This　paper　proposes　a　new　robust　environmental　sounds　recognition　technology　based　on　APNCC　to　improve　the　accuracy　of　environmental　sounds　recognition　in　real　noisy　conditions.　First,　a　highly　non-stationary　noise　estimation　algorithm　is　applied　for　the　noise　power　spectrum　estimation.　Second,　to　achieve　noise　reduction　with　less　residual　colored　noise,　we　present　a　multi-band　spectral　subtraction.　Then,　the　process　of　PNCC　extraction　is　combined　with　the　estimated　clean　environmental　sounds　to　extract　APNCC.　Finally,　using　70　subclasses　of　4　classes　of　clean　environmental　sounds,　the　comparison　experiments　in　different　environments　under　different　SNRs　are　constructed　based　on　the　combination　of　SVM　classifier　and　different　features,　namely　APNCC,　PNCC　and　MFCC.　The　experimental　results　show　that　APNCC　outperforms　other　features　in　average　recognition　accuracy　and　noise　robustness,　especially　for　conditions　of　SNRs　lower　than　30dB.　©　2012　IEEE.