The northern Andes in South America harbors one of the most diverse biological diversity on the planet. Yet, it is one of the most threatened regions as a result of habitat fragmentation, invasive species, agriculture and cattle grazing, and global climate change. It is therefore critical to implement robust conservation strategies and effective monitoring plans. In the region, biological monitoring relies on traditional methods such as direct observation and capture. These methods are expensive and require a large effort specially for rare species. As an alternative, automated passive bioacoustics allow to obtain large amounts of data both in time and space and in comparison, with traditional methods at low cost. The main challenge in passive monitoring is to handle and analyze these rivers of information in order to obtain meaningful results from acoustic data. We have implemented a passive bioacoustic monitoring since 2012 on the northern Andes in Colombia, a highly diverse region in the Neotropics. Our goals are two fold: first, we want to develop analytical strategies to process large amounts of sound files and second we are interested in answering biological questions from individuals to the landscape. As a result of this monitoring, we have developed a machine learning algorithm based on syllable recognition to automatically identify frog species (Ecol. Inf. 24: 200-209). We also have developed an algorithm to estimate the amount of rain from acoustic recordings (Ecol. Ind. 75:95-100). We have answered biological questions ranging from acoustic niche partitioning, interaction of traditional community indices with acoustic indices, and association of acoustic indices with landscape features. Now, we are using passive monitoring to fit complex occupation models and to determine assembling rules in anuran communities. In addition, we are assessing acoustic indices aiming to develop tools with more functions for soundscape analysis (Ecol. Inf. 45:16-25). We show how the continuous feedback between biologists and engineers will spike the implementation and analysis of passive monitoring in imperiled tropical hotspots.