6 results about "Sound classification" patented technology

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.