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Sonar Noise Classifier

Bring the Noise

The Sonar Noise Classifier automatically identifies noise resulting in significantly less effort for the data processor, allowing more energy to be focused on higher-value aspects of the production chain. The Sonar noise classifier was trained with numerous manually classified datasets in different geography and sensor scenarios so that it can understand typical noise patterns and real features. This allows the data processor to spend their time reviewing results rather then picking out individual clusters of noise.




Core Features

Efficiency

The Sonar Noise Classifier automatically identifies noise providing significant reductions in manual cleaning and quickly propels data from acquisition to review.

Seamless Workflow

Accelerate data processing by seamlessly integrating CARIS MIRA AI Sonar Noise Classifier in your HIPS or Onboard workflow.

Security

Our AWS-certified team is focused on the safety and security of customer data: data directed to CARIS Mira AI platform is anonymized, randomized and encrypted before transmission. No data remains stored on the cloud once its is processed.

How it works

The Sonar Noise Classifier is a trained Convolutional Neural Network optimized to identify noise in 3D point clouds generated by acoustic sensor platforms. All data goes through a pre- and post-processing stage to form the data into something the AI will understand: the data is rasterized into a high-resolution 3D voxel grid (zero-origin) before being transmitted to the Deep Learning Model for inference. Besides re-sampling the data into a format the AI can understand, this also has a side-benefit of completely anonymizing the data, as all spatial information is stripped during the voxelization process before transmission. When the classified voxels are returned from the AI, we post-process the data and map the results back to individual points to continue processing. The automation results in significantly less effort for the human processor allowing more energy to be focused on other, higher-value aspects of the production chain and, ultimately, increases the availability of data.



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