Environmental　sounds　depict　the　sound　content　of　varieties　of　creatures＇　survival　and　activities,　and　also　closely　related　with　the　human　living　environment.　Current　conventional　approaches　for　recognition　of　environmental　sounds　required　important　computational　resources　and　employing　complex　signal　processing　methods　in　the　frequency　domain.　This　work　proposes　a　low-complexity　method　named　Time　Encoded　Signal　Processing　and　Recognition　(TESPAR　for　short).　The　computational　requirements　for　this　method　are　two　orders　of　magnitude　less　than　that　required　by　other　usual　methods.　We　used　the　TESPAR　coding　method　to　produce　simple　data　structures,　and　then　used　the　archetypes　technique　for　classification.　Our　method　was　tested　on　two　databases,　database　1　consisted　of　10　classes　of　bird　sounds　to　test　the　interspecific　recognition,　database　2　consisted　of　10　classes　of　different　environmental　sounds　to　test　intraspecific　recognition.　We　also　did　the　experiments　on　the　same　databases　using　MFCC　and　SVM　to　make　a　comparison.　Results　showed　that　TESPAR　has　lower　training　time　complexity　than　SVM,　and　the　recognition　rate　of　intraspecific　recognition　was　better　than　interspecific　recognition.　©　2012　IEEE.