In　order　to　improve　the　accuracy　of　bird　sounds　recognition　under　different　kinds　of　noise　environments　in　the　real　world,　a　new　bird　sounds　recognition　technology　based　on　the　APNCC　extraction　was　proposed.　First,　the　noise　estimation　algorithm　for　highly　non-stationary　environments　was　used　to　estimate　the　noise　power　spectrum　of　the　bird　sound　in　the　noise　environment.　Second,　the　multi-band　spectral　subtraction　was　presented　to　achieve　the　background　noise　reduction.　Then,　the　estimated　clean　bird　sound　spectrum　was　combined　with　the　process　of　the　PNCC　extraction　to　calculate　the　APNCC.　Finally,　the　comparison　experiments　of　34　bird　sounds　recognition　in　3　different　real　environments　under　different　SNRs　were　constructed,　based　on　the　combination　of　the　SVM　classifier　and　3　different　features,　namely　the　APNCC,　PNCC　and　MFCC.　The　experimental　results　show　that　the　APNCC　outperforms　other　features　in　the　average　recognition　rate　and　the　noise　robustness,　especially　for　the　conditions　of　all　SNRs　lower　than　30　dB.