38 results about "Abnormal voice" patented technology

The invention relates to the technical field of sound anomaly detection, in particular to a sound anomaly detection method and device based on a Transform model, equipment and a medium, and the method comprises the steps: collecting a sound signal during the operation of the equipment through a data collection interface, and obtaining an original sound signal; resampling is carried out on the collected sound signals, and normalization processing is carried out on the resampled data; mel-frequency cepstrum coefficient features are extracted from the sound signals after normalization processing, and the sound signals after normalization processing are input into a pre-training module to output high-dimensional features including time sequence and semantic information; splicing the Mel-frequency cepstrum coefficient features with the high-dimensional features to form a comprehensive feature vector; inputting the comprehensive feature vector into a support vector machine model, and performing abnormal sound recognition through a trained classification hyperplane; and detected abnormal information is fed back to the user in real time. Multi-feature fusion enables the model to identify abnormal sound more accurately.

The invention discloses a transformer fault detection method and device based on an automatic encoder and multi-scale feature fusion, and belongs to the technical field of power transformer fault detection.The method comprises the steps that sound signals during operation of a transformer are collected, and data frame division and normalization preprocessing are conducted on the collected sound signals; reconstructing the data of the collected sound signals by using an automatic encoder trained by normal sound data, and extracting the data coded by the automatic encoder as the implicit features of the sound signals; according to the error before and after reconstruction, sound abnormity judgment is carried out, and fault type detection is further carried out on abnormal data by using a classifier; and the classifier receives the collected sound data and the characteristic signal extracted by the automatic encoder as input, and classifies fault types. According to the method, the classifier is arranged, fault features are mined through the multi-scale features of the transformer sound signals, and the accuracy of transformer fault classification is improved.

The invention provides an unsupervised sound anomaly recognition method and device based on a mask strategy, and the method comprises the steps: obtaining normal audio data of machine equipment, and carrying out the preprocessing of the normal audio data, so as to obtain a preprocessed logarithmic Mel spectrogram; an anomaly recognition model of the deep convolutional generative adversarial network is constructed, and the anomaly recognition model comprises a generator, a global discriminator and a random local discriminator; the preprocessed logarithm Mel spectrogram is masked, an anomaly recognition model is trained according to the masked logarithm Mel spectrogram and the preprocessed logarithm Mel spectrogram so as to obtain a trained anomaly recognition model, and in the training process, the logarithm Mel spectrogram and the preprocessed logarithm Mel spectrogram are matched with each other; optimizing the anomaly recognition model by adopting mask loss, reconstruction loss, local feature constraint loss and global feature constraint loss; adopting a trained anomaly recognition model to obtain an anomaly score so as to determine whether the corresponding to-be-recognized sample is abnormal or not; therefore, the sound anomaly detection effect is improved.

The invention provides a power plant equipment sound abnormity identification and health prediction method based on granular computing and an LSTM network, and the method comprises the steps: employing an array pickup and a high-dynamic-range microphone array in a complex and high-noise background environment of a power plant, and combining an anti-interference filtering and beam forming algorithm, thereby achieving the sound abnormity identification and health prediction of the power plant equipment. According to the method, directional, multi-channel and non-contact sound acquisition is carried out on key parts of equipment, acquired sound signals are preprocessed, converted into time domain, frequency domain and time-frequency domain representations and coded into multi-dimensional numerical vectors, and compared with a rule-based expert system, the method has higher generalization ability and real-time response ability; a large language model is introduced, so that the output is closer to an engineering language and is suitable for operation and maintenance personnel to understand and execute; a self-defined knowledge base or safety semantic filtering is supported, and closed-loop deployment in an industrial field is facilitated; the system can be in butt joint with an intelligent inspection system, and full-link linkage of voice recognition, health assessment and suggestion generation is achieved.

The invention discloses a calibration data set acquisition method, a spoken language test scoring method and a related device. The method comprises the following steps: acquiring at least one abnormal voice data; wherein the abnormal voice data is first answering voice data with an abnormal sound event, and the first answering voice data is obtained by performing voice answering on the oral test by an answerer; obtaining a target calibration data set by using the at least one abnormal voice data and the initial calibration data set; wherein the target calibration data set is used for training the oral test scoring system. In this way, the coverage range of the calibration data set can be wider.

The invention relates to the technical field of sound detection, in particular to a sound anomaly detection smart bracelet and a detection method, and a sound anomaly detection method. The smart bracelet for sound anomaly detection is matched with an analysis host and acoustic detection software, and an acoustic detection algorithm based on spectral analysis and machine learning is used for detecting the sound anomaly. And automatic detection and alarm of abnormal sound are realized. According to the invention, on-line automatic detection of wiring harness plugging is realized, the outdated condition of manual detection of an existing production line is replaced, the detection efficiency and accuracy are higher than those of manual detection, the SOP automation level is improved, and information archiving and backtracking are realized.

The present invention discloses a Whisper audio transcription method, system, and storage medium based on hot word excitation. The present invention proposes a hot word framework, adds a bias encoder / decoder module to the Whisper base framework, and modifies a part of the calculation logic. Among them, the bias encoder is used to extract hot word text features, and the bias decoder combines the hot word feature excitation to transcribe the text, so that the keywords that may have been transcribed incorrectly converge in the direction of the hot word feature. Through hot word excitation, Whisper corrects the erroneous text and obtains the correct text content. Similarly, when an abnormal voice transcription occurs on site, the transcription error correction can be quickly achieved by retaining / inputting hot words, thereby optimizing the fluency of human-computer interaction.

The application discloses an abnormal voice data detection method and device, computer equipment and a storage medium, and applies to the technical field of voice detection. The method comprises the following steps: obtaining historical voice data, obtaining a first corpus set according to the historical voice data, obtaining a second corpus set, and obtaining a representative word set according to the second corpus set and the first corpus set. When the current voice segment of the i-th service scene is detected, whether at least one abnormal word is contained in the current voice segment is determined according to the representative word set corresponding to the i-th service scene before the current voice segment is sent. When at least one abnormal word is contained in the current voice segment, a preset abnormal processing rule is obtained, and the current voice segment is processed based on the preset abnormal processing rule, so that the detection efficiency of abnormal voice data is improved.

The invention provides a self-supervised sound anomaly recognition method and device based on multivariate feature enhancement, and the method comprises the steps: carrying out the preprocessing of original data, carrying out the coding of the attribute information of machine equipment through employing a Mixup technology, generating a classification tag, constructing an auxiliary classification task, and carrying out the recognition of the classification tag; the method comprises the following steps: respectively extracting a logarithmic Mel spectrogram, a speech spectrogram and a spectrogram of audio data as the input of a multivariate feature extraction network, aggregating the output of the multivariate feature extraction network to serve as the feature of a single audio sample, and enhancing the discrimination and robustness of the feature by using a multivariate feature enhancement method based on cross fusion, so as to improve the robustness of the audio sample. Finally, the features of the feature space are clustered, and a clustering center is generated; in the test stage, to-be-tested data is input into the feature extraction network to extract features, and an exception score is calculated by comparing cosine distances between the features, so that exception is judged; therefore, the sound anomaly detection effect is improved.

To provide an output sound abnormality detecting device capable of detecting an abnormality in an output system including a content of an output sound.SOLUTION: An on-vehicle device 100 comprises: a warning sound generation unit 24 that generates an L channel warning sound that is an output target and an R channel sound having the same content as the warning sound; an amplifier 30 that amplifies these sounds and outputs an amplified sound; a comparator 40 that compares signal levels of the two sounds output from the amplifier 30; and a warning sound abnormality determining unit 28 that determines whether or not there is an abnormality in the warning sound and the amplifier 30 based on a comparison result by the comparator 40.SELECTED DRAWING: Figure 1

The invention discloses a voice interaction method and device, equipment and a storage medium, and relates to the technical field of voice processing, and the method comprises the steps: determining an optimal voice collection mode of a voice signal under the condition that the voice signal of a user is detected; acquiring a real-time voice signal of the user through the optimal voice acquisition mode; detecting whether abnormal voice signals exist in the real-time voice signals or not, wherein the abnormal voice signals are voice signals with pronunciation problems; if yes, repairing the real-time voice signal; and generating and executing a voice control instruction according to the restored voice signal. According to the invention, when the real-time voice signal of the user has the pronunciation problem, the real-time voice signal can be repaired, and the voice control instruction is generated and executed according to the repaired voice signal, so that the problem that the existing far-field voice recognition system cannot accurately recognize the voice instruction with the voice problem is solved, and the voice recognition efficiency is improved. And the voice interaction is limited.

This invention relates to a drive control method and system for a micro motor, belonging to the field of intelligent control. The method includes: acquiring real-time sound signals and the motor model during micro motor operation; obtaining a reference sound signal based on the motor model; comparing the real-time sound signal with the reference sound signal to determine if there is a sound anomaly in the micro motor; if a sound anomaly is determined, obtaining abnormal acoustic signature features and sound acquisition location information based on the real-time sound signal; combining the abnormal acoustic signature features and sound acquisition location information to locate the specific anomaly location; matching the anomaly handling method according to the specific anomaly location and abnormal acoustic signature features, and controlling a preset actuator to perform the corresponding processing operation according to the anomaly handling method, while simultaneously reporting a sound anomaly handling prompt. This application has the effect of improving the timeliness of fault early warning.

The present application provides a kind of traditional Chinese medicine preparation for treating Tourette syndrome of children with laryngeal abnormal voice as main symptom, the traditional Chinese medicine preparation includes the following weight parts of raw medicinal materials: 8-15 parts of radix isatidis, 3-6 parts of radix sophorae tonkinensis, 10-15 parts of radix puerariae, 6-10 parts of jinyu longan, 6-10 parts of momordica grosvenori, 6-8 parts of platycodon grandiflorum, 3-6 parts of licorice, 5-8 parts of periostracum cicada, 10 parts of radix angelicae, and addition and subtraction according to different indications, the traditional Chinese medicine preparation of the present application can clear lung and stomach heat, benefit throat, block the way of external wind driving internal wind, can rapidly and effectively treat pediatric vocal tic, improve laryngeal voice symptoms, also can treat exogenous heat syndrome sore throat, and the traditional Chinese medicine preparation of the present application is safe, no obvious toxic side effects, suitable for children to take when laryngeal abnormal voice.

To provide a device that processes acoustic data detected in a facility to identify an apparatus in which an abnormality occurs.SOLUTION: The device includes: an acoustic data acquisition section that acquires acoustic data detected by a robot capable of moving in a facility and collecting sound; a first sound abnormality detection section that detects an abnormal sound in a first direction centering around the robot using the acoustic data; a second sound abnormality detection section that detects an abnormal sound in a second direction centering around the robot using the acoustic data; and an abnormal apparatus identification section that identifies an apparatus in which an abnormality occurs using a position of the abnormal sound in the first direction and a position of the abnormal sound in the second direction.SELECTED DRAWING: Figure 10

The application relates to the field of artificial intelligence and discloses a sound anomaly detection method and device based on self-supervised learning, an electronic device and a storage medium. The method comprises the following steps: performing speech enhancement on a positive sample sound data set to obtain an enhanced sample sound data set; training a preset feature extraction model by using the positive sample sound data set and the enhanced sample sound data set to obtain a trained feature extraction model; extracting features of the positive sample sound data set and to-be-detected sound data by using the trained feature extraction model to obtain a standard sound wave feature data set and to-be-detected sound wave feature data; calculating the mean and covariance of the sound wave feature data set to obtain a standard sound wave mean and a standard sound wave covariance; and calculating the distance between the to-be-detected sound wave feature data and the sound wave feature data set according to the standard sound wave mean and the standard sound wave covariance, and determining that the to-be-detected sound data is abnormal data when the distance is greater than a preset threshold. The application can improve the accuracy of sound anomaly detection.

The invention discloses a speech recognition method, device and system. Receiving a voice slice sent by the CCU through the websocket connection, and caching the voice slice; in response to the arrival time of a preset duration period, splicing all the cached voice slices into a complete long voice according to a caching sequence; sending the long voice to an online voice recognition server, enabling the online voice recognition server to perform voice recognition on the long voice to obtain a transcription text, and returning the transcription text; receiving a transliteration text returned by the online voice recognition server, sending the transliteration text to a large language model server, enabling the large language model server to judge the transliteration text through a preset large language model, obtaining a corresponding judgment result, and returning the judgment result; and in response to determining that the long voice is abnormal voice according to the judgment result, stopping voice slice splicing, and returning the judgment result to the CCU, so that the CCU performs processing according to a preset abnormality processing strategy. According to the method, voice recognition can be accurately realized in real time.

The invention discloses an automatic audio device self-checking method based on embedded telephone equipment, and relates to the field of audio signal processing and hardware diagnosis. The method comprises the steps that firstly, a microphone collects first 3S playback signals of a test audio, and frequency similarity detection is carried out by extracting spectrum features; then, continuously collecting the 4s-5s playback signal of the audio, and carrying out single frequency point detection; meanwhile, aiming at the front 3S playback signals, calculating a root-mean-square RMS value according to signal energy, converting the RMS value into loudness, and carrying out loudness anomaly detection; thirdly, continuously collecting the 6s-7s playback signal of the audio for sampling analysis, calculating the root-mean-square of the playback signal, converting the root-mean-square into loudness L2, and when the loudness value L2 is lower than a set threshold value, indicating that the MIC device is abnormal; and finally, through frequency similarity detection, single-frequency detection, loudness anomaly detection and device anomaly detection, results of four rounds of detection are reported in a binary form, and equipment sound anomaly reasons are analyzed. According to the invention, specific abnormal links in the audio link can be effectively positioned.

The invention relates to the technical field of audio detection, in particular to a sound abnormity monitoring processing method and system in the operation process of industrial equipment, and the method comprises the steps: carrying out the data packet segmentation of equipment audio data generated in the operation process, and configuring and outputting a state conversion diagram associated with the equipment audio data; based on the state transition diagram, performing data packet signal recombination between energy sound channel audio data packets in the equipment audio data, and outputting a plurality of reference audio data associated with the equipment audio data; for each reference audio data, based on the fundamental tone frequency data, calculating the anomaly detection confidence of each energy sound channel audio data packet; and performing anomaly detection on the industrial equipment operation audio library based on the anomaly detection confidence of each energy sound channel audio data packet, and outputting an anomaly detection result associated with the equipment audio data. According to the invention, richness and diversity of data packet expression of the equipment audio data caused by different energy positions in the equipment audio data are fully considered, and the accuracy of anomaly detection is improved.

The invention discloses a voice information processing method and device, a storage medium and electronic equipment, and relates to the field of artificial intelligence. The method comprises the following steps: converting target voice information of a user into target text information; inputting the target text information into a target model, determining a feature vector of the target text information according to priori knowledge learned by the target model in a model training stage, and generating a probability vector for the target voice information based on the feature vector; a target probability value and a target dimension corresponding to the target probability value in the probability vector are determined, and the target probability value is larger than a first preset threshold value; and determining whether the target voice information is abnormal voice according to the target probability value, the target dimension and a second preset threshold. According to the method and the device, the technical problem that the false alarm rate is high when illegal committed sales records are recognized through keyword matching in the sales process of financial products is solved.

The invention relates to the technical field of sound analysis, in particular to a throat lesion monitoring system based on sound recognition, which comprises a sound quality optimization module, a sound feature analysis module, an abnormality evaluation module and a treatment effect evaluation module. According to the invention, by utilizing the historical sound data of the user and comparing and evaluating the deviation of the current sound features, the monitoring accuracy is improved, the detection of early lesions is more reliable, the deviation detection threshold is adjusted according to the age and gender differences of individuals, the sound anomaly detection is more accurate, the sound anomaly of the user can be found in time, and the user experience is improved. The system has the advantages that the system is simple in structure and convenient to use, false alarm and missing alarm are reduced, real-time and visual treatment feedback is provided by collecting sound data before and after treatment, analyzing the sound change degree, combining the treatment time and evaluating the treatment effect, data support is provided for doctors, measure adjustment in the treatment process is optimized, and the disease monitoring and management fineness and effect are improved.

The invention belongs to the technical field of artificial intelligence and intelligent security and protection, and particularly relates to a campus hidden area abnormal voice sensing and classification recognition system. Comprising a distributed acoustic sensing array, an environment context sensing module, an adaptive signal conditioning module, a sound source credibility evaluation module, a context enhanced abnormal voice classification module and a privacy protection type data processing module. Through a multi-mode environment perception and dynamic feedback coupling mechanism, sound source space verification, adaptive gain control and multi-level classification decision are realized, and through an adaptive pickup mechanism driven by an environment context, excessive acquisition of environment noise in an unmanned state is avoided; therefore, false triggering caused by non-human-body sound sources can be eliminated by utilizing consistency verification of sound source spatial orientation and human-body existence information.

The invention discloses a bipartite graph clustering-based live pig sound anomaly detection method and device, and the method comprises the steps: carrying out the preprocessing of collected audio data, and obtaining a pig sound standardized audio sample; extracting acoustic characteristic parameters of the audio samples, and constructing a sample characteristic matrix; dividing the sample feature space into a plurality of balanced sub-clusters by adopting a balanced hierarchical K-means algorithm, and selecting a centroid as an anchor point; the sample-anchor point similarity is calculated, and a bipartite graph is constructed; inputting the bipartite graph into a preset clustering model BGCB, optimizing a sample and an anchor label matrix by adopting an alternating coordinate descent algorithm, generating a clustering result and calculating a clustering center; and identifying abnormal sounds of pigs such as cough, squeal and moan according to the clustering center difference and the spectrum energy distribution characteristics, and outputting an alarm. The method can achieve the automatic detection of the abnormal sound of the live pigs without manual marking, has the advantages of balanced clustering result, low calculation complexity and high robustness, and is suitable for the health monitoring and disease early warning of intelligent farms.

The invention discloses a data processing method and device, electronic equipment and a storage medium. Comprising the following steps: determining multi-frame voice data corresponding to a to-be-processed original voice signal, and performing feature extraction processing on the multi-frame voice data to obtain frequency domain voice features and time domain voice features; based on the frequency domain voice features and the time domain voice features, abnormity recognition processing is carried out through a preset abnormity recognition model, an abnormity recognition result is obtained, and the abnormity recognition result comprises at least one classification label and abnormity probability data corresponding to the classification label; and performing abnormality level judgment based on the abnormality recognition result to obtain an abnormality level judgment result, determining an abnormality handling measure based on the abnormality level judgment result, and processing the original voice signal through the abnormality handling measure. According to the scheme, the time domain and frequency domain features are directly extracted from the original voice signal, collaborative analysis is carried out, effective recognition of the abnormal voice is achieved, the comprehensiveness and accuracy of abnormal recognition are improved, and the user experience is improved.

The application relates to the technical field of audio analysis, and discloses a device sound anomaly detection method and device and electronic equipment, the method comprising the following steps: mixing acquired device running sound and environmental noise according to different signal-to-noise ratios to construct an enhanced data set; training a self-encoder model based on the enhanced data set to obtain an audio detection model; acquiring the spectral features of to-be-detected audio, inputting the spectral features into the audio detection model, and calculating a reconstruction error; based on the reconstruction error and the matching result of the spectral features and a preset audio feature library, identifying whether the to-be-detected audio is abnormal audio, and classifying the abnormal audio; the application mixes device running sound and environmental noise according to different signal-to-noise ratios to construct an enhanced data set, thereby improving the generalization capability of the model in a complex noise environment; a two-stage diagnosis strategy combining the self-encoder reconstruction error and the matching of the preset audio feature library can effectively identify known abnormal types and detect unknown abnormalities.

The present application relates to the field of health care technology, in particular to a kind of psychological nursing decision system for old chronic obstructive pulmonary disease patient based on emotion recognition.The system includes obtaining patient daily voice signal and synchronous blood oxygen saturation, extracting sound characteristic value and calculating its sound abnormality degree;combining blood oxygen change and sound abnormality degree, assessing the blood oxygen fluctuation stability degree of each sampling time, generating initial pathological interference degree;by analyzing the correlation between different sound characteristic value initial pathological interference degree, correcting initial pathological interference degree, obtaining target pathological interference degree;based on the target pathological interference degree of all sound characteristic values at the same sampling time, screening out the analysis time with small pathological interference, using the voice signal of analysis time for accurate emotion recognition and individualized psychological nursing decision, effectively overcome the interference of chronic obstructive pulmonary disease physiological symptoms on emotion recognition, improve the accuracy of emotion state judgment for chronic obstructive pulmonary disease patients.

This invention belongs to the field of communication security and artificial intelligence quality inspection technology, and relates to a real-time quality inspection method and system for abnormal voice calls using a multimodal AI large-scale model for telecom operators. The method receives call information, industry information, and compliance script information; aligns and verifies detailed call records with audio recordings; and performs speech recognition and feature extraction. It combines text semantic features, speech acoustic features, call behavior features, voiceprint biometric features, and deviation features of the actual call content from the reported information to construct multimodal evidence units and evidence sequences. It uses an AI large-scale model to identify candidate risk segments, and uses recording integrity identifiers and reported deviation features as pre-gating to determine the risk level of candidate risk segments and perform review and triage, outputting a real-time quality inspection evidence package. This invention can improve the evidence completeness, risk identification accuracy, and traceability of handling in the quality inspection of abnormal voice calls for telecom operators.

The invention relates to the technical field of voice interaction investigation data processing, and discloses an investigation data consistency auditing system and method based on customer interaction service, at least one piece of auditing item data is obtained, and the auditing item data comprises basic data, visitors and a plurality of voice samples interacted with corresponding investigated objects; analyzing each voice sample to form corresponding interaction data; consistency auditing is carried out on the process of analyzing the voice sample to form interaction data, wherein the consistency auditing comprises basic record consistency auditing and voice emotion consistency auditing; creating a task library, matching and / or updating auditing tasks in the task library according to the basic data of the auditing items, obtaining a plurality of target auditing tasks, and performing basic record consistency auditing; constructing an emotion identification model, respectively identifying emotions of the visitor and the investigated object in the voice sample, and performing voice emotion consistency auditing; performing exception marking and secondary re-checking on the voice segments with exceptions in the voice sample consistency checking; and the voice interaction investigation and examination efficiency and accuracy are improved.

A diagnostic method of electrical equipment which includes a processing unit, an internal microphone, and electrical components other than microphones or speakers, the diagnostic method including the steps of acquiring a received audio signal produced from an ambient sound signal, by the at least one internal microphone or by the at least one external microphone, producing monitoring parameters from the received audio signal, which are representative of an interfering sound signal comprised in the ambient sound signal and emitted by at least one of the electrical components, and detecting a sound anomaly resulting from a failure of at least one electrical component of the electrical equipment from the monitoring parameters.

The present invention relates to the field of audio detection technology, and in particular to a method and system for monitoring and processing sound anomalies during the operation of industrial equipment. The method divides the equipment audio data generated during the operation into data packets, and configures and outputs a state transition diagram associated with the equipment audio data; based on the state transition diagram, the data packet signals between the energy channel audio data packets in the equipment audio data are reorganized, and multiple reference audio data associated with the equipment audio data are output; for each reference audio data, the anomaly detection confidence of each energy channel audio data packet is calculated based on the fundamental frequency data; based on the anomaly detection confidence of each energy channel audio data packet, anomaly detection is performed on the industrial equipment operation audio library, and an anomaly detection result associated with the equipment audio data is output. The present invention fully considers the richness and diversity of the device audio data packet representation caused by the different energy positions in the device audio data, and improves the accuracy of anomaly detection.

The invention discloses a method for monitoring and adjusting the slagging process of an LF furnace in real time through sound. The method is characterized by comprising the following steps: (1) installing a sound sensor and a sound acquisition system on site; (2) an LF slagging audio recognition and recording system; (3) the original sound file is stored in the form of an audio file, and a voiceprint library is built; (4) sound in the LF slagging process is monitored in real time, and an LF slagging sound abnormity alarm model is developed through audio features and a machine learning mode; (5) recording process data in the production process, and providing data support for further production process model optimization in combination with the abnormal sound data to form an audio identification recording system; and (6) externally connecting external data of the system, developing an LF slagging audible alarm model through audio characteristics and a machine learning mode, and combining the collected process data to realize LF slagging audible alarm. According to the method, the LF furnace slagging sound state is subjected to numerical regression processing through the audio regression model, the slagging sound state curve is formed, the LF furnace slagging sound is recognized and classified through the adopted audio recognition model, when abnormal sound appears, an alarm is given, the alarm can be output, and timely subsequent operation processing is facilitated.