Extraction of MapReduce-based features from spectrograms for audio-based surveillance

In this paper, we proposed a novel parallel method for extraction of significant information from spectrograms using MapReduce programming model for the audio-based surveillance system, which effectively recognizes critical acoustic events in the surrounding environment. Extraction of reliable information as features from spectrograms of big noisy audio event dataset demands high computational time. Parallelizing the feature extraction using MapReduce programming model on Hadoop improves the efficiency of the overall system. The acoustic events with real-time background noise from Mivia lab audio event data set are used for surveillance applications. The proposed approach is time efficient and achieves high performance of recognizing critical acoustic events with the average recognition rate of 96.5% in different noisy conditions.