333 results about "Audio recognition" patented technology

The invention provides a multi-mode cooperative sensing power station high-risk operation inspection method and system, and relates to the technical field of video recognition, and the method comprises the steps: activating an unmanned plane and a quadruped robot after a power station operation task is started; starting a video acquisition unit, and establishing a synchronous video stream; carrying out fusion alignment with the global reference coordinate system through an external synchronization signal; inputting the video sequence of the fused view angle into a multi-view angle action behavior recognition network, and establishing a dangerous behavior grade score; auditory data and olfactory data of the quadruped robot are obtained, and linkage abnormity is established; and polling abnormity is reported according to linkage abnormity and dangerous behavior grade scores. Through the method and the device, the technical problem of low inspection efficiency caused by difficulty in comprehensively identifying potential risks in a dynamic environment due to limitation of a single sensing mode is solved, and the inspection efficiency of a power station is improved by fusing multi-mode data, timely finding and processing the potential risks and improving the accuracy of video and audio identification.

The invention is suitable for the technical field of intelligent equipment, and provides an intelligent conference recording method and device, intelligent equipment and a storage medium, and the method comprises the steps: collecting a video frame sequence image and an on-site audio of a conference site in real time; identifying whether a target speaking triggering posture exists in the video frame sequence image or not; determining a current spokesman and identity information of the current spokesman based on the recognized target speaking triggering posture and the conference participant information record; associating speaking content corresponding to an on-site audio collected from the moment when the target speaking triggering posture is recognized with the spokesman and the identity information thereof; and generating a conference record according to all spokesmen determined in the conference site and the speaking content associated with the identity information of the spokesmen. Conference recording can be completed accurately and efficiently, and retrieval and sharing of conference information are facilitated.

The invention discloses a forest fire audio recognition method based on a BP neural network, a medium and a product in the technical field of forest fire recognition. The method comprises the following steps: acquiring forest fire audio data to be detected; extracting a Mel-frequency cepstrum coefficient (MFCC) based on the frequency domain features of the forest fire audio data to be detected; inputting the Mel-frequency cepstral coefficient MFCC into a pre-trained forest fire audio recognition model based on a BP neural network, and outputting a forest fire category; wherein the forest fire category comprises a forest fire and a non-forest fire. Compared with a traditional visual monitoring means, the method can cover a wider forest area, in the same monitoring range, in addition, model training based on the BP neural network can efficiently process a large amount of audio data and rapidly output an identification result, the efficiency and accuracy of forest fire identification are further improved, and the method is suitable for popularization and application. The problems of limited coverage range, high cost and low accuracy in the existing forest fire identification technology are solved.

Turntable control systems are disclosed. A playback device can include an analog audio source component such as a turntable. The playback device is configured to cause playback of first audio based on analog audio signals generated by the analog audio source component. Based on an indication that analog audio signals are no longer being generated, the playback device requests, via a network interface, audio content from one or more remote computing devices. The request can include identifying related content based on the first audio. The playback device receives, via the network interface, second audio content from, and causes playback of second audio based on the second audio content.

The invention discloses a power distribution line bird repelling method and system based on Internet of Things edge calculation, and relates to the technical field of power distribution network safety. The system comprises: a bird condition acquisition module configured to obtain a bird audio and a bird image to be detected; a model identification module configured to identify the bird audio by using a bird sound identification model and identify the bird image by using a bird image identification model; the controller module is configured to integrate the bird audio recognition result and the bird image recognition result to obtain the type of the bird; the intelligent decision-making module is configured to generate a bird repelling strategy according to the types of the birds; and the bird repelling module is configured to repel birds according to the bird repelling strategy. According to the invention, birds are identified by utilizing edge calculation, so that different bird repelling modes are adopted for different birds, and intelligent and effective modern bird repelling is realized.

The invention discloses an audio coal gangue identification method. The method comprises the following steps: S1, collecting audio signal data in a coal caving process of a related field of a fully-mechanized face; s2, preprocessing the audio signal data to obtain the audio signal data after noise reduction; s3, performing data calibration on the audio signal features; s4, constructing an audio coal gangue recognition algorithm model; and S5, outputting a coal and gangue recognition result by using the audio coal and gangue recognition algorithm model. According to the invention, a novel audio identification system mainly based on the audio acquisition sensor is utilized, automation of top coal caving of the fully-mechanized caving face is realized through voice identification of coal caving, gangue caving and non-coal caving of the fully-mechanized caving face, and the system has the characteristics of high-precision coal caving and gangue identification and improvement of coal caving efficiency.

The embodiment of the invention provides a virtual human video generation method and device, a computer program product and electronic equipment. The method comprises the following steps: identifying corresponding text content according to original audio, and determining skeleton posture graphs of multiple groups of human body actions; processing the text content and the skeleton posture graph through a large language model, and outputting split mirror planning information corresponding to the text content; the sub-mirror planning information comprises an overall style, scene description, figure description and sub-mirror attribute information of each sub-mirror; generating a scene dynamic graph based on the overall style and the scene description, and generating a character static graph based on the overall style and the character description; and according to the character static graph bound to each sub-mirror and the sub-mirror attribute information, generating a character motion graph of each sub-mirror, and combining the scene motion graph and the character motion graphs of the plurality of sub-mirrors to generate a virtual human video in the same scene. According to the technical scheme of the embodiment of the invention, the efficiency of generating the virtual human video can be improved.

The invention discloses a sound duplicating method and system, and the method comprises the steps: obtaining a long audio from a network, and carrying out the audio processing, and obtaining a preprocessed audio; performing noise reduction processing on the preprocessed audio based on a deep learning noise reduction method to obtain a noise-reduced voice; performing long audio recognition and alignment on the noise-reduced voice by adopting a large multi-language voice recognition model to obtain an alignment result; segmenting the long audio and the long text in the alignment result to obtain a segmentation result; performing multi-modal feature extraction on the segmentation result to obtain a multi-modal feature; performing sound repetition training and reasoning according to the multi-modal features; and during sound duplicate reasoning, data generation is carried out. The long audio alignment accuracy is improved, and the sound duplicating effect is improved.

The invention discloses an audio recognition and noise reduction processing method and system for a power grid regulation and control workbench environment, and the method comprises the steps: obtaining pure audio signals of all power grid regulation and control workers in different working scenes and different background noises in a dispatching desk, carrying out the cross-domain fusion of two features of a time domain and a frequency domain, and taking the two features as a training set to train a speaker model; voice signals are collected in real time, and pure noise and silence in the voice signals are removed; according to the noise level estimation and the noise power, carrying out weighting processing on the voice signal after the pure noise and the mute are removed; recognizing and separating human voice fragments by adopting a continuous down-sampling and resampling algorithm with multi-resolution characteristics; judging whether the human voice fragment contains a target speaker or not through a speaker model; and generating a plurality of voice segments based on the human voice segments, and extracting target speaking human voice through clustering and a speaker model. The method adapts to environmental changes of regulation and control dispatching desks at all levels, environmental noise can be remarkably reduced, and accurate recognition and extraction of human voices are achieved.

The invention discloses an audio gender recognition method based on an LSTM architecture neural network, and the method comprises the following steps: 1, carrying out the audio preprocessing, and carrying out the sampling rate conversion, noise elimination and voice segment positioning; 2, feature extraction: extracting features from the audio signal, and taking the features as the input of an LSTM network; 3, constructing and training an LSTM (Long Short Term Memory) model; and 4, gender classification: after model training is completed, performing gender classification on a new audio signal by using the constructed model. The invention aims to overcome the limitation of the prior art and improve the accuracy and adaptability of gender recognition, especially in noise environment and cross-age recognition tasks. By introducing an advanced deep learning technology and an innovative feature engineering strategy, the speaker audio gender recognition method is devoted to promote the development of a speaker audio gender recognition technology, so that the speaker audio gender recognition technology can be applied in a wider range of scenes.

The invention discloses a bipartite graph matching-based speaker recognition method and electronic equipment, and belongs to the technical field of audio recognition. The method comprises the following steps: acquiring an audio stream, and segmenting the audio stream into continuous target duration audio clips in real time; determining a voiceprint embedding vector corresponding to each audio clip of the target duration; determining an embedding matrix based on the voiceprint embedding vector; determining a target voiceprint feature corresponding to each target duration audio clip according to a bipartite graph method; calculating the similarity between the voiceprint embedding vectors; and grouping the voiceprint embedding vectors based on the similarity between the voiceprint embedding vectors, and clustering the voiceprint embedding vectors corresponding to the same speaker to obtain a clustering result and generate a target recognition report. According to the invention, the calculation complexity can be reduced and the identification precision can be improved.

An end-to-end dialect audio recognition method (10) based on multi-layer information fusion. The method (10) comprises: performing audio preprocessing on dialect audio to generate an acoustic feature (S102); inputting the acoustic feature into a coder, and then performing, by the coder, a progressive downsampling operation on the acoustic feature to generate multi-layer fine-grained acoustic features (S104); performing, by means of a layer adaptation module, multi-layer information fusion on the multi-layer fine-grained acoustic features to generate a fused acoustic feature (S106); performing, by means of a cross-attention mechanism, cross-fusion on the fused acoustic feature to generate a corrected acoustic feature (S108); and inputting the corrected acoustic feature into an end-to-end dialect recognition model to generate a dialect audio recognition result (S110). In the method, complex speech signals and multi-accent features can be efficiently captured and processed, and dialect audio can also be classified and decoded online in real time, thereby improving the accuracy and robustness of speech recognition.

The invention relates to the technical field of high-voltage switch fault diagnosis systems, and discloses a high-voltage switch fault diagnosis system based on an artificial intelligence audio recognition technology, and the system comprises at least one audio collection device which is used for collecting audio signals generated in the operation process of a high-voltage switch; the data preprocessing module is used for preprocessing the audio signals, and the feature extraction module is used for extracting feature vectors from the preprocessed audio signals. According to the high-voltage switch fault diagnosis system based on the artificial intelligence audio recognition technology, by using the high-sensitivity MEMS microphone and the advanced audio processing technology, weak abnormal sound in the operation process of a high-voltage switch can be accurately captured, the fault diagnosis precision is improved, various sensor data such as a temperature sensor and a vibration sensor are combined, and the fault diagnosis accuracy is improved. The accuracy and reliability of fault diagnosis are improved, the system can automatically adjust the sensitivity parameters of the model according to the environmental noise level, and false alarms are reduced.

The invention relates to the technical field of computers, and discloses a manuscript generation method, system and device and a storage medium, and the manuscript generation method comprises the steps: carrying out the audio recognition of a video file to obtain an audio recognition result, and carrying out the recognition of subtitles of the video file to obtain a subtitle recognition result; adding a segmentation punctuation in the audio recognition result, and obtaining audio sub-strings segmented according to a target segmentation punctuation; for any audio sub-character string, according to the timestamp of the audio sub-character string, acquiring a subtitle sub-character string located in the same time period as the audio sub-character string from the subtitle recognition result, and correcting the audio sub-character string according to the subtitle sub-character string; and outputting the corrected audio recognition result as a video manuscript of the video file.

Provided is an audio recognition method. The method includes that audio data is encoded to an audio encoding feature; the audio encoding feature is decoded to a first decoded feature; a preset word text and the first decoded feature are encoded to a first text encoding feature, the first text encoding feature including a feature representing semantics of the preset word text and a feature representing semantics of the audio data; and the first text encoding feature is decoded to a predicted audio text, where the predicted audio text represents the semantics of the preset word text and the semantics of the audio data. An audio recognition apparatus and a storage medium are also provided.

The invention relates to the technical field of AI audio chips, and discloses an audio processing model quantification method and system, and the method comprises the steps: applying an incremental voltage pulse to a multi-layer memristor cross array, measuring a conductivity value, and generating a memristor quantification configuration table; programming the neuron units in the multi-layer memristor cross array to obtain quantized neuron units; calculating a synaptic weight matrix; the audio signal to be processed is input into the quantization neuron unit to execute pulse calculation, the audio features are obtained, and the audio recognition result is generated on the end-side processor, so that the problems of nonlinearity and element difference of the memristor are effectively solved, the robustness of the system is improved, and the optimal balance of precision and power consumption can be realized in different scenes.

The embodiment of the invention provides a multi-mode emotion recognition method and device, electronic equipment, a storage medium and a product, and relates to the technical field of emotion recognition. The method comprises the following steps: acquiring a to-be-recognized audio / video which comprises an audio stream and a video stream, segmenting the audio stream to obtain at least one audio segment, inputting each audio segment into an audio recognition model to obtain an audio recognition result, determining a corresponding video segment in the video stream according to a target audio segment of which the audio recognition result is an emotion result, and inputting the video segments into a video recognition model to obtain a video recognition result, and determining a target emotion result of the to-be-recognized audio and video based on the audio recognition result and the video recognition result. According to the embodiment of the invention, the video emotion recognition is used for assisting the audio emotion recognition to complete the emotion recognition of the audio and the video, errors possibly caused by single audio recognition are avoided, and the recognition accuracy can be improved.

The invention relates to an audio recognition method and device based on marking and backtracking correction, and the method comprises the steps: segmenting a target audio, and enabling adjacent segments after segmentation to have a partial overlapping region; if a slice point exists in a non-mute segment of the target audio and the acoustic feature similarity of a preset number of frames before and after the slice point is smaller than a set similarity threshold value, the slice point is marked as a cut-off risk point, and the cut-off risk point is used for indicating that continuous semantics before and after the slice point has a cut-off risk; after a text corresponding to the audio of each slice is recognized through the speech recognition model, the language confidence degree of an overlapping area where the truncation risk point is located is determined, and the language confidence degree is used for indicating context language logic of the overlapping area; and if it is determined that the language confidence is lower than a set confidence threshold, correcting the recognition texts of the slices before and after the truncation risk point. According to the invention, the accuracy of audio recognition is improved.

The invention provides a bullying behavior detection and judgment method and device based on audio and behavior feature recognition, and the method comprises the steps: extracting and analyzing the emotion, keyword and abnormal sound features through an audio recognition module, and judging whether a bullying-related language or audio behavior exists in real time; the behavior recognition module is used for behavior data processing and time sequence feature extraction, behavior classification is performed in combination with a GRU network, and detection and recognition of bullying related behaviors are completed; in combination with the recognition results of the audio recognition module and the behavior recognition module, whether the bullying behavior occurs in the current scene is judged, and if the judgment result is yes, an alarm is given and corresponding personnel are notified to go to the current scene; otherwise, recording the identification result to the cloud. The beneficial effects of the invention are that through multi-dimensional technical means and process design, the bullying behavior can be efficiently, accurately and sustainably monitored, pre-warned and recorded, and powerful technical guarantee is provided for building a safe and friendly environment.

The embodiment of the invention relates to the technical field of hearing aids, and discloses a hearing aid noise reduction method and device, and the method comprises the steps: collecting environment audio data through a hearing aid; inputting the environment audio data into a preset audio recognition model, and obtaining target noise data through the audio recognition model; and performing visual adjustment on target noise data in the environment audio data to generate target audio data. According to the method, the noise source can be actively identified, the frequency range and the loudness of the noise source are obtained, the noise source is selected visually, and the loudness of the noise source in the hearing aid is adjusted in real time, so that discomfort of hearing aid caused by continuous output of periodic environmental noise to human ears is avoided, and the hearing aid is more comfortable to use. And the condition of tinnitus is avoided. Meanwhile, through visual operation of the hearing aid, the user can operate the hearing aid by himself / herself very conveniently, and the applicability of the hearing aid is improved.

The embodiment of the invention discloses a video stream mixing method and device, electronic equipment and a storage medium. According to the embodiment of the invention, an editing interface comprising a multi-track area can be displayed, the multi-track area comprises a video track and a dubbing subtitle track, and a target video is loaded in the video track; in response to the starting dubbing operation, starting to record audio at the first progress of the dubbing subtitle track; in response to the dubbing ending operation, generating a dubbing fragment and an audio recognition text corresponding to the dubbing fragment; a mixed stream video is generated in response to the stream mixing operation. According to the invention, a rapid flow mixing mode for adding dubbing and corresponding subtitles at the same time is provided, manual configuration is not needed, a user only needs to input the voice, the audio recognition text corresponding to the voice can be directly displayed in the dubbing subtitle track as the subtitles when recording is completed, and the voice and the audio recognition text are mixed into the video during flow mixing. Therefore, according to the scheme, the operation complexity of the video stream mixing method can be reduced.

The embodiment of the invention provides a voice recognition training method, device and equipment based on the industrial and mining field and a medium, and relates to the technical field of voice recognition. Acquiring voice data and storing the voice data in a voice database; pre-training the audio recognition model by using the voice data of the voice database; training the audio recognition model according to the working condition voice library in a transfer learning mode; evaluating the prediction performance of the audio recognition model according to a set index; the method can adapt to different dialects and terminologies, and can be flexibly applied to various industrial and mining environments, so that the accuracy is improved, and the prediction result is improved.

Techniques for automatically de-identifying sensitive information in audio conversations by combining un-transcribed voice activity detection (VAD) with large language model (LLM) analysis are disclosed. An audio de-identification system processes speech-to-text transcriptions while identifying segments where automatic speech recognition (ASR) failed to transcribe spoken content. These un-transcribed segments are represented as placeholders in prompts sent to an LLM, which analyzes the surrounding textual context to determine if sensitive information (such as PII or PHI) was likely spoken during these gaps. When sensitive content is identified, the system modifies the corresponding audio segments through an audio identification tactic. This approach addresses the technical challenge of incomplete de-identification in automated audio processing by leveraging LLMs' contextual understanding to detect sensitive information in segments that traditional ASR systems miss, particularly in scenarios involving poor audio quality or diverse accents. The result is a more comprehensive and reliable audio de-identification system.

The invention discloses an intelligent deep learning method and system for animal audio voiceprint recognition, and relates to the technical field of audio recognition. Animal audios of a plurality of monitoring nodes in a target area are collected, the animal audios are processed into standardized audio information packets, non-target sound sources are filtered out, and target sound sources are extracted; the method comprises the steps of performing multi-modal feature extraction on a target sound source, performing confidence evaluation after obtaining multi-modal voiceprint features, obtaining the multi-modal voiceprint features meeting a confidence screening threshold as a modeling data set, constructing a mixed deep learning model, inputting the modeling data set, performing voiceprint modeling, and generating a voiceprint feature library and a target voiceprint template. And inputting animal audio voiceprints needing to be recognized into the voiceprint feature library, calculating voiceprint similarity, marking out a target voiceprint fragment conforming to a target voiceprint template, judging whether a fuzzy fragment region exists or not, determining whether context semantic completion is performed or not according to a judgment result, and outputting a recognition result of final complete voiceprint information.

The invention provides a livestock and poultry abnormal sound recognition and positioning method and system, and relates to the technical field of livestock and poultry breeding monitoring, and the method comprises the steps: extracting the sound characteristics of a livestock and poultry sound signal, carrying out the abnormal audio recognition according to the characteristic pattern of the sound characteristics, and obtaining the sound type of the sound signal; when the sound category is abnormal audio, calculating a generalized cross-correlation delay inequality to determine a sound source position range of the sound signal; and calling a particle swarm optimization algorithm to search the generalized cross-correlation delay inequality to obtain a distance value of an optimal sound source position of the sound signal, calling an SRP-PHAT algorithm to search to obtain a sound source position space point of the sound signal in a sound source position range, and finally determining a three-dimensional position coordinate when the livestock and poultry make a sound. Through the method and the device, the defects that livestock abnormal audio recognition is easily interfered by factors such as external environment noise and the like in the prior art, and the sound source positioning precision is limited in a multi-sound-source scene are overcome.

The invention relates to the technical field of artificial intelligence, and provides an audio recognition model training method, an audio recognition model training device, a computer readable storage medium and electronic equipment, and the method comprises the steps: obtaining a plurality of groups of training samples, and collecting each piece of frequency spectrum information corresponding to each audio in each group of training samples; extracting each spectrum feature through a feature extraction network, and encoding each spectrum feature through an audio encoder to obtain each audio vector; determining a comparison loss value corresponding to each group of training samples according to the difference degree between the audio vectors corresponding to each group of training samples, and performing iterative training on the feature extraction network and the audio encoder according to multiple groups of comparison loss values; and when the comparison loss value satisfies a first preset convergence condition, freezing the feature extraction network and the audio encoder, and performing iterative training on the classifier according to the audio vectors corresponding to the multiple groups of training samples to obtain an audio recognition model. The audio recognition accuracy can be improved.

The invention relates to a blowing-based screen operation method, system and equipment and a medium, and belongs to the field of blowing detection. The method comprises the following steps: acquiring a blowing audio and a face image of a user, and performing face contour detection on the face image based on a face contour recognition algorithm; when the face contour of the user meets a preset detection condition, whether the mouth shape of the user in the face image is a blowing mouth shape or not is judged based on a blowing mouth shape recognition algorithm; when the user is in the blowing nozzle type, the identity of the user is recognized according to the blowing audio of the user based on a blowing voiceprint recognition algorithm; wherein the identity of the user comprises a registered user and a non-registered user; when the identity of the user is a registered user, acquiring a sight focus from the face image based on an eyeball focus recognition algorithm; and executing an operation of the corresponding screen area according to the sight focus. The accuracy of blowing behavior detection is greatly improved, and effective blowing interaction between the screen and the user is achieved.

The invention discloses an intelligent sound signal sensing method and system based on time-frequency feature fusion, and relates to the technical field of intelligent sound signal processing, and the method comprises the steps: obtaining an original audio signal; performing short-time Fourier transform on the original audio signal to obtain a plurality of frequency spectrum features; inputting the plurality of frequency spectrum features into a plurality of neural networks for processing, and extracting frequency domain depth features; processing the original audio signal by using a one-dimensional convolutional neural network, and extracting time domain depth features; splicing and fusing the frequency domain depth feature and the time domain depth feature in a channel dimension to obtain a time-frequency fusion feature; and inputting the time-frequency fusion feature into a full connection layer classifier to obtain a final audio scene recognition result. According to the invention, by designing a double-branch network architecture of time domain and frequency domain parallel processing, time domain waveform information and frequency domain amplitude and phase information are effectively fused, and in an end-to-end training scene, the accuracy of audio recognition is remarkably improved.

The invention relates to an audio-to-sign language conversion method and device, equipment, a readable storage medium and a program product. The method comprises the following steps: capturing real-time audio on a webpage; performing voice recognition on the real-time audio by adopting a pre-trained audio recognition model to obtain a text sequence corresponding to the real-time audio; analyzing the text sequence to obtain a plurality of sign language actions; and synthesizing the plurality of sign language actions into a continuous sign language animation, displaying the sign language animation in the webpage, and playing real-time audio. By adopting the method, the real-time performance of converting the audio into the sign language can be improved.

The invention relates to the field of audio data analysis, in particular to an audio data processing method and device, equipment and a storage medium. The method comprises the following steps: carrying out multi-angle audio continuous acquisition and scene noise suppression on a conference room through a surrounding microphone array, and generating a filtering optimization audio signal; performing three-dimensional time difference positioning calculation according to the filtered and optimized audio signal to obtain accurate sound source positioning information; personalized voiceprint analysis is carried out on the filtered and optimized audio signals, audio distribution modeling is carried out based on accurate sound source positioning information, and a multi-person conference audio field is constructed; performing parallel audio stream separation according to the multi-person conference audio field to generate an intelligent spliced audio segment; and performing deep semantic analysis and semantic logic correction on the intelligent spliced audio segment to generate an audio analysis result. According to the invention, rapid and accurate speaker audio recognition of a multi-person parallel conference is improved.