and RPCA (robust principal component analysis) based feature selection for monitoring of natural fish sounds produced in situ by the plainfin midshipman (Porichthys notatus); here, we investigate this fish's grunts, growls and groans. Both local and contextual information are exploited by MRAF, while sparse components of theMRAF matrix obtained through RPCA is found to bemore robust to overlapping low-frequency spectral contentsamong different classes. The simulation results obtained fromreal-recorded ocean data reveal the advantages of the proposed scheme for monitoring underwater soundscapes and determining a variety of fish sounds in natural marine habitats.