26 results about "Abnormal voice" patented technology

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 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.

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.

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 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 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.

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 relates to the technical field of medical care, in particular to a psychological nursing decision-making system for senile chronic obstructive pulmonary disease patients based on emotion recognition. Comprising the following steps: acquiring daily voice signals and synchronous blood oxygen saturation of a patient, extracting a sound characteristic value and calculating a sound abnormal degree; the blood oxygen fluctuation stability degree at each sampling moment is evaluated in combination with the blood oxygen change and the sound abnormity degree, and the initial pathological interference degree is generated; correcting the initial pathological interference degrees by analyzing the correlation between the initial pathological interference degrees of different sound characteristic values to obtain a target pathological interference degree; based on the target pathological interference degrees of all sound characteristic values at the same sampling moment, analysis moments with small pathological interference are screened out, accurate emotion recognition and personalized psychological nursing decision making are performed by using voice signals at the analysis moments, the interference of chronic obstructive pulmonary disease physiological symptoms on emotion recognition is effectively overcome, and the accuracy of emotion recognition is improved. And the accuracy of judging the emotional state of the chronic obstructive pulmonary disease patient is improved.

The invention relates to a driving control method and system for a micro motor, and relates to the field of intelligent control, and the method comprises the steps: collecting a real-time sound signal and a motor model in the operation process of the micro motor; obtaining a reference sound signal based on the motor model; comparing the real-time sound signal with the reference sound signal, and judging whether the sound of the micro motor is abnormal or not; if it is judged that the sound is abnormal, abnormal voiceprint features and sound collection orientation information are obtained based on the real-time sound signals; positioning a specific abnormal position by combining the abnormal voiceprint feature and the sound acquisition azimuth information; and matching an exception handling method according to the specific exception position and the exception voiceprint feature, controlling a preset execution mechanism to execute corresponding handling operation according to the exception handling method, and reporting a sound exception handling prompt at the same time. The method has the effect of improving the timeliness of fault early warning.

The invention relates to a sound anomaly detection method based on a prototype network, and relates to the technical field of sound anomaly detection. The method comprises the following steps: constructing a feature extractor, and obtaining the feature extractor which can capture difference features of two types of audios and is fixed in structure through supervised training based on normal and abnormal audios; a prototype system is constructed, a prototype set and a prototype network classifier are constructed based on the feature extractor, and normal and abnormal audio prototypes are obtained through calculation; performing anomaly detection judgment, and completing normal and abnormal classification of the to-be-predicted audio through feature extraction and similarity comparison; according to the method, optimization and adaptation are achieved, the prototype set is updated and iterated based on the evaluation set, representative samples of newly-added audio types can be directly added into the prototype set, and adaptation can be achieved without retraining the model. According to the invention, through a prototype network architecture, the detection precision can still be ensured under the condition of a small number of newly added abnormal samples, and the method has relatively high practicability and robustness.

This invention relates to the field of pediatric therapeutic instrument technology, specifically to a method and system for detecting abnormal speech in a speech therapy instrument. The method involves acquiring voltage signals and filtering discrete speech frame sequences, extracting formant trajectories to analyze frequency change trends, comparing the articulation direction distribution of children with standard pronunciation, identifying directional deviation segments, and using formant neighborhood envelope peak comparison to pinpoint envelope offset segments. It also compares spectral peak and valley distribution characteristics, performs multidimensional temporal overlap comparison, and finally outputs the abnormal speech detection results. This invention utilizes amplitude cohesion to filter stable speech frame sequences, extracts structured formant trajectories, correlates syllable segment change directions to enhance dynamic trend discrimination, analyzes neighborhood envelope peak offsets to refine spectral characterization, cross-validates peak and valley distribution and temporal overlap segments, strengthens multidimensional consistency, achieves hierarchical identification and precise localization of pronunciation abnormalities, and improves the stability and distinguishability of detection results.

According to the present invention, a sound anomaly detection method includes acquiring, by an audio unit, a noise from an inspection target; generating, by a data processing unit, an input value that is a feature vector matrix including a plurality of feature vectors from the noise; generating, by a detection unit, a restored value imitating the input value through a detection neural network that is a deep neural network learned for the input value; determining, by the detection unit, whether a restoration error indicating a difference between the input value and the restored value is greater than or equal to a calculated reference value; and determining, by the detection unit, that there is an anomaly in the inspection target when determining that the restoration error is greater than or equal to the reference value.

An output sound abnormality detection apparatus includes an output sound generation circuit configured to generate a first sound and a second sound, the first sound being an output target and the second sound having same content as content of the first sound; an amplifier configured to amplify and output the first sound and the second sound; a comparator configured to compare signal levels of the first sound and the second sound output from the amplifier; and an abnormality determination circuit configured to determine whether there is an abnormality in one or both of the first sound generated by the output sound generation circuit and the amplifier based on a comparison result obtained by the comparator.