This study aims to recognise frog choruses using false-colour spectrograms and machine learning algorithms with acoustic indices. This can be a useful solution for improving the efficiency of long-term acoustic monitoring. Acid frogs, our target species, are a group of endemic frogs that are particularly sensitive to habitat change and competition from other species. The Wallum Sedgefrog (Litoria olongburensis) is the most threatened acid frog species facing habitat loss and degradation across much of their distribution, in addition to further pressures associated with anecdotally-recognised competition from their sibling species, the Eastern Sedgefrogs (Litoria fallax). Monitoring the calling behaviours of these two species is essential for informing L. olongburensis management and protection, and for obtaining ecological information about the process and implications of their competition. Considering the cryptic nature of L. olongburensis and the sensitivity of their habitat to human disturbance, passive acoustic monitoring is a suitable method for monitoring this species. However, manually processing this overwhelmingly large quantities of acoustic data collected is time-consuming and not feasible in the long-term. Therefore, there is a high demand for automated acoustic recognition methods to efficiently search long-duration recordings and identify target species. In this study, we propose a two-step scheme for quickly identifying frog choruses, which is first narrowing down the search scope by inspecting long-duration false-colour spectrograms and then recognising target acoustic signals using machine learning and acoustic indices. This method is efficient, time-saving and general, which means it can easily adopted to other species. Our research also provides insights on how to choose acoustic features that efficiently recognise species from larger scale field-collected recordings. The experimental results show that these techniques are useful in identifying choruses of the two competitive frog species with an accuracy of 76.7% on identifying four acoustic patterns (whether the two species occurred).