The combination of bioacoustic monitoring with machine learning algorithms is increasingly used to gain information on species distribution or activity. Among these methods, convolutional neural networks (CNN) for the automatic classification of both environmental and animal sounds proved particularly effective. We employed an automated classifier based on a CNN aimed at detecting the presence of Indri indri songs recorded in Maromizaha Forest from 2019 to 2021 via passive acoustic monitoring. The network achieved high accuracy (>90%) and recall (>80%) values in assessing the songs presence while the use of data augmentation and transfer learning was able to generalize to unsampled periods. Lastly, our process was able to correctly describe both daily and annual pattern of indris’ singing behavior, critical piece of information to plan data collection and conservation practices.