In　this　paper,　we　propose　an　animal　sound　recognition　method　in　various　noise　environments　with　different　Signal-to-Noise　Ratios　(SNRs).　In　real　world,　the　ability　to　automatically　recognize　a　wide　range　of　animal　sounds　can　analyze　the　habits　and　distributions　of　animals,　which　makes　it　possible　to　effectively　monitor　and　protect　them.　However,　due　to　the　existence　of　different　environments　and　noises,　the　existing　method　is　difficult　to　ensure　the　recognition　accuracy　of　animal　sound　in　low　SNR　condition.　To　address　this　problem,　this　paper　proposes　double　feature,　which　consists　of　projection　feature　and　local　binary　pattern　variance　(LBPV)　feature,　combined　with　random　forests　for　animal　sound　recognition.　In　feature　extraction,　an　operation　of　projecting　is　made　on　spectrogram　to　generate　the　projection　feature.　Meanwhile,　LPBV　feature　is　generated　by　means　of　accumulating　the　corresponding　variances　of　all　pixels　for　every　uniform　local　binary　pattern　(ULBP)　in　the　spectrogram.　As　the　experimental　results　show,　the　proposed　method　can　recognize　a　wide　range　of　animal　sounds　and　still　remains　a　recognition　rate　over　80%　even　under　10dB　SNR.