51184 results about "Audio frequency" patented technology

The invention discloses a domain speech recognition method and system based on RAG. The method comprises the steps that an original domain document set is acquired, a hierarchical document structure is constructed through subject classification, paragraph segmentation and sentence segmentation, vectorization processing is executed to generate a multi-granularity vector matrix set, and a hierarchical knowledge vector index structure is established; performing multi-scale time-frequency analysis on the real-time audio stream, extracting and enhancing acoustic features, and generating a multi-scale feature tensor; performing adaptive blocking and progressive attention processing on the feature tensor, constructing a probability estimation model, and generating a candidate text sequence; executing multi-layer retrieval in the knowledge index based on the multi-dimensional query vector to generate related knowledge fragments; and carrying out space mapping and dynamic attention processing on the features, and executing probability optimization decoding to obtain a final recognition text. According to the invention, through knowledge enhancement and multi-level feature processing, the accuracy and robustness of voice recognition in the professional field are improved.

A headset antenna and a connector for the same are provided. The headset antenna includes an audio signal line, an antenna and a high impedance element in specified application frequency ranges. The audio signal line is adapted for transmitting an audio signal and the antenna is adapted for receiving an RF signal. The high impedance element is disposed on a transmission path of the audio signal and generates a high impedance at a specified frequency band of the RF signal, so that the audio signal line is equivalent to an open circuit and the antenna obtains a better receiving capability.

The invention provides a live broadcast room content identification and intelligent distribution method and system based on multi-modal fusion, and relates to the technical field of machine learning, and the method comprises the steps: collecting video, audio and bullet screen data of a live broadcast room, and carrying out the feature extraction; performing cross-modal interaction, feature alignment and feature reconstruction on the multi-modal features by using the multi-modal feature fusion model to generate cross-modal fusion features; and carrying out content identification based on a pre-trained content classification model to obtain a category label and a time sequence feature. Furthermore, a live broadcast room content portrait and an association network are constructed, and a live broadcast room real-time score is generated and a live broadcast room display weight and a push strategy are dynamically adjusted in combination with user historical watching behavior data and a deep reinforcement learning model. Through multi-modal data fusion and deep reinforcement learning, accurate identification and personalized recommendation of the live broadcast room content are realized, and the user experience and the platform operation efficiency are effectively improved.

Systems, devices, and methods for presenting audio associated with events associated with spatialized audio effects or non-spatialized audio effects in three-dimensional environments are disclosed. The spatialized and / or non-spatialized audio effects correspond with displaying, via a display generation component, a three-dimensional environment from a viewpoint of a user. While displaying the three-dimensional environment from the viewpoint of the user, the computer system detects an event. When the computer system detects an event which corresponds to a spatialized sound effect, the computer system presents the sound effect as emanating from a location in the three-dimensional environment associated with the event. When the computer system detects an event which corresponds to a non-spatialized sound effect, the computer system presents the sound effect as emanating from a predetermined location for audio in the three-dimensional environment that has a predetermined spatial relationship relative to the viewpoint of the user

The invention relates to a method and a system for monitoring and analyzing regional noise. The method comprises the following steps: carrying out partition processing on a target area according to an acoustic environment function standard to obtain acoustic environment function area division data and determine a monitoring point position distribution matrix; noise data and environment parameter data are collected, timestamp alignment is carried out, and a multi-dimensional associated data set is obtained; performing spectral analysis and data compensation to obtain a noise feature vector, and performing standard exceeding judgment to generate audio recording data; inputting a voiceprint recognition model for classification to obtain a noise source type identifier, and performing positioning calculation to obtain a noise source space coordinate; carrying out space-time distribution statistics to obtain a functional area noise standard-reaching rate and calculating contribution degrees of various noise sources; and carrying out graded early warning judgment, and generating alarm information and a noise pollution prevention and control instruction. According to the embodiment of the invention, the whole-process automatic management of monitoring and early warning of regional noise is realized.

The invention relates to the technical field of intelligent conference processing, and relates to a conference summary processing method and system using AI, and the method comprises the steps: carrying out the real-time noise suppression of a collected conference audio stream and associated text data through a noise suppression algorithm, and carrying out the cross-modal alignment of the denoised data through a cross-modal alignment algorithm; a domain-specific attention head is inserted into an attention layer of the pre-trained Transform model, a domain-enhanced speech recognition model is constructed, and audio is converted into a text sequence with a speaker tag; adopting a heterogeneous graph neural network to construct a structured topic evolution graph; key decision nodes in the structured topic evolution graph are extracted based on a reinforcement learning strategy, and a final conference summary document is generated. In the decoding stage, the fusion proportion of the acoustic model and the language model is dynamically adjusted based on the real-time acoustic confidence coefficient, the recognition rate of the vocabularies in the professional field is increased, and the problems of frequent term transcription errors and poor semantic coherence in the professional conference are effectively solved.

The invention relates to an intelligent MV generation method, system and device based on AIGC and a medium, and belongs to the technical field of artificial intelligence content generation. The method comprises the following steps: receiving an original music file input by a user and a user instruction; performing audio preprocessing on the original music file to generate a standardized audio signal, and mapping a user instruction into a semantic vector; rhythm features and emotion features are extracted, music feature vectors are generated and subjected to feature alignment with semantic vectors, and an initial key frame sequence is generated based on a diffusion model; performing particle effect density dynamic adjustment and shot switching point generation on the initial key frame sequence, outputting an enhanced key frame sequence, performing optical flow frame insertion and long time sequence prediction, and generating an optimized video frame sequence; and performing block parallel rendering on the optimized video frame sequence according to the target platform parameters to generate an MV file and metadata. According to the invention, deep fusion of audio and visual contents can be realized, and high-quality video contents can be generated according to individual demands of users.

The invention discloses an e-commerce platform commodity recommendation method and system based on user preference analysis, and relates to the technical field of e-commerce platforms, and the method comprises the steps: constructing a user multi-modal interest feature vector; the multi-modal interest feature vector fuses the text search history, the image browsing preference, the audio consumption habit and the video interaction behavior of the user; utilizing a cross-modal attention mechanism to match the multi-modal interest feature vector of the user with multi-modal attribute information of commodities to generate a candidate commodity set; and applying a dynamic context-aware reinforcement learning model to reorder the candidate commodity set according to real-time interaction behaviors of the user and environmental factors, and outputting a final recommendation result. According to the method, multi-modal interest feature extraction, dynamic context perception and reinforcement learning technologies are combined, so that comprehensive capture and accurate modeling of user interests are realized.

Systems, methods, and non-transitory computer readable media including instructions for interpreting facial skin micromovements are disclosed. An example head mountable system includes a housing configured to be worn on a head of a wearer. The head mountable system also includes at least one detector integrated with the housing and configured to receive light reflections from a facial region of the head and to output associated reflection signals. The head mountable system also includes at least one microphone associated with the housing and configured to capture sounds produced by the wearer and to output associated audio signals. The head mountable system also includes and at least one processor in the housing, configured to use both the reflection signals and the audio signals to generate output that corresponds with words articulated by the wearer.

The invention provides an intelligent sound box voice processing method and system based on artificial intelligence, and the method comprises the steps: obtaining audio data and mouth shape video data, carrying out the processing of the audio data and the mouth shape video data, and carrying out the multi-modal feature fusion, and obtaining a fusion feature; performing bimodal voice activity detection on the fusion features to obtain effective voice data; performing context sensing recognition of audio and video fusion on the effective voice data to obtain a first text; constructing a user feature model, and performing semantic understanding on the text based on the model to obtain an understanding result; performing intention recognition and slot filling based on the understanding result to obtain user intention and key information; generating a response strategy in combination with the user intention, the key information and the environment perception data; generating response voice according to the response strategy; and monitoring feedback information of the user to the response voice in real time, and updating the user feature model and the response strategy evaluation model based on feedback. According to the scheme, the voice can be recognized more accurately, and the safety and robustness of the system are enhanced.

The invention discloses a multi-agent-based automatic generation method, system and terminal for a micro-drama, and the method comprises the steps: obtaining a user creation theme, and carrying out the story line generation processing and the split processing to obtain a plurality of split scripts which comprise commentaries, visual information, shot information and atmosphere information; performing dubbing conversion processing and subtitle generation processing according to the commentary to obtain a target dubbing audio and a target video subtitle; performing key frame generation processing according to the visual information to obtain a target key frame image, and performing video generation processing with the shot information to obtain a target video clip; performing background music generation processing according to the atmosphere information to obtain target background music; and performing time axis alignment processing and splicing processing on the target dubbing audio, the target video subtitles, the target video clips and the target background music to obtain a target synthesized episode. According to the invention, the production efficiency and the output quality of the micro-movie can be effectively improved.

The invention discloses an automatic labeling and acquisition standardization method and system based on video content, and the method comprises the steps: decoding a video file into a processable video frame sequence, carrying out the standardization processing, unifying the format and the coding standard, and adjusting the resolution and the frame rate; performing content analysis on the standardized video frame sequence, extracting key frames, identifying objects, scenes and actions, and generating a video analysis result and a preliminary label; according to a video content analysis result, separating an audio stream from the video to carry out noise reduction processing and voice-to-text analysis, and combining the processed text with the video content analysis result to generate multi-modal comprehensive annotation information; based on the comprehensive labeling information, taking feedback of the user in video retrieval, label clicking and watching behaviors as an adjustment basis of label weights, and updating weight distribution of labels by analyzing user behavior data; labeling information and weight distribution are integrated into an index database, and indexes are updated in real time according to user behaviors and content changes.

The invention discloses a low-delay adaptive wireless audio transmission method, and relates to the technical field of audio data transmission. The invention relates to a low-delay self-adaptive wireless audio transmission method, which comprises the steps of audio signal acquisition and coding, priority scheduling and time slot allocation, wireless channel transmission and interference resistance, congestion control algorithm optimization, decoding and reconstruction of a receiving end, and dynamic buffer management and delay optimization. According to the method, low-delay transmission of audio signals is ensured through a plurality of technologies such as priority scheduling, time slot allocation, dynamic buffer management and adaptive coding, the stability of audio transmission is remarkably improved by combining congestion control, forward error correction (FEC) and adaptive interference elimination technologies, and even in a complex wireless environment, the stability of the audio transmission is greatly improved. And the audio signal can keep high quality, and the transmission stability is ensured by quickly responding to channel change, eliminating interference, correcting errors, reducing packet loss and retransmission.

A method for early diagnosis of Parkinson's disease based on multimodal deep learning is provided. Audio-visual data of a to-be-diagnosed subject while performing a speech task is acquired. The audio-visual data are preprocessed to extract a plurality of audio segments and a plurality of video segments. A face image sequence is extracted from each of the plurality of video segments. A Mel-spectrogram of each of the plurality of audio segments is calculated. The face image sequence and the Mel-spectrogram are input into a multimodal deep learning model to output a classification result for Parkinson's disease early diagnosis of the to-be-diagnosed subject. A system for early diagnosis of Parkinson's disease based on multimodal deep learning is also provided.

Systems and methods of the present disclosure include animating virtual avatars or agents according to input audio and one or more selected or determined emotions and / or styles. For example, a deep neural network can be trained to output motion or deformation information for a character that is representative of the character uttering speech contained in audio input. The character can have different facial components or regions (e.g., head, skin, eyes, tongue) modeled separately, such that the network can output motion or deformation information for each of these different facial components. During training, the network can use a transformer-based audio encoder with locked parameters to train an associated decoder using a weighted feature vector. The network output can be provided to a renderer to generate audio-driven facial animation that is emotion-accurate.

A computer implemented system for authenticating an audio conversation occurring between a human target and an unknown speaker. The system adjusts messages sent to the speaker to include characteristics that evoke different responses in bots controlling vocoders to generate deepfakes than are evoked in humans. By analyzing these responses, harms from deepfake threats can be avoided by turning the tools used by deepfake generating bots against those very bots.

The invention discloses a real-time interactive digital human system supporting high concurrency and an implementation method thereof, and relates to the technical field of digital human interaction.The system comprises a model instance pool module used for loading the weight of a deep learning model to a shared memory area through the memory mapping technology; the multi-thread scheduling module is used for scheduling user requests by adopting a lock-free queue and a dynamic priority algorithm; the asynchronous pipeline processing module is composed of decoupled micro-services, and the modules are connected in series through asynchronous message queues; the client SDK is used for dynamically switching a rendering mode according to terminal hardware performance and network conditions; the elastic capacity expansion and contraction module is used for monitoring resource loads in real time and automatically adjusting the number of service instances; and the audio and video synchronization calibration module is used for ensuring that the synchronization error of the audio and video frames is lower than a preset threshold value through a timestamp alignment algorithm. According to the scheme, core challenges in a high-concurrency digital human interaction scene can be systematically solved, and innovative support is provided for large-scale real-time application.

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

The invention relates to the technical field of audio and video analysis and data processing, in particular to an AI-based multimedia equipment operation and maintenance management system, which comprises a data acquisition module, an analysis module, an equipment state modeling module, a fault prediction module, a maintenance strategy optimization module, a resource allocation module and a visual platform. The data acquisition module acquires temperature, vibration, audio and video feature data of the equipment through the sensor and the video equipment, and the data are preprocessed and then transmitted to the cloud; the equipment state modeling module constructs a dynamic baseline model based on historical data, abnormal signals are generated through real-time comparison, and the visual platform realizes multi-dimensional state monitoring through 3D topology rendering, AR auxiliary diagnosis and early warning linkage. The system solves the problems of response lag and low resource utilization rate caused by dependence on manual operation and maintenance in the prior art through a closed-loop management mechanism driven by a data stream, and improves the fault diagnosis precision and the operation and maintenance efficiency.

The invention provides an intelligent security and protection data analysis and decision method and system based on artificial intelligence. The method comprises the following steps: firstly, collecting heterogeneous data streams such as video frame sequences, infrared sensing signals, audio waveforms and equipment state logs uploaded by a plurality of security and protection monitoring equipment in a target area; multi-modal feature extraction is carried out through the spatial-temporal feature fusion model, and spatial-temporal feature vectors containing equipment deployment coordinates and the like are generated; dynamically dividing equipment clusters based on a self-adaptive clustering algorithm, so that the spatial-temporal feature vector similarity of the equipment in the same cluster is high; secondly, for each cluster, analyzing abnormal signal relevance by using an abnormal propagation network model to obtain a cluster-level risk prediction result and an equipment collaborative response instruction; and finally, according to the risk prediction result, dynamically adjusting the node weight of the abnormal propagation network model and the clustering center of the adaptive clustering algorithm, optimizing subsequent cluster division, and improving the accuracy and intelligence of intelligent security data analysis decision.

Various implementations include audio devices and methods for spatializing audio output based on content. Certain implementations include at least one audio output device for providing an audio output based on data, and at least one controller coupled with the at least one audio output device, the controller configured to, use the data to determine a content type for the audio output from a group of content types, automatically select a spatialization mode for the audio output from a plurality of spatialization modes based on the determined content type, and apply the selected spatialization mode to the audio output.

A method for controlling an artificial intelligence (AI) device for implementing a digital avatar can include receiving an audio signal corresponding to a user query, converting, by a speech-to-text neural network model, the audio signal into a text query, inputting the text query into a large language gesture instruction model to generate high level movement instructions, inputting the text query and the high level movement instructions into an information retrieval model to generate a text response including at least one sentence and digital avatar control information, and inputting the text response into a text-to-speech neural network model to generate an audio response. Also, the method can include inputting the audio response into an audio-to-facial animation model and an audio-to-conversational gesture model to generate updated digital avatar control information including gesture information, and outputting the audio response and the updated digital avatar control information for controlling the digital avatar.

A game controller for a mobile device, the game controller including a first handle, a bridge, and an acoustic waveguide. The first handle is configured to contact and support a mobile device and includes a user-accessible, first hardware interface on a main body portion of the first handle that is configured to accept touch inputs. The bridge extends from the main body portion of the first handle. The acoustic waveguide is at a juncture of the first handle and the bridge. The acoustic waveguide is configured to redirect sound emitted from the mobile device toward at least one of a front side of the game controller, a top side of the game controller, and a bottom side of the game controller.

The invention relates to the technical field of audio noise reduction, and discloses a Bluetooth headset environment noise adaptive adjustment method and system, and the method comprises the steps: collecting multi-band sound data of headset environment noise through a preset acoustic sensor array, generating a noise spectrogram, extracting a noise masking threshold through time-frequency joint analysis, and collecting a headset audio signal; next, noise suppression is carried out on the audio signal by using a multi-order digital filter bank to obtain a primary noise reduction signal, the primary noise reduction signal is optimized into an optimized noise reduction signal based on a signal-to-noise ratio, and the frequency response matching degree of the optimized noise reduction signal and the original audio signal is calculated; then, performing phase correction and amplitude-frequency equalization processing according to the frequency response matching degree to obtain an audio equalization parameter; and finally, monitoring residual noise energy based on the parameter, generating a noise regulation and control instruction, driving adaptive sampling, and generating an adaptive noise reduction scheme. According to the invention, the audio noise reduction effect of the Bluetooth earphone can be improved.

The present disclosure relates to systems and methods for enhancing the interaction between users and automated agents, such as digital assistants, by employing Large Language Models (LLMs) to infer the intent of spoken language. The invention involves continuously monitoring ambient audio, converting speech to text, and utilizing LLMs to determine whether spoken language is intended for the automated agent. A structured prompt, including the converted text and specific instructions, is sent to the LLM, which is fine-tuned to process domain-specific prompts. The LLM provides a structured output in a standardized format, indicating the user's intent. The system may involve multiple prompts to perform separate tasks, such as identifying intent and generating additional context-specific data. This approach facilitates a more natural and intuitive user experience by eliminating the need for wake words and allowing seamless conversational interaction with virtual assistants across various platforms and devices.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology, cultural transmission and the like, and discloses a voice generation method based on multi-modal fusion, which comprises the following steps: collecting audio data to extract timbre features, and training a field feature timbre generation model; analyzing text semantic recognition emotion information, adjusting speech synthesis parameters, combining personalized information to construct a parameter mapping table, fusing to generate a synthesis control parameter sequence, aligning the synthesis control parameter sequence with character labels, visual elements and background music data, driving a domain feature timbre generation model, and generating synthesis data of synchronous speech, text, vision and music. Domain timbres are generated through timbre feature training, speech expression is optimized by combining semantic analysis and emotion recognition, user requirements are matched based on personalized information, and time alignment is performed by fusing text, vision and music data, so that the synthesized speech has domain features, emotion adaptability and personalization, and the speech quality is improved. And the voice immersion and the information transmission capability are improved.

The invention discloses an intelligent control method and system for tunnel loudspeakers, and relates to the technical field of tunnel audio control, environmental parameters in a tunnel are collected by adopting a mode of deploying sampling equipment in a distributed manner, and data preprocessing is performed in a targeted manner for different environmental parameters; a sound propagation model is established, and attenuation and delay of sound in different environments are simulated. According to the intelligent control method and system for the tunnel loudspeakers, various temperature and humidity sensors are arranged in the tunnel, and the absolute humidity is calculated in combination with the air pressure data, so that the sound velocity is accurately corrected, and the phase difference of the multiple loudspeakers is reduced; an adaptive Kalman filtering algorithm is adopted to process wind speed data, and reliable input is provided for a sound propagation model; a deep reinforcement learning algorithm is used to carry out collaborative optimization on parameters such as amplitudes and directional angles of multiple loudspeakers, a Bayesian network is used to detect loudspeaker faults, and Delaunay triangulation and a distributed consistency algorithm are combined to realize rapid fault reconstruction.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an audio signal authenticity verification method, device, equipment and medium, and the method comprises the steps: constructing an original audio text data set, and generating an adversarial sample set, inputting the original audio text data set and the adversarial sample set into an audio detection model for joint training to obtain an audio detection model subjected to adversarial training; the method comprises the steps of obtaining a to-be-detected audio signal and extracting an acoustic feature of the to-be-detected audio signal, obtaining a non-acoustic feature associated with the to-be-detected audio signal, constructing a multi-dimensional feature vector according to the acoustic feature and the non-acoustic feature, inputting the multi-dimensional feature vector into an audio detection model to generate an abnormal index, and executing a hierarchical response operation based on the abnormal index. According to the method, the robustness of the model is enhanced by introducing adversarial sample training, and the multi-dimensional feature vector is constructed by fusing the multi-modal features, so that accurate recognition and hierarchical response to the voice cloning attack are realized.

A loudspeaker device includes a speaker component, a first microphone element, and a second microphone element. The speaker component includes an earphone core and a core housing. The first microphone element is disposed on a first branch circuit board of a circuit board. The second microphone element is disposed on a second branch circuit board of the circuit board. The circuit board is configured to electrically connect to an audio signal wire, a first auxiliary signal wire, and a second auxiliary signal wire of an external control circuit. The audio signal wire, the first auxiliary signal wire, and the second auxiliary signal wire are electrically and respectively connected to the earphone core, the first microphone element, and the second microphone element through the circuit board. A board surface of the second branch circuit board is tilt with respect to a board surface of the first branch circuit board.

A system configured to improve audio processing by performing dereverberation and noise reduction during a communication session. In some examples, the system may include a deep neural network (DNN) configured to perform speech enhancement, which is located after an Acoustic Echo Cancellation (AEC) component. For example, the DNN may process isolated audio data output by the AEC component to jointly mitigate additive noise and reverberation. In other examples, the system may include a DNN configured to perform acoustic interference cancellation, which may jointly mitigate additive noise, reverberation, and residual echo, removing the need to perform residual echo suppression processing. The DNN is configured to process complex-valued spectrograms corresponding to the isolated audio data and / or estimated echo data generated by the AEC component.