  In the big data era of observational oceanography, passive acoustics datasets
are becoming too high volume to be processed on local computers due to their
processor and memory limitations. As a result there is a current need for our
community to turn to cloud-based distributed computing. We present a scalable
computing system for FFT (Fast Fourier Transform)-based features (e.g., Power
Spectral Density) based on the Apache distributed frameworks Hadoop and Spark.
These features are at the core of many different types of acoustic analysis
where the need of processing data at scale with speed is evident, e.g. serving
as long-term averaged learning representations of soundscapes to identify
periods of acoustic interest. In addition to provide a complete description of
our system implementation, we also performed a computational benchmark
comparing our system to three other Scala-only, Matlab and Python based systems
in standalone executions, and evaluated its scalability using the speed up
metric. Our current results are very promising in terms of computational
performance, as we show that our proposed Hadoop/Spark system performs
reasonably well on a single node setup comparatively to state-of-the-art
processing tools used by the PAM community, and that it could also fully
leverage more intensive cluster resources with a almost-linear scalability
behaviour above a certain dataset volume.
