Two sound classifiers were proposed for a novel aquaculture application that involved processing sound to estimate the feed consumption of prawns within the turbid waters of farm ponds. A two stage content classifier inferred feed events using identified sound features. To deal with the class ambiguity created by the acoustically challenging conditions of ponds, the CADBN was proposed to jointly model the sound features with the context of feed events. The CADBN was then reformulated to classify the energy load of devices using a distributed state space that enabled flexible and efficient modelling of context. The CADBN was compared to a set of benchmark classifiers for both the prawn feeding and energy applications. Results indicate that the inclusion of context greatly enhances class discrimination in both problems. Furthermore, results illustrate that the temporal structure of the CADBN produced superior performance to benchmark context classifiers that adopt the same context features as independent inputs.