault diagnosis prediction method and device and electronic equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting sound data and electrical data of a plurality of pieces of power equipment, positioning noise data from the sound data, and extracting fault features according to a time-varying characteristic curve of the sound data of the positioned noise data; positioning the fault time nodes with the extracted fault features, and matching the electrical data corresponding to the fault time nodes to obtain fault electrical data; separating noise data in the sound data by using a sound separation model to obtain the noise data of each power device, and determining the fault of each power device according to the noise data of each power device, the time-varying characteristic curve and the matching degree of the fault electrical data; and predicting a fault trend according to the data exception trend of the fault type of the fault. By applying the scheme of the invention, the accuracy and reliability of partial discharge detection can be improved, and the risk of missing detection or misjudgment can be reduced.

The invention relates to the technical field of wind tunnel flow sound separation, in particular to a method for separating acoustics and related pneumatic wall pressure fluctuation in a closed wind tunnel, which comprises the following steps: acquiring sound pressure of a measurement signal in the closed wind tunnel, and storing the sound pressure in a cross-spectrum matrix; constructing a related pneumatic wall pressure fluctuation interference model of the closed wind tunnel according to physical factors such as convection velocity in the closed wind tunnel, a related transverse attenuation degree and a related longitudinal attenuation degree; constructing a related pneumatic wall pressure fluctuation interference model and a related pneumatic wall pressure fluctuation interference-acoustic separation model of probability factor analysis according to the low-rank property and the sparsity; according to the sparse prior information, constructing a prior model to constrain a related pneumatic wall pressure fluctuation interference-acoustic separation model; and obtaining maximum likelihood estimation of the parameters through an expectation maximization algorithm, and determining contribution degrees of acoustics and related pneumatic wall pressure fluctuation pressure of the to-be-tested component in the closed wind tunnel. The problem that an existing method does not consider interference physical information and is difficult to be directly applied to closed wind tunnel flow-sound separation is solved.

A computer-implemented method of applying a trained neural network for sound separation based on distance estimation is provided. The method includes receiving, by an audio input component of a computing device, an audio mixture from one or more sources. The method includes predicting, by a trained distance estimation neural network and based on the audio mixture, respective distances of the one or more sources from the audio input component. The method includes determining one or more near sounds and one or more far sounds based on the respective distances. The near sounds correspond to sources that are located within a threshold distance of the audio input component, and the far sounds correspond to sources that are not located within the threshold distance of the audio input component. The method includes providing the predicted one or more near sounds.

The invention relates to the field of intelligent environment control, in particular to a method and system for controlling operation of air conditioning equipment and a storage medium. The method comprises the following steps: acquiring external sound; based on a voiceprint library of the air conditioning equipment, air conditioning equipment noise and environment noise are separated from the external sound in a frequency domain; setting, for each frequency in the spectrum of the external sound, a weight indicating a corresponding tolerance of noise at that frequency; on the basis of the set weight, whether the noise of the air conditioning equipment at the frequency affects the user experience or not is determined for each frequency; and when the noise of the air conditioning equipment at any frequency affects the user experience, operation of the air conditioning equipment is adjusted.

The invention discloses a physiological sound separation and extraction method, and relates to the technical field of body sound signal processing and analysis. Comprising the steps of collecting human body physiological sound signals, constructing a multi-level signal separation model based on statistical characteristics, sequentially realizing sound signal elimination and extraction of various physiological sound signals, establishing a multi-dimensional characteristic parameter set, and performing intelligent identification and classification on the extracted physiological sound signals in combination with a deep learning classifier. And carrying out physiological and pathological state evaluation according to an identification and classification result. By comprehensively applying various signal processing and classification algorithms, automatic separation and accurate classification of heart sound, lung sound and borborygmus signals are realized, the physiological sound signal processing efficiency and precision are remarkably improved, and clinical doctors can be helped to quickly judge diseased regions and diseased property.

The invention discloses a sound separation and target sound extraction method based on a unified architecture, and relates to the technical field of audio signal processing, and the method comprises the steps: in a first training stage, employing an attractor network to estimate the number of sound sources in a mixed audio signal, and generating attractor embedding; the attractors are embedded and input into the separation backbone network, and separated sound is generated; in the second training stage, the multi-mode clue processing network is adopted to process clue information of multiple modes, and clue embedding is generated; randomly selecting attractor embedding or clue embedding as input for separating the backbone network; in the training stage, an alignment loss function between attractor embedding and clue embedding is calculated; in the reasoning stage, if there is no clue information, a sound separation task is executed; and if one to three pieces of clue information exist, executing a target sound extraction task. The task is flexibly selected and executed according to the input clue condition, better adaptability and flexibility are achieved, and the method is suitable for complex sound scenes.

A system for controlling the operation of a machine that includes a plurality of actuators assisting one or more tools in performing one or more tasks, in response to receiving an acoustic mixture of signals generated by the tools performing the tasks and signals generated by the plurality of actuators actuating the tools, submits the acoustic mixture of signals into a neural network that is trained to separate the signals generated by the tools performing the tasks from the signals generated by the actuators of the actuating tools from the acoustic mixture, to extract the signals generated by the tools performing the tasks from the acoustic mixture of signals, analyzes the extracted signals to generate a state of the execution of the tasks, and performs a control action selected according to the state of the execution of the tasks.

The present invention addresses the problem of providing an "audio signal processing device" that achieves a good sense of localization with respect to a sound having a narrow frequency band. [Solution] The present invention is provided with signal processing units provided so as to correspond to respective channels of audio signals of a plurality of channels, and each signal processing unit has: a target sound separation unit (411) that separates a target sound signal indicating a target sound, which is a sound to be subjected to localization enhancement, from a source sound signal, which is an audio signal of the corresponding channel; a sense-of-localization-enhanced sound generation unit that generates a sense-of-localization-enhanced sound signal in which the frequency of the target sound signal separated by the target sound separation unit (411) is multiplied by k (k is an integer of 2 or more); and a synthesis unit that synthesizes and outputs the localization-enhanced sound signal generated by the localization-enhanced sound generation unit and the source sound signal.

The present application relates to the field of audio processing, in particular to a sound separation method and system based on a vector microphone, the method comprising: constructing a fourth-order cumulant matrix of a sound signal received by the vector microphone, solving sound source direction angle estimation and sound source pitch angle estimation of each incident signal; constructing a spatial characteristic matrix of each incident signal at different frequencies; based on the spatial characteristic matrix, calculating a spatial correlation matrix estimation of the sound signal at different frequencies and different frame numbers, and constructing a spatial correlation matrix of the sound signal at different frequencies and different frame numbers; constructing an orthogonal MNMF model; adjusting the variables of the MNMF through the multiplication update method, so that the difference between the spatial correlation matrix estimation and the spatial correlation matrix is less than a preset target, to obtain an optimal spatial correlation matrix estimation; and separating the sound signal based on the optimal spatial correlation matrix. The present application effectively solves the sound separation problem when the distance between sound sources is close, and improves the accuracy of sound separation.

The invention relates to the technical field of sound processing, and provides a sound separation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a first sound signal and a second sound signal; based on the short-time energy values of the first sound signal and the second sound signal and the linear combination relation between the environment sound and the coherent sound contained in the first sound signal and the second sound signal, determining the short-time energy values of the environment sound and the coherent sound and difference factors of the coherent sound on different channels; based on a weight mapping relation, the short-time energy values of the environment sound and the coherent sound and the difference factors are mapped into sound separation weights, and the weight mapping relation is obtained through linear fitting based on a linear combination relation; the ambient sound and the coherent sound are separated from the first sound signal and the second sound signal based on the sound separation weight. According to the method, the device, the electronic equipment and the storage medium provided by the invention, the effectiveness and the reliability of separation of the environment sound and the coherent sound are ensured.

The invention belongs to the field of sound separation processing, and discloses an individual perception multi-bird sound separation method and system based on identity embedding, and the method comprises the steps: S1, carrying out the preprocessing of an audio segment containing bird sound, obtaining a preprocessed audio segment, and obtaining a time domain waveform corresponding to the audio segment; s2, carrying out separation processing on the time domain waveform to obtain a separated individual audio waveform; and S3, based on the separated individual audio waveform, carrying out classification identification on the bird sound. According to the method, under the conditions that voiceprint differences of individuals of the same type of birds are extremely small and sound sources are highly overlapped, stable individual consistency and separation precision can still be kept. According to the method, the technical bottleneck of identity drift and pseudo separation of a traditional model in long-time sequence monitoring is solved, refinement and automation of individual-level ecological acoustic analysis are also realized, and a feasible technical path is provided for field long-term ecological monitoring and acoustic big data processing.

The invention relates to a three-dimensional point cloud fused power transformation main equipment sound source separation method, which comprises the following steps of: acquiring high-density point cloud data, preprocessing the point cloud data, and performing equipment segmentation; extracting sound signal features; fusion features are obtained; inputting the fusion features into a sound source separation network based on Transform, and carrying out sound signal separation to obtain separated sound signals; and inputting the separated sound signals into a sound source separation network based on Transform to carry out iteration for multiple times to optimize a separation result. The invention provides a novel multi-sound-source separation method, and the method can remarkably improve the sound source positioning and recognition precision through the combination of the three-dimensional point cloud and sound signals. Compared with a traditional sound separation method, the method has the advantages that a sound source can be positioned more accurately by means of the three-dimensional point cloud data, noise interference is reduced, the reliability of substation equipment fault diagnosis is improved, and the method has wide application prospects and particularly has important significance in the aspects of automatic monitoring and maintenance of a power system.

To provide a recording device, a recording system, and their recording method for separating vocalizations of a user from those of surrounding persons when the vocalizations of a plurality of persons including the user are collected using a bone-conducting microphone and an air-conducting microphone.SOLUTION: An embodiment of the present invention comprises: a bone-conducting microphone 10 that collects vocalizations of a user and generates a bone-conducting sound signal; an air-conducting microphone 11 that collects vocalizations of a plurality of persons including the user and generates an air-conducting sound signal; an arithmetic unit 36 that performs a process for separating vocalizations of the user based on the bone-conducting sound signal from vocalizations of the plurality of persons based on the air-conducting sound signal and generates separation sound data; and a memory unit 42 that stores the bone-conducted sound data including the user's vocalizations generated based on the bone-conducted sound signal and the separation sound data including the vocalizations of persons other than the user generated based on the separated sound signal, respectively.SELECTED DRAWING: Figure 3

The invention discloses a music audio processing method and device, equipment and a storage medium. The music audio processing method comprises the following steps: acquiring a plurality of frames of target music signals in music audio; performing drum sound separation processing on each target music signal to obtain a drum sound track signal of each target music signal; performing drum sound detection processing on each drum sound track signal to obtain a drum sound detection result of each target music signal, the drum sound detection result of each target music signal including that the target music signal is in a drum sound state or the target music signal is in a non-drum state; and processing each target music signal based on the drum sound detection result of each target music signal and the drum sound track signal of each target music signal to obtain a music audio processing result. According to the scheme, the sound quality of the processing result of the music audio can be improved.

The application discloses a mechanical arm deception attack side channel detection method based on acoustic characteristics and belongs to the technical field of deep learning, which comprises the following steps: constructing a training recognition model; receiving the collected target audio, performing a sound processing step operation to extract features, performing a training recognition step operation to output predicted motion data; comparing the predicted motion data with the obtained corresponding real-time motion data, and evaluating whether the difference between each pair of parameters exceeds a preset threshold value. The application uses sound separation technology to separate mixed multi-axis sound into different individual axis sound, constructs an acoustic motion information recognition model of each motion joint through a collaborative physical and data-driven method, identifies the target sound according to the recognition model, obtains the predicted motion information of each axis, compares the predicted motion information with the collected real-time motion command, combines a threshold value to determine whether a deception attack is received, and realizes mechanical arm deception attack side channel detection.

Systems and methods for audio source separation. A deep learning-based system uses azimuth angle position to separate an audio signal originating from a selected location from other sounds. Techniques for directing a virtual microphone towards a selected speaker are revealed. A deep learning-based audio regression method, which can be implemented as a neural network, learns to separate different speakers by effectively utilizing the spectral and spatial properties of all sources. The neural network can focus on multiple sources in multiple respective target directions and filter out other sounds. A user can select which source to hear. The network can use the time-domain signal and a frequency-domain signal to separate the target signal and produce separate audio outputs.The direction of the selected speaker relative to the microphone array can be entered into the system as a vector.

The application discloses a sound isolation method and device based on deep learning and a storage medium. The method comprises the following steps: obtaining an audio file used for constructing a DeepAudioSep model and preprocessing the audio file used for constructing the DeepAudioSep model; constructing the DeepAudioSep model and training the DeepAudioSep model, wherein the DeepAudioSep model comprises one mixed source input and ten isolated source outputs; and performing sound separation through the DeepAudioSep model. The application introduces the data driving and deep learning idea into sound separation and noise isolation processing, improves the sound separation and noise isolation processing capability in the environmental monitoring field, and therefore has wide noise processing prospects and practical value.

An audio device capable of inhibiting malfunction of an information terminal is provided. The audio device includes a sound sensor portion, a sound separation portion, a sound determination portion, and a processing portion. The sound sensor portion has a function of sensing sound. The sound separation portion has a function of separating the sound sensed by the sound sensor portion into a voice and sound other than a voice. The sound determination portion has a function of storing the feature quantity of the sound. The sound determination portion has a function of determining, with a machine learning model such as a neural network model, whether the feature quantity of the voice separated by the sound separation portion is the stored feature quantity. The processing portion has a function of analyzing an instruction contained in the voice and generating an instruction signal representing the content of the instruction in the case where the feature quantity of the voice is the stored feature quantity. The processing portion has a function of performing, on the sound other than a voice separated by the sound separation portion, processing for canceling the sound other than a voice. Specifically, the processing portion has a function of performing, on the sound other than a voice, processing for inverting the phase thereof.

A method to detect and analyze cough parameters remotely and in the background through the user's device in everyday life. The method involves consecutive or selective execution of 4 stages, which solve a variety of the following tasks: Detection of coughing events in sound, in environmental noise. Separation of the sound containing the cough into the sound of the cough and the rest of the sounds, even when extraneous sounds occurred during the cough. Identify the user by the sound of the cough to prevent analyzing the sounds of the cough that do not belong to the user (for example when another person coughs nearby). Assessment of cough characteristics (wet / dry, severity, duration, number of spasms, etc.). The method can work on wearable devices, smart devices, personal computers, laptops in 24 / 7 mode or in a selected range of time, and has a high energy efficiency.

We provide an "audio signal processing device" that achieves good localization for sounds with a narrow frequency band. [Solution] The system has a signal processing unit corresponding to each channel of a multi-channel audio signal. Each signal processing unit includes a target sound separation unit 411 that separates a target sound signal, which is the sound to be targeted for localization enhancement, from a source sound signal, which is the audio signal of the corresponding channel; a localization enhancement sound generation unit that generates a localization enhancement sound signal by multiplying the frequency of the target sound signal separated by the target sound separation unit 411 by k (where k is an integer of 2 or more); and a synthesis unit that synthesizes the localization enhancement sound signal generated by the localization enhancement sound generation unit and the source sound signal and outputs the result.

The present application relates to a kind of adaptive band optimization chorus sound separation method and system based on harmonic maintenance, first, the signal is collected by microphone array and time delay compensation and multi-channel time-frequency transformation are carried out;Sound source spatial positioning and initial band division are carried out using MUSIC algorithm;Using the greedy optimization strategy, the band boundary is dynamically adjusted and optimized by maximizing the objective function including signal-to-noise ratio and harmonic integrity measure;Then, the fundamental frequency is obtained by harmonic analysis and detection, and the time-varying harmonic trajectory is tracked across frames by Viterbi algorithm;On this basis, combined with MVDR beam forming and the introduction of harmonic maintenance factor, enhanced time-frequency representation is obtained by soft time-frequency mask;Finally, inverse short-time Fourier transform time-domain reconstruction and synchronous harmonic phase are carried out.The present application realizes the accurate allocation of voice part spectrum, effectively reduces the overlap interference, protects the overtone column of human voice while accurately separating, significantly improves the timbre fidelity and naturalness of separated voice part.

To provide an acoustic device which suppresses malfunction of an information terminal and can highly precisely recognize voice, an operation method of the device, an information processing system and an information processing method.SOLUTION: An acoustic device 10 includes: a sound detection unit 11 having a function for detecting sound 21; a sound separation unit 12 for separating the sound which the sound detection unit detects into voice and sound other than the voice; a sound determination unit 13 for registering a feature amount of the sound; a processing unit 15 for determining whether or not the feature amount of voice which the sound separation unit separates is the one registered by a machine learning model such as a neural network model, analyzing a command included in the voice if the feature amount of the voice is the one registered, generating a command signal indicating a content of the command and performing processing to cancel sound other than the voice to the sound other than the voice which the sound separation unit separates; a transmission / reception unit 16 for synthesizing the sound having been subjected to processing by the processing unit and sound which an information terminal 22 emits; and a sound output unit 17 for emitting the synthesized sound to the outside of the acoustic device.SELECTED DRAWING: Figure 1

The invention relates to the field of air conduction hearing aids, in particular to an air conduction hearing aid coincidence sound separation method, which comprises a hearing aid module, a coincidence sound processing module, a telescopic module, a sound insulation module and an auxiliary hearing aid module, and is characterized in that the hearing aid module comprises an external ear fixing assembly, a control assembly and a broadcaster assembly; the coincident sound processing module comprises a processing chip, a coincident sound separating module and a coincident sound playing module; the telescopic module comprises a storage assembly and a telescopic assembly, the sound transmitter assembly is controlled to extend into the ear canal, the storage assembly is opened, the inflation assembly is controlled to expand the inflation air bag to isolate external sound, the bone conduction assembly is driven to abut against the inner ear canal, and then coincident sound is separated through the processing chip; and then the positive sound part is played through the separated broadcaster assembly, and the bone conduction assembly plays the coincident sound part, so that the situation that the same player plays too disorderly is avoided, and then the situation that information in the coincident sound is missed under the normal use condition can be avoided.

The invention provides a sound directional frequency conversion method and system, hearing aid equipment, a medium and a hearing aid robot, and the method comprises the steps: carrying out the face recognition of the image information of a person, and obtaining a corresponding face recognition result; performing beam forming on the microphone array sound, and performing pronunciation object recognition and pronunciation object direction recognition according to a beam forming result, or performing scene recognition on external input sound; and performing sound separation based on the sound recognition result, matching a current frequency conversion scheme from a frequency conversion scheme database stored in the cloud according to the face recognition result and the sound separation result, performing directional frequency conversion according to the current frequency conversion scheme by using a sound frequency conversion algorithm stored in the local end, and outputting directional frequency conversion sound to the target person. Through directional frequency conversion of sound, high-quality auditory experience can be provided for hearing impairment patients, the hearing impairment patients can be better integrated into the society and life, and the system has wide application prospects in the fields of old-age care, medical treatment and the like.

The invention discloses a passive respiratory disease early warning device based on respiratory audio spectrum feature separation and a bidirectional long short-term memory network, which is characterized in that signal preprocessing is triggered when a posture is static by continuously collecting respiratory sound and synchronizing body position and motion state data; cardiopulmonary sounds are separated in real time by adopting an online non-negative matrix factorization technology, pure respiratory signals are extracted, and a dynamic signal environment is adapted through an incremental updating mechanism; key frequency band features are extracted in combination with wavelet transform time-frequency analysis, and breathing micro-variation features such as expiratory phase extension and wet rale are quantized by using a dynamic time bending algorithm; a two-way LSTM fused with an external attention mechanism is introduced to model breathing time sequence characteristics, key frequency domain information is focused, abnormal probabilities of pneumonia, asthma and chronic obstructive pulmonary disease are output, and when a preset early warning condition is met, a prompt is triggered; respiration feature extraction and desensitization are completed through local edge calculation, and only abnormal fragment ciphertexts are uploaded to guarantee data privacy. The method solves the problems that in the prior art, breathing sound separation precision is insufficient, single-mode analysis is limited, time sequence modeling adaptability is poor and the like, and zero-intervention and high-precision early passive monitoring of respiratory system diseases is achieved.

This invention discloses a multi-target sleep monitoring method and device based on snoring separation. The method includes: constructing independent respiratory wave time-stamped sequences corresponding to each monitored object based on the radio frequency signal corresponding to the target monitoring area; constructing a mixed snoring time-stamped sequence corresponding to all monitored objects based on the mixed environmental audio signal corresponding to the target monitoring area; performing cross-modal correlation matching between the mixed snoring time-stamped sequence and each independent respiratory wave time-stamped sequence to determine the independent snoring time-stamped sequence corresponding to each monitored object; and performing sleep monitoring analysis on the corresponding monitored objects based on the independent respiratory wave time-stamped sequence and / or the independent snoring time-stamped sequence of the same monitored object to obtain the sleep monitoring results corresponding to each monitored object. This achieves accurate sleep monitoring analysis of multiple monitored objects in the same space, resulting in precise sleep monitoring analysis results.

The invention discloses a task execution method and device and a storage medium, which can be applied to various scenes such as cloud technology, artificial intelligence, smart traffic and Internet of Vehicles, and the method comprises the following steps: responding to a target task execution request of a target account; obtaining a target collection image of the target account and a target sound in a collection process of the target collection image; performing image segmentation processing on the target acquisition image to obtain a target biological image and a target background image; performing a background sound separation operation on the target sound to obtain a target background sound; performing rationality judgment on the target background image and the target background sound to obtain a target judgment result; under the condition that the target judgment result represents that the target background image and the target background sound are reasonable, the target biological image is recognized, and a target recognition result is obtained; and under the condition that the target identification result is an identification passing result, executing the target task. According to the invention, the security of task execution is improved.

The invention relates to the field of hearing aids, in particular to an air conduction hearing aid far and near sound separation method, which comprises a hearing aid body module, a sound collection module, a control module, a broadcasting module, a vibration module and an isolation module, and is characterized in that the hearing aid body module comprises a body module and an ear hook module; the sound collection module comprises a stretching module and a sound receiver; the control module comprises a key module, a processing chip, a separation module and a transmission module; the hearing aid is worn, air is injected into the air bag, the air bag is expanded to isolate the upper broadcasting module and the lower broadcasting module and push the upper broadcasting module and the lower broadcasting module to abut against the ear canal, then the sound receiver extends out of the hearing aid body to enhance the receiving effect, and far and near sounds are separated and transmitted into the upper broadcasting module and the lower broadcasting module respectively. And the upper broadcasting module and the lower broadcasting module can synchronously start the upper vibration module and the lower vibration module during playing, so that a user can clearly hear the playing of far and near sounds and identify the far and near sounds.