The　paper　proposes　a　new　method　of　multi-band　signal　reconstruction　based　on　Orthogonal　Matching　Pursuit　(OMP),　which　aims　to　develop　a　robust　Ecological　Sounds　Recognition　(ESR)　system.　Firstly,　the　OMP　is　employed　to　sparsely　decompose　the　original　signal,　thus　the　high　correlation　components　are　retained　to　reconstruct　in　the　first　stage.　Then,　according　to　the　frequency　distribution　of　both　foreground　sound　and　background　noise,　the　signal　can　be　compensated　by　the　residual　components　in　the　second　stage.　Via　the　two-stage　reconstruction,　high　non-stationary　noises　are　effectively　reduced,　and　the　reconstruction　precision　of　foreground　sound　is　improved.　At　recognition　stage,　we　employ　deep　belief　networks　to　model　the　composite　feature　sets　extracted　from　reconstructed　signal.　The　experimental　results　show　that　the　proposed　approach　achieved　superior　recognition　performance　on　60　classes　of　ecological　sounds　in　different　environments　under　different　Signal-to-Noise　Ratio　(SNR),　compared　with　the　existing　method.　©　2014　Science　Press,　Institute　of　Electronics,　CAS　and　Springer-Verlag　Berlin　Heidelberg.