B2P2: A scalable big bioacoustic processing platform

Many application areas such as biodiversity monitoring and speech recognition produce several gigabytes of audio data per day; think of weeks, months of accumulated data available to analyse. However, current methods and frameworks can only process a few megabytes at a time. Moreover, these methods require a lot of manual processing before they can give relevant results for biodiversity researchers. In this paper, we present a novel scalable framework called B2P2 for handling large audio files and processing them in an efficient manner. The proposed framework can process and handle large audio data that can indicate whether there are sounds of potential interest throughout large audio recordings. We implemented the proposed framework using BigData platforms such as Spark and HDFS. Experimental results demonstrate an increase in speed-up and performance of processing audio files when increasing nodes and data sizes on cloud computing.