On classification of environmental acoustic data using crowds

In this work, we use crowds for acoustic classification of animal species in supervised and unsupervised manners. We demonstrate the effectiveness of the proposed triplet based crowdsourcing systems via actual experiments. Moreover, we propose a generalized 1-bit RPCA algorithm to further improve classification performance. The unique marriage of crowdsourcing and generalized 1-bit RPCA algorithm is shown to yield excellent performance for acoustic data classification.