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A system for biodiversity conservation and classification: forest observation stations stream video and audio to the cloud, where machine learning classifies each biological unit.
Environment · AI biodiversity monitoringListening to the forest.
The goal of this project was to develop a system for biodiversity conservation and classification. Observation stations are positioned in dense forest regions in order to monitor and classify the biodiversity.
Video and audio footage are streamed to the cloud database. Analytics and statistics are performed and reports are extracted.
From forest to report Observation stations in dense forest regions Video and audio streamed to the cloud Analytics, statistics and reports ML classification For each individual biounitAccurate metrics for a whole region.
The challenge of the project was a high reliability system that provides accurate metrics and statistics for the region’s biodiversity. Machine learning models analyzed the data streams and provided classification for each individual biounit.
01High reliability
02Accurate metrics and statistics
03Classification for each individual biounit
Processing and analysis of audio streams.
The client assigned Generic Soft with the processing and analysis of audio streams and the machine learning pipeline.
Data processing pipeline design
Adaptive filtration
Spectral analysis
Wavelet decompositions and filter banks
