A Machine Hearing Framework for Real-Time Streaming Analytics Using Lambda Architecture

Disruptions to the earth’s biosphere and to the natural environment stemming from the indiscreet human activity, have caused serious environmental problems which are tantamount to an extended and prolonged ecological crisis. Climate change is clearly reflected in the increase of the global average air and ocean temperatures, in the excessive melting of snow-ice, and in the rise of the global average sea level. One of the most serious impacts of climate change is the complex interaction of species in relation to their corresponding climatic survival factors, which favors the spread of invasive species (INSP). These species constitute a very serious and rapidly deteriorating threat to the natural biodiversity of the native environment, but also to the flora, fauna, and even to the local human population. This research proposes a Machine Hearing (MH) framework for real-time streaming analytics, employing Lambda Architecture (LARC). The hybrid modeling effort is based on timely and advanced Computational Intelligence (COIN) approaches. The Framework for Lambda Architecture Machine Hearing (FLAME_H) uses a combination of batch and streaming data. The FLAME_H applies the EL_GROSEMMARI (Extreme Learning Graph Regularized Online Sequential Multilayer Multiencoder Algorithm) to classify the batch data and the Adaptive Random Forest (ARF) in order to control the data streams in real time. The aim of the proposed framework is the intelligent identification and classification of invasive alien species, based on the sounds they produce. This would contribute to the protection of biodiversity and biosecurity in a certain area.