Eco-environmental　sounds　depict　the　sound　content　of　varieties　of　creatures＇　survival　and　activities　in　the　ecological　environment　at　a　time　interval.　Research　on　eco-environmental　sounds　is　useful　in　monitoring　of　the　wildlife　and　their　evolution　with　time.　Due　to　varieties　of　noises　in　the　ecological　environment,　we　consider　the　task　of　eco-environmental　sounds　classification　under　noise　conditions.　Time-frequency　representations　have　the　potential　to　be　powerful　features　for　nonstationary　signals.　Especially,　time-frequency　domain　features　can　classify　sounds　with　noise　where　using　frequency-domain　features　(e.g.,　MFCCs)　fail.　Hence,　a　classification　approach　using　time-frequency　features　for　eco-environmental　sounds　under　noise　conditions　is　presented　in　this　paper.　Matching　pursuit　(MP)　algorithm　is　proposed　to　extract　time-frequency　features　(MP-based　features,　for　short)　of　effective　signals.　Besides　statistical　features　extracted　under　Choi-Williams　distribution　(CWD-based　features,　for　short)　also　perform　more　effectively　than　other　conventional　audio　features　under　noise　conditions.　Considering　the　effectiveness　of　features　and　robustness　of　classifier,　a　classification　model　using　time-frequency　features　(the　combination　features　of　MP-based　features　and　CWD-based　features)　and　support　vector　machine　(MP+CWD-SVM　for　short)　is　proposed.　Experimentally,　CWD+MP-SVM　is　able　to　achieve　a　higher　classification　rate　for　eco-environmental　sounds　under　noise　conditions.　The　result　shows　that　time-frequency　features　and　SVM　classifier　have　better　noise　immunity.　©　2013　IEEE.