In　an　ecological　breeding　environment,　feeders　usually　don＇t　know　whether　their　chicken　are　in　good　condition.　Fortunately,　Chicken　voices　reveal　a　lot　of　messages　and　it　is　easy　to　access　and　process　voice　signals.　Therefore,　we　propose　a　new-type　chicken　voice　recognition　method　using　sparse　representation　aiming　at　the　chicken　voice　recognition　problem　in　an　ecological　breeding　environment.　In　future,　the　method　can　be　used　in　chickens　for　automated　disease　detection　during　the　period　of　the　bird　flu.　First,　we　use　a　multi-band　spectral　subtraction　method　for　de-noise　processing.　Second,　we　reconstruct　voice　signals　via　sparse　representation　using　the　orthogonal　matching　pursuit　algorithm.　Third,　we　extract　Mel-frequency　cepstral　coefficients　(MFCC),　linear　predictive　coding　(LPC)　and　power-normalized　cepstral　coefficients　(PNCC)　from　the　chicken　voices.　Finally,　we　use　support　vector　machine　(SVM)　to　classify　chicken　voices　under　different　environments.　Extensive　experimental　results　show　that　the　features　using　sparse　representation　can　respectively　get　better　recognition　effects.　©　2015　IEEE.