Cavitation erosion is a concern for most hydraulic machinery. An especially damaging type of cavitation is cloud cavitation. This type of cavitation is characterized by a growth-collapse cycle in which a group of vapor bubbles first grows together in a low-pressure region and then collapses almost simultaneously when the pressure recovers. Measuring the frequency of these collapse events is possible by acoustic emission (AE), as demonstrated in this study, in which a cavitation tunnel is utilized to create cloud cavitation in the vicinity of a sample surface. These samples were equipped with AE sensors, and the initially high frequency AE signal was demodulated to detect the relatively low frequency cloud cavitation shedding. It was found that when the cavitation number is increased, AE successfully detects the changes in this frequency, confirmed by comparing the results to video analysis and to simulations from literature. Additionally, the frequency increases when cavitation erosion progresses, thus providing means to track the erosion stage. It is concluded that the presented method is suitable for both detecting the transition from cloud to sheet cavitation and the erosion evolution in the experimental cavitation tunnel. The method could probably be extended to non-intrusive hydraulic machine monitoring, as this type of cloud cavitation is common in hydrofoils. (C) 2019 Elsevier Ltd. All rights reserved.