The　acoustic　detection　of　pipeline　leaks　is　strongly　influenced　by　the　associated　interference　and　noise.　In　this　paper,　acoustic　emission　signals　are　analyzed　using　a　sparse　representation　method　and　the　main　components　associated　with　the　leakage　are　extracted.　Dictionary　learning　is　performed　using　training　samples　composed　of　the　leakage　signals　and　noise　signals.　The　measured　signal　is　sparsely　decomposed　on　the　composite　dictionary,　allowing　the　main　leakage　components　to　be　estimated.　The　effects　of　dictionary　dimensionality,　redundancy,　sparsity　constraints,　and　other　parameters　on　the　performance　of　the　sparse　representation　algorithm　are　investigated.　Finally,　the　leak　location　in　the　pipeline　is　determined　through　cross-correlation　analysis　of　the　reconstructed　acoustic　signals.　Experimental　results　show　that　the　proposed　sparse　representation　method　effectively　improves　the　signal-to-noise　ratio　of　the　acoustic　emission　signals,　and　correspondingly　improves　the　accuracy　and　reliability　of　pipeline　leak　location　compared　with　traditional　localization　methods.