The Ninth Annual Machine Learning for Signal Processing (MLSP) Data Competition Committee has hosted a bird classification challenge at Kaggle.com (http://www.kaggle.com/c/mlsp-2013-birds). For this year's competition, participants were asked to develop classification algorithms to reliably identify the set of bird species in real-world audio data collected in an acoustic monitoring scenario. In this paper, we (the organizers of the competition) briefly describe the application, the data, the rules, and the outcomes of the competition. An MLSP record number of 79 teams entered the contest. We provided training data to the participants. The entries were tested using disjoint test data. The participants had access to the test data, but not the test labels. A separate multi-author long paper to summarize the new methods, which were contributed by multiple teams, will be included in this year's conference proceedings. The two top ranking teams described their approaches in two separate companion papers, all of which will appear in this year's conference proceedings. The first place team, whose entry produced an area under the receiver operating curve (ROC) of 0.956, is Gabor Fodor from Budapest University of Technology and Economics in Budapest, Hungary. The second place team, whose entry produced an area under the ROC of 0.951, consists of Hong Wei Ng and Thi Ngoc Tho Nguyen from Advanced Digital Sciences Center, University of Illinois at Urbana-Champaign, Singapore, Singapore.