Water losses due to pipeline breakages are a global issue. Timely detection of these events would help mitigate the waste of this natural resource. Various approaches have been employed to address this problem, ranging from classical Machine Learning algorithms and Digital Signal Processing to Deep Learning architectures. This article presents a Convolutional Neural Network for classifying pressure signals identified as background noise or presence of a burst. The model’s architecture comprises an initial normalization layer, four convolutional layers followed by max pooling layers with ReLU activation functions, a global pooling layer, and a final output neuron with a sigmoid activation function. With a lightweight design intended for implementation on low-power microcontrollers and an end-to-end architecture, it achieves 100% accuracy in identifying bursts over background noise in signals collected under controlled conditions.