This　paper　deals　with　acoustic　event　detection　(AED),　such　as　screams,　gunshots,　and　explosions,　in　noisy　environments.　The　main　aim　is　to　improve　the　detection　performance　under　adverse　conditions　with　a　very　low　signal-to-noise　ratio　(SNR).　A　novel　filtering　method　combined　with　an　energy　detector　is　presented.　The　wavelet　packet　transform　(WPT)　is　first　used　for　time-frequency　representation　of　the　acoustic　signals.　The　proposed　filter　in　the　wavelet　packet　domain　then　uses　a　priori　knowledge　of　the　target　event　and　an　estimate　of　noise　features　to　selectively　suppress　the　background　noise.　It　is　in　fact　a　content-aware　band-pass　filter　which　can　automatically　pass　the　frequency　bands　that　are　more　significant　in　the　target　than　in　the　noise.　Theoretical　analysis　shows　that　the　proposed　filtering　method　is　capable　of　enhancing　the　target　content　while　suppressing　the　background　noise　for　signals　with　a　low　SNR.　A　condition　to　increase　the　probability　of　correct　detection　is　also　obtained.　Experiments　have　been　carried　out　on　a　large　dataset　of　acoustic　events　that　are　contaminated　by　different　types　of　environmental　noise　and　white　noise　with　varying　SNRs.　Results　show　that　the　proposed　method　is　more　robust　and　better　adapted　to　noise　than　ordinary　energy　detectors,　and　it　can　work　even　with　an　SNR　as　low　as　-15　dB.　A　practical　system　for　real　time　processing　and　multi-target　detection　is　also　proposed　in　this　work.