753 results about "Sound detection" patented technology

The invention provides a live pig health monitoring method based on multi-source information fusion, and the method comprises the steps: collecting the sound data of a live pig in a natural environment, carrying out the classification and preprocessing of the sound data, forming a sound sample data set, inputting an LSTM neural network model, and carrying out the training, and obtaining a sound detection model; acquiring a live pig attitude image to obtain an attitude sample data set, and inputting the live pig attitude sample data set into the YOLOv5s model for training to obtain an attitude detection model; inputting live pig sound data collected in real time, preprocessing the data, and inputting the data into the sound detection model to obtain sound features; inputting live pig attitude images collected in real time into the attitude detection model to obtain attitude features; measuring the body temperature of the live pig by using an infrared temperature sensor, measuring the weight of the live pig by using a weight sensor, and fusing the sound feature, the posture feature, the body temperature and the weight to obtain a fusion vector; and inputting the fusion vector into a random forest classifier for decision making, and determining the health state of the live pig. According to the method, multi-modal data are fused, comprehensive evaluation and diagnosis of the health condition of the live pigs are facilitated, and the accuracy and robustness of the whole monitoring system are further improved.

The invention provides a domain generalization-based unsupervised abnormal sound detection method and system for mechanical equipment, and the method comprises the steps: constructing an SE-ECA fusion attention mechanism module based on a global context, constructing an MFCC branch network architecture and a Log-Mel branch network architecture, adding the modules to each branch network architecture, and carrying out the detection of the abnormal sound of the mechanical equipment. Audio feature extraction is carried out on the MFCC spectrogram features and the Log-Mel spectrogram features respectively; embedding the audio features extracted by the two branch network architectures into a channel dimension for merging to form a comprehensive feature vector, and classifying the comprehensive feature vector; an anomaly detection model is constructed and trained, the anomaly detection model uses a Gaussian mixture model to calculate a global anomaly score, then uses a local anomaly factor to detect local anomaly, and then performs weighted averaging, so that normal sound features can be automatically learned, anomaly can be detected, and the anomaly detection performance of equipment is improved.

The invention provides a casting industry abnormal sound detection and grading response method based on voiceprint recognition, and belongs to the technical field of casting industry detection. A high-temperature-resistant microphone array is arranged at an easy-to-leak part of cast aluminum equipment, and three-stage filtering noise reduction and amplitude normalization preprocessing are adopted, so that the problem of poor signal quality caused by noise interference in a complex environment is effectively solved; the characteristics of the molten aluminum leakage sound in different frequency bands and different stages can be captured through variable window long-short time Fourier transform and extended Mel frequency cepstrum coefficient in combination with extraction of an energy change rate and a frequency spectrum gravity center; a Transform-CNN hybrid deep learning model based on an attention mechanism is constructed, and feature screening is optimized through principal component analysis and recursive feature elimination, so that the recognition and generalization ability of the model to the abnormal sound in the casting industry is significantly improved; and meanwhile, graded response measures are made based on the detection result, so that the abnormal conditions of molten aluminum leakage with different severity degrees are processed.

The embodiment of the invention relates to the technical field of power line system detection, and discloses a power line detection method based on a sound signal, which comprises the following steps: acquiring sound detection information in a current detection scene through a sound acquisition array; identifying and processing the processed sound detection information through a partial discharge pulse identification algorithm to determine partial discharge pulses in the sound detection information, and determining amplitude parameters of each phase interval according to pulse peak characteristics of the partial discharge pulses, generating a corresponding phase-amplitude distribution diagram according to the phase interval and the amplitude parameter; inputting the phase-amplitude distribution diagram into a pre-constructed partial discharge recognition model for recognition to obtain a corresponding partial discharge type recognition result; and outputting a corresponding partial discharge type identification result and a phase-amplitude distribution diagram. The accuracy and efficiency of power line system safety detection can be improved.

The invention belongs to the technical field of optical fiber sensing, and discloses a high-temperature-resistant and high-pressure-resistant multi-parameter integrated sensor and system based on optical fiber dislocation welding, and the sensor comprises a packaging main body, and the center of the packaging main body is provided with a center hole and a cavity which are used for accommodating a transmission optical fiber. The tail end of the transmission optical fiber is sequentially connected with a temperature detection device and a pressure / sound detection device which are positioned in the cavity; the temperature detection device comprises a fiber bragg grating; the pressure / sound detection device comprises a single-mode fiber, a multimode fiber, a first hollow capillary fiber and a second hollow capillary fiber which are connected in sequence; the axes of the first hollow capillary optical fiber and the second hollow capillary optical fiber are connected in a staggered manner to form an open structure. The pressure / sound-temperature multi-parameter composite in-situ synchronous measurement device realizes composite in-situ synchronous measurement of multiple parameters of pressure / sound-temperature, has the advantages of high temperature resistance, small size and high test precision, and has wide application prospects in the fields of aerospace, petroleum metallurgy, environmental monitoring and the like.

The invention provides a belt conveyor carrier roller fault sound detection method and system, and relates to the field of nondestructive fault detection, and the method comprises the steps: obtaining a time-frequency domain characteristic pattern X of a sound signal; performing feature extraction on the X through a plurality of convolutional layers with different sizes to obtain a multi-scale feature map # imgabs0 #; performing deeper-level fault feature mapping of the spatial dimension and the channel dimension on the # imgabs1 # through a first Ghost module, a deep convolutional layer, a spatial channel collaborative attention module and a second Ghost module in sequence to obtain a high-level semantic feature map # imgabs2 #; inputting # imgabs3 # into a classifier module to obtain a classification result, and completing model training by using a cross entropy loss function and a re-parameterization training method to obtain a fault detection model; and performing short-time Fourier transform on a sound signal to be detected, and inputting the sound signal to the trained fault detection model to obtain a fault diagnosis result. According to the invention, the accuracy and real-time performance of belt conveyor carrier roller fault detection are improved.

The invention relates to the technical field of sound monitoring, in particular to a wild animal sound detection system, method and device based on voiceprint recognition, and the system comprises a multi-mode sound collection module, a voiceprint feature extraction engine, an acoustic fingerprint database, a species recognition decision module and an ecological behavior analysis unit, the multi-mode sound acquisition module integrates an array microphone and an infrasonic wave sensor, and supports full-band environmental acoustic signal acquisition; the voiceprint feature extraction engine extracts animal voiceprint biological features by adopting a self-adaptive frequency band segmentation algorithm; the acoustic fingerprint database stores a multi-species acoustic feature template and an environment interference noise sample; the species identification decision module constructs a voiceprint classification model based on a deep residual network; and the ecological behavior analysis unit generates a wild animal activity mode report through the acoustic event space-time distribution map. Therefore, the problems of serious environmental noise interference, low individual feature discrimination, difficulty and insufficiency in small sample species identification and the like in the prior art are solved.

A detection device for detecting a quality of ultra-long pile foundation of deep foundation pit support structure is provided, and includes a plurality of sound detection pipes which are parallel to each other and are vertically arranged on a reinforcement cage of a pile foundation. The plurality of sound detection pipes are paired, two sound detection pipes in each pair of the plurality of sound detection pipes are arranged opposite to each other, and ends of the plurality of sound detection pipes are connected to each other through connecting brackets; and a sound wave transmitter and a sound wave transducer are respectively arranged in the two sound detection pipes and are connected with the sound wave detector arranged outside the pile foundation through cables.

The invention discloses a fan blade diagnosis method and device based on the voiceprint perception technology and a product, and the method comprises the steps: firstly obtaining a sound detection signal of a target fan blade, and then carrying out the feature extraction of the sound detection signal, and obtaining a corresponding feature vector; then, based on the feature vector, extracting an effective blade sound signal from the sound detection signal; after the extraction of the effective sound signals is completed, voiceprint feature extraction processing can be carried out on the effective blade sound signals to obtain corresponding voiceprint features; thirdly, constructing a blade damage detection model which takes voiceprint features as input and takes damage detection results as output; on the basis, the voiceprint features of the effective blade sound signals can be directly input into the model, so that the damage detection result of the target fan blade can be quickly obtained; therefore, blade detection is carried out based on the voiceprint technology, the cost is low, and the method can adapt to the complex field environment of the fan.

Provided in the present invention are an abnormal-sound detection method and system based on diffusion denoising for an air conditioner outdoor unit. The method comprises: acquiring normal operation sound data of an air conditioner outdoor unit and interference sound data in an environment where air conditioner detection is performed; pre-processing the sound data to obtain respective time-frequency features; training a Gaussian mixture model on the basis of the time-frequency feature of the interference sound data, obtaining the data distribution thereof to perform random sampling, and superimposing, onto the time-frequency feature of normal sound data of an air conditioner, sound data sampled from the data distribution, so as to obtain fifth sound data and a corresponding sampling step label; training a neural network model by means of the normal operation sound data of the air conditioner outdoor unit and the fifth sound data, so as to obtain a trained anti-interference abnormal-sound detection model for an air conditioner outdoor unit; and acquiring operation sound data to be subjected to detection of the air conditioner, pre-processing same, and then inputting same into the trained abnormal-sound detection model for an air conditioner, so as to determine whether an operation sound of the air conditioner is abnormal. The present application improves the anti-interference performance of a detection algorithm and the air conditioner outdoor unit detection efficiency, thereby reducing the labor cost.

The invention relates to an intelligent AI information processing method and system and an intelligent terminal, and relates to the technical field of information processing, and the method comprises the steps: obtaining sound detection information in a preset detection range and triggering information of a user; inputting the sound detection information into a preset noise model to match noise detection information; updating the sound detection information through a preset sound processing method according to the noise detection information; inputting the updated sound detection information into a preset audio processing system, and determining an output text according to the updated sound detection information when the audio processing system outputs the text; obtaining a selected domain image based on the trigger information; determining a selected text field according to the selected field image; and performing matching combination according to the selected text field and the output text to obtain final uploading information, and uploading the final uploading information to a preset storage database for storage. The information processing method and device have the effect of improving the information processing accuracy.

The invention provides a track inspection robot abnormal sound detection method based on a convolution auto-encoder, and the method comprises the steps: firstly collecting sound data of the normal work of coal mine transportation equipment in an inspection region through sound pickup equipment carried on a track inspection robot, and carrying out the environment noise reduction of the sound data through a noise reduction algorithm; secondly, extracting Mel-frequency cepstral coefficient features of the sound of the coal mine transportation equipment through a feature extraction algorithm, inputting normal audio features into a convolutional auto-encoder network for training to obtain reconstructed audio features, and calculating a mean square loss function of the input normal audio features and the reconstructed normal audio features to obtain a reconstruction threshold value; and finally, calculating a mean square loss function of other audio features input into the auto-encoder network, and detecting the abnormal sound of the coal mine transportation equipment in a mode of judging whether the mean square loss function exceeds a reconstruction threshold value or not. According to the detection method, the problem that enough abnormal samples are difficult to accumulate in a short time due to the fact that the abnormal sound of the coal mine transportation equipment has occasionality and diversity can be solved, and the abnormal sound of the coal mine transportation equipment can be detected by the track inspection robot under the condition that the abnormal samples are lacked; and the production safety problem caused by the abnormity of the coal mine transportation equipment in the coal mine production can be early warned in time.

The invention provides an abnormal sound detection method based on multi-scale time-frequency feature perception, and relates to the technical field of acoustic detection for industrial machine state monitoring, and the method comprises the steps: inputting an original sound signal, and carrying out the dual-branch feature extraction to generate a time domain coding spectrogram and a logarithmic Mel spectrogram; executing hybrid data enhancement; splicing the enhanced spectrogram and inputting the enhanced spectrogram into a dense encoder for compression modeling; extracting features through a two-stage multi-scale time-frequency sensing network, processing along a time dimension in the first stage, and processing along a frequency dimension in the second stage; inputting the high-order features into a lightweight classifier to output an abnormal score; and comparing the abnormal score with the gamma distribution threshold to judge abnormity. According to the method, the weak anomaly detection rate and the cross-equipment stability are remarkably improved, and the perception and discrimination capability of the model on the multi-scale time-frequency characteristics is effectively enhanced.

The invention relates to the technical field of identity recognition, in particular to a multi-modal data fusion identity authentication and security monitoring system based on deep learning. Comprising an identity authentication module, a real-time analysis module and a remote control module, and the identity authentication module is used for collecting face images, sound and behavior pattern multi-modal data of a user, performing multi-modal data fusion and outputting identity authentication data of the user; the real-time analysis module is used for respectively collecting real-time video data and audio data, fusing the picture detection data and the sound detection data and outputting real-time abnormal behavior data; the remote control module is used for storing the identity authentication data and the real-time abnormal behavior data and transmitting the data in a remote access mode; through the above mode, the identification precision is improved during analysis, and the interference of environmental factors is reduced, so that the safety of the whole identity authentication system and the user experience are improved.

The present application provides a self-supervised machine abnormal sound detection method based on a dual-path WaveNet, including: Step 1: Use WaveNet in the frequency-domain path to model the frequency information of each time frame of the input sound signal and extract frequency-domain features; Step 2: The time-domain path takes the output of the frequency-domain path as input, transposes it, and models the time dimension; Step 3: The outputs of the frequency-domain path and the time-domain path are further fused through a two-dimensional convolutional layer to generate a one-dimensional embedding vector; Step 4: Adopt a self-supervised strategy to construct a classification task using metadata to train the model; Step 5: For the trained model, in the detection stage, determine whether it is normal according to the abnormal score of the model test sample. The dual-path design of the present application realizes the comprehensive modeling of the time-frequency spectrogram, not only retains the local details of the time and frequency dimensions, but also can effectively capture the frequency-domain harmonic information and the time-domain dynamic characteristics, and solves the problem of insufficient modeling of complex features in the time-frequency spectrogram.

The invention relates to the technical field of equipment testing, in particular to an intelligent testing method and system for sound equipment production defects, and the method comprises the steps: obtaining hardware parameter data corresponding to a to-be-tested sound equipment, inputting the hardware parameter data into a preset automatic testing platform, the automatic test platform performs preliminary hardware function detection on the to-be-tested sound based on the hardware parameter data to obtain a corresponding hardware detection result; when the production line tests the sound equipment to be tested in different working states, key parameter sets of the sound equipment in different working states are collected in real time based on an embedded sensor, and a hardware detection result is associated with each key parameter set; and calling a pre-trained detection model, inputting the associated key parameter set and the hardware detection result into the detection model to identify a potential defect mode, obtaining a corresponding detection analysis result, and generating a detailed test report based on the detection analysis result. According to the invention, the accuracy of sound comprehensive detection can be improved.

The present invention provides a method for detecting abnormal sound of multiple types of machines based on a diffusion model, comprising the following steps: collecting normal sound data, wherein the normal sound data contains category information, and constructing a training data set; preprocessing the normal sound data in the training data set to generate a mel-spectrogram, and generating a label corresponding to the category; training a multi-class abnormal sound detection network based on a diffusion model according to the label corresponding to the category and the mel-spectrogram; using the trained multi-class abnormal sound detection network to make mean‑GMM features, and training a Gaussian mixture model; inputting the sound data to be detected into the multi-class abnormal sound detection network and the Gaussian mixture model to obtain a detection result. The present invention designs an abnormal score algorithm based on a Gaussian mixture model, which evenly considers the continuity and change characteristics of the sound data in the time domain. A more fine-grained abnormal score is provided, which significantly improves the accuracy of detection and the adaptability to complex industrial environments.

The invention discloses a machine abnormal sound detection method and system based on a dynamic attention mechanism, and the method comprises the steps: constructing a feature extraction network and a detection network, extracting the frequency domain-time domain features of machine audio in the feature extraction network through a log-Mel spectrogram and an improved WaveNet and TgramNet network, obtaining a feature fusion spectrogram, and carrying out the detection of the abnormal sound of a machine. A mobile FaceNet network and a detection network of an OCBAM dynamic attention mechanism are included, machine audio is obtained, data labeling of a specific machine type and a working state is carried out on the machine audio, a feature extraction network is used for carrying out feature extraction on a machine audio data set, a detection model is trained, and the detection model is used for detecting abnormal sound. According to the invention, the problem of difficulty in machine abnormal sound detection in a complex industrial environment is solved, the safety and efficiency of industrial production can be effectively improved, the production process and quality control are optimized, and the downtime and maintenance cost of equipment are reduced.

A method, computer system, and a computer program product for environment monitoring. Exemplary embodiments may include emitting one or more soundwaves within an environment, detecting the emitted one or more soundwaves, and identifying motion of an object within the environment based on an analysis of the detected one or more soundwaves.

The invention is suitable for the technical field of butterfly valve control, and particularly relates to a butterfly valve rapid cut-off control method and a butterfly valve. Wherein the position information comprises a first position and a second position, the first position is used for reflecting the position of a first sound detection device arranged at the upstream of the butterfly valve, and the second position is used for reflecting the position of a second sound detection device arranged at the downstream of the butterfly valve; acquiring sound information according to the position information; analyzing according to the sound information to obtain a change graph; wherein the change pattern is used for reflecting a sound pattern formed by impact of the fluid on the pipeline in the process that the fluid flows from the first position to the second position; analyzing according to the change graph and the sound information to obtain closing information; wherein the closing information is used for indicating the closing time of the butterfly valve. According to the butterfly valve quick cut-off control method, noise in the pipeline can be reduced, and damage to the butterfly valve and the pipeline due to pressure shock waves is reduced.

A computer-implemented method for training an anomaly detection neural network system comprising an encoder and a decoder is described. The method includes receiving training data comprising a plurality of training examples, each training example including a training audio waveform and a machine identity (ID); processing the training audio waveform to extract training audio features; receiving an environmental noise audio waveform; processing the environmental noise audio waveform to extract noise features; generating augmented features by combining the extracted training audio features and the noise features; processing, using the encoder, the augmented features to generate latent embeddings; processing, using the decoder, the latent embeddings to generate reconstructed audio features; processing, using a convolutional neural network, the augmented features to generate a predicted machine ID probability distribution; and adjusting, through backpropagation, the current values of the parameters of the encoder and the decoder to minimize an objective function.

The invention provides a machine equipment abnormal sound detection method, a medium, equipment and a product, and relates to the technical field of abnormal sound detection, and the method comprises the steps: carrying out the data enhancement processing of an original audio during the operation of machine equipment, and dividing the original audio into a source domain sample and a target domain sample; the waveform features of the enhanced audio are converted into FFT spectrogram features, amplitude spectrogram features and logarithmic Mel spectrogram features; constructing three networks, extracting three embedded features, and then connecting; based on the splicing features of the source domain samples, obtaining a plurality of clustering centers of each category of samples in the source domain through clustering, and taking the minimum value of cosine distances between the source domain samples and all the clustering centers as the similarity score of the source domain; and based on the splicing features of the target domain samples, taking the minimum value of the cosine distance between the target domain samples and the sample mean value as the similarity score of the target domain, and taking the smaller value of the two similarity scores as an abnormal score. The method can effectively cope with the situation of domain generalization.

An unsupervised industrial abnormal sound detection method combining self-supervised learning and a Gaussian mixture model (GMM) is provided to deal with intelligent industrial equipment monitoring challenges. Traditional diagnosis depends on manpower or rules, various faults are difficult to process, and data are uneven. According to the method, TinyCRNN and GMM are used, audio is processed into a Mel spectrogram through Librosa, normal sound features are extracted through self-supervision, then abnormity is identified through GMM clustering analysis, and performance is evaluated through AUC and the like. According to the method, dependence on labeled data is reduced, the problem of data scarcity is solved, detection accuracy and robustness are improved, the application potential in industrial fault detection is large, and especially the advantage on unknown faults is remarkable.

The invention relates to a glass capillary microscopic flaw detection method and system, and relates to the field of material nondestructive testing, and the method comprises the following steps: collecting a capillary detection signal; when the capillary detection signal is consistent with the detection starting signal, collecting a capillary serial number; determining a sound detection number according to the capillary number; obtaining a standard reaction characteristic based on the capillary serial number and the sound detection serial number; generating and transmitting a corresponding sound wave signal to the capillary tube based on the sound detection number, and collecting sound reaction characteristics; when the sound reaction characteristics are inconsistent with the standard reaction characteristics, controlling vibration auxiliary equipment to apply auxiliary vibration to the capillary tube, and collecting post-vibration sound reaction characteristics; if the post-vibration sound reaction characteristics are inconsistent with the sound reaction characteristics, a capillary tube cleaning prompt is reported; if the after-vibration sound reaction characteristics are consistent with the sound reaction characteristics, capillary tube defect prompts are reported. The method has the effect of improving the detection accuracy of the glass capillary.

An image forming apparatus includes: an image forming unit that forms an image on a recording medium; plural sound detection units that detect a sound of the image forming unit; and a specifying unit that specifies an abnormality of the image forming unit based on information output from the plural sound detection units, in which the specifying unit is provided in a number less than the number of plural sound detection units.

The invention discloses a transformer fault sound detection and early warning method, and belongs to the technical field of transformer detection, and the method comprises the steps: collecting the working sound data of a transformer in a fixed time period, including frequency, amplitude and duration; according to the working sound data of the transformer, analysis is carried out based on a pre-trained fault sound detection model, a fault type is output, and fault early warning is carried out; wherein a layered attention mechanism, residual connection and a Mish activation function are introduced to improve the time delay neural network, and the fault sound detection model is constructed based on the improved time delay neural network and a large language model. According to the invention, the problem that the fault type of the transformer cannot be accurately and timely identified and automatic early warning cannot be carried out in the prior art is solved.

The invention relates to the technical field of industrial equipment fault diagnosis and audio analysis, in particular to a machine abnormal sound detection method and system and a storage medium, and the method comprises the steps: obtaining a to-be-detected sound; preprocessing the to-be-detected sound to obtain a spectrogram of the to-be-detected sound; the spectrogram of the sound to be detected is input into an abnormal sound detection model for recognition, and the abnormal sound detection model comprises a student network, a teacher network and a reconstruction head. According to the embodiment of the invention, the global semantic information and the local detail features of the abnormal sound can be captured at the same time by combining the self-distillation and the mask self-encoder, the accuracy and the stability of abnormal sound detection are remarkably improved, and the method is suitable for abnormal detection scenes of various machine equipment and has relatively strong generalization ability.

The invention provides a buried pipeline leakage optical fiber composite detection method and device, and the method comprises the steps: S1, obtaining the sound wave information of a to-be-detected optical fiber through an optical fiber sound detection method; the to-be-tested optical fiber is mounted near a to-be-tested buried pipeline; s2, determining initial condition information of the buried pipeline to be detected based on the sound wave information; if the initial condition information is normal, returning to the step S1; if the initial condition information is abnormal, determining abnormal location; s3, determining the temperature value of the soil around the abnormal positioning through an optical fiber temperature measurement method; s4, determining target condition information of the buried pipeline to be measured according to the temperature value of the soil around the abnormal positioning; if the target condition information is abnormal, determining that the buried pipeline to be detected leaks, and giving an alarm; and if the target condition information is normal, returning to the step S1. Therefore, the alarm accuracy and timeliness are improved.

A device to perform target sound detection includes a memory including a buffer configured to store audio data. The device includes one or more processors coupled to the memory. The one or more processors are configured to receive the audio data from the buffer. The one or more processors are configured to detect the presence or absence of one or more target non-speech sounds in the audio data. The one or more processors are further configured to generate a user interface signal, to indicate one of the one or more target non-speech sounds has been detected, and provide the user interface signal to an output device. The device further comprises the output device that is configured to output a visual representation associated with the one of the one or more target non-speech sounds has been detected.