Due　to　existence　of　different　environments　and　noises,　the　existing　method　is　difficult　to　ensure　the　recognition　accuracy　of　animal　sound　in　low　Signal-to-noise　(SNR)　conditions.　To　address　these　problems,　we　propose　a　double　feature,　which　consists　of　projection　feature　and　Local　binary　pattern　variance　(LBPV)　feature,　combined　with　Random　forest　(RF)　for　animal　sound　recognition.　In　feature　extraction,　an　operation　of　projecting　is　made　on　spectrogram　to　generate　the　projection　feature.　Meanwhile,　LBPV　feature　is　generated　by　means　of　accumulating　the　corresponding　variances　of　all　pixels　for　every　Uniform　local　binary　pattern　(ULBP)　in　the　spectrogram.　Short-time　spectral　estimation　algorithm　is　used　to　enhance　sound　signals　in　severe　mismatched　noise　conditions.　In　the　experiments,　we　classify　40　kinds　of　common　animal　sounds　under　different　SNRs　with　rain　noise,　traffic　noise,　and　wind　noise.　As　the　experimental　results　show,　the　proposed　framework　consisting　of　short-time　spectrum　estimation,　double　feature,　and　RF,　can　recognize　a　wide　range　of　animal　sounds　and　still　remains　a　recognition　rate　over　80%　even　under　0dB　SNR.