2 results about "Noise masking" patented technology

The application discloses a kind of underwater glider acoustic data processing method and storage medium based on Conformer, belong to underwater acoustic signal processing technical field.The method includes: using MCR-AAD model to the original acoustic data collected by glider Effective signal detection, output the probability of each frame as effective signal, and using double threshold post-processing method to determine effective signal and noise signal;The detected effective signal is input into the causal BGformer noise reduction model, generates and reverses noise mask by audio feature fusion, DCC encoder, Conformer decoder and label embedding module, to obtain the audio result after noise reduction.MCR-AAD model is composed of mel feature extractor, multi-scale depth separable convolution, dynamic position coding, Conformer encoder and multi-task classification module.The application constructs efficient, real-time underwater acoustic data processing scheme, effectively improves the application performance of glider in marine observation and target identification.

This invention discloses a method and system for denoising magnetotelluric signals based on a self-supervised diffusion model, belonging to the field of magnetotelluric technology. The invention designs the TimeDART diffusion model as a two-stage architecture for constructing a noise classifier model and a diffusion denoising model. The initial input magnetotelluric data first passes through a noise classifier to generate a noise mask to identify target noise regions. Subsequently, the diffusion denoising model based on the diffusion model processes only these noise regions, generating denoised signal segments. Finally, the denoised noise regions are merged with the clean regions of the initial magnetotelluric data. By combining time-frequency analysis and VMD techniques to extract low-frequency trends from the magnetotelluric data, precise noise region localization, differentiated denoising, and smooth fusion can be achieved. While efficiently suppressing noise, it retains the effective signal characteristics to the greatest extent, improving the automation level of noise suppression, signal-to-noise identification accuracy, and denoising accuracy.