12 results about "Sound classification" patented technology

An anti-snoring system comprising an adjustable bed having a sleeping surface that may be mechanically raised or lowered, and a control module adapted to receive commands from a source external to the adjustable bed; a mobile device in direct or indirect communication with the control module in the adjustable bed, and wherein the mobile device has sound recording capabilities; a mobile application resident on the mobile device, wherein the mobile application includes a sound classification machine learning model that includes an artificial intelligence or neural network operative to determine whether or not a person on the sleep surface is snoring, and wherein upon a determination that the person is snoring and has been snoring for a predetermined period of time, the mobile application instructs the control module to raise or adjust the sleeping surface to a height or position that will discourage the person from snoring.

The application relates to the technical field of machine learning, and provides an animal sound classification method, system, device, medium and product. The application trains a classification model through a staged strategy, including pre-training the classification model based on a general audio dataset, end-to-end training the classification model based on an animal sound dataset, and deploying the trained classification model to an intelligent device; the intelligent device collects original audio signals in real time and extracts log-mel spectrum features as feature inputs of the classification model, and the model finally outputs prediction probabilities for each target sound category, so as to determine an animal sound classification result. The staged training method significantly reduces the model training difficulty, the log-mel spectrum features are used as inputs to improve the robustness of the model to background noise, and the generalization ability and overall accuracy of the classification model are effectively improved.

The application discloses a bird chirp sound recognition method based on a combination of voiceprints and spatial distribution, and belongs to the technical field of intelligent sound classification and recognition. In view of the problems of ignoring geographical distribution prior knowledge and sample imbalance in the prior art, the application firstly constructs a voiceprint recognition model: a training data set is constructed by audio preprocessing, logarithmic mel spectrum and dynamic difference feature extraction, a model is trained based on DenseNet-121 by adopting a two-stage training strategy, and recognition confidence of each species is obtained; meanwhile, a spatial distribution model is constructed: based on public observation data, an average observer ability index is used to correct an original encounter rate, and spatial distribution probability of the species in a specific city is obtained; finally, a Sigmoid function is used to perform nonlinear fusion on the two, a joint recognition probability is calculated, and a classification result is output. The application introduces ecological spatial constraints into the recognition decision, effectively reduces false positive misjudgment, improves rare species monitoring capability, and makes the recognition result have ecological interpretability.

This application discloses a method for determining model parameters, a method for classifying vehicle abnormal noises, an apparatus, and a device. The method includes: acquiring a training dataset of vehicle abnormal noise features; generating an initial population based on the training dataset; the population containing multiple individuals; constructing a kernel principal component analysis (KPC) model based on preset model parameters; the KPC model is used to extract target abnormal noise feature vectors from the vehicle's abnormal noise features for vehicle abnormal noise classification; determining an objective function based on the KPC model; iteratively updating the initial population based on the objective function to obtain an updated population; determining the optimal individual from the updated population; and determining the target model parameters of the KPC model based on the optimal individual and a preset parameter range. This method can determine the target model parameters of the KPC model most suitable for the current abnormal noise environment, thereby improving the classification accuracy of vehicle abnormal noise classification.

The application relates to a snoring sound identification intervention system and method, wherein the snoring sound identification intervention system comprises a data acquisition module, a snoring sound identification module and a snoring sound intervention module; the data acquisition module is used for acquiring heart impact snoring sound vibration data of a user; the snoring sound identification module is used for generating a corresponding snoring sound feature vector based on the heart impact snoring sound vibration data, inputting the snoring sound feature vector into a trained snoring sound classification model, and outputting a snoring sound judgment result and a snoring sound intensity level; the snoring sound intervention module is used for generating a corresponding intervention scheme based on the snoring sound intensity level when the snoring sound judgment result is that snoring sound is identified, and adjusting an intelligent pillow used by the user based on the intervention scheme. Through the application, the problem of high snoring sound misjudgment rate is solved.

The present utility model relates to a classroom noise monitoring device that detects ambient sound levels and provides immediate visual feedback to regulate classroom behavior. The device comprises a sound detection module configured to capture noise, a microcontroller board programmed to classify the detected sound into predefined threshold ranges, and a visual output module that displays indicators corresponding to acceptable, moderately high, and excessive noise levels. In one embodiment, the visual output module employs colored light indicators, while in another embodiment it utilizes a graphic display presenting emoticon icons. An optional wireless communication module may transmit noise data to a remote server or mobile device for monitoring and record-keeping. The device is enclosed in a wall-mountable or desktop casing and powered by a standard low-voltage supply. By providing real-time, intuitive feedback, the utility model offers an affordable and effective tool for promoting discipline and self-awareness in educational settings.

The application relates to the technical field of artificial intelligence, and provides a method, device and equipment for jointly detecting a water pipe leakage point based on vision and sound, to solve the problem that there is no leakage anomaly detection method with high accuracy and good universality in related technologies. First, an image sample of a water pipe is taken as input of a vision network model to obtain a positioning result and a positioning confidence for representing a leakage point position in the image sample, and a vision classification result and a vision classification confidence for representing a leakage point category in the image sample; then, a sound sample of the water pipe is taken as input of a sound network model to obtain a sound classification result and a sound classification confidence for representing a sound sample category; the position of the leakage point is comprehensively obtained according to the positioning confidence and the sound classification confidence, and then the category information of the leakage point is comprehensively obtained according to the vision classification confidence and the sound classification confidence, so that the position and type of the leakage point are finally obtained.

The application discloses a bird chirp sound classification and recognition method and device, comprising the following steps: acquiring bird chirp sound audio data; pre-processing the bird chirp sound audio data to obtain pre-processed audio data; performing Fourier transform on the pre-processed audio data to obtain a spectrogram of the bird chirp sound; obtaining an MFCC hybrid feature vector of the pre-processed audio data based on a mel-frequency cepstrum coefficient and a difference operation; processing the spectrogram by using a CNN network to obtain local fine-grained spectral features after training; processing the MFCC hybrid feature vector by using a Transformer encoder network to obtain global sequence features considering context after training; and obtaining a recognition classification result of the bird chirp sound by using a Softmax classifier after splicing and fusing the local fine-grained spectral features and the global sequence features. The application can improve the bird sound classification and recognition accuracy.

A computer implemented method for analysing sounds, such as audio tracks, and automatically classifying the sounds in a space in which arousal is one axis and valence is another axis. The location of a sound or track in that arousal-valence space is automatically determined using a computer implemented system that analyses, measures or infers values for each of the following base feature parameters: harmonicity, turbulence, rhythmicity, sharpness, volume and linear harmonic cost, or any combination of two or more of those parameters.

The invention relates to the technical field of deep learning, in particular to a model training method, an alarm sound classification method and alarm sound classification equipment. The model training method comprises the steps that an alarm sound classification data set is determined, and data in the alarm sound classification data set comprises multiple pieces of audio data and equipment information labels and alarm sound category labels corresponding to the audio data; training a teacher model based on the alarm sound classification data set to obtain a classification soft label corresponding to the audio data; and training a target model based on the alarm sound classification data set and the classification soft label, the parameter quantity of the target model being smaller than that of the teacher model. According to the method and the device, the equipment information label corresponding to the audio data is introduced into the model training, so that the alarm sound classification accuracy and generalization ability of the model in a cross-equipment scene can be effectively improved, and the alarm sound discrimination ability and robustness of the audio are effectively improved. The classification performance of the target model can be guaranteed, and meanwhile model lightweight is achieved.

The invention discloses an AI accompanying housekeeper system and method special for a long person based on an AI large model and a voiceprint recognition model. The method comprises the following steps: acquiring audio data from hardware equipment, and distinguishing environment sound and human voice through a sound classification model; if the sound is environmental sound, matching an early warning tag library and triggering early warning; and if the voice is human voice, accurately identifying a user through a voiceprint model, analyzing the voice into characters and inputting the characters into a large language model. And the big language model interacts with the intelligent agent according to the intention, and combines with the personality big model to analyze the thread to construct a dynamic personality portrait. And based on the personality portrait and the historical interaction record, generating a personalized response text and automatically continuing the topic, and finally converting the topic into audio through a speech synthesis model and outputting the audio. The system comprises an environment sensor module, a voiceprint recognition module, an AI large model processing module, a user portrait and history interaction module, a response execution module and the like. According to the invention, active safety monitoring and personalized anthropomorphic accompanying of the living environment of the elder are realized, and the safety and emotion requirements of the living environment are improved.

The present disclosure relates to the field of computer technology and audio signal processing, and provides a brain-like low-power audio classification method and device suitable for edge devices, equipment, medium and product, which comprises: obtaining a target audio; using an audio decomposition module of an audio classification model to decompose the target audio to obtain a first sub-band and a second sub-band; using a feature extraction module of the audio classification model to extract features from the first sub-band and the second sub-band to obtain first features corresponding to the first sub-band and second features corresponding to the second sub-band; and using a classification module of the audio classification model to predict the sound classification of the target audio based on the first features and the second features. The present disclosure can solve the problem of redundant calculation caused by homogenization processing of audio, avoid the problem of being unable to distinguish high-frequency information and low-frequency information, and perform targeted feature extraction on different frequency components, thereby avoiding redundant calculation and improving the calculation efficiency of audio classification.