{'fraction_non_alphanumeric': 0.018456375838926176, 'fraction_numerical': 0.0016778523489932886, 'mean_word_length': 5.650557620817844, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Recognition of Frog Chorusing with Acoustic Indices and Machine Learning

This research explores the recognition of choruses of two co-existing sibling frog species in long-duration field recordings using false-colour spectrograms and acoustic indices. Acid frogs 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 the large quantities of acoustic data collected using these methods is time-consuming and not feasible in the long-term. Therefore, there is a high demand for automated acoustic recognition tools to efficiently search months of recordings and identify target species. Our research provides more insight on how to choose acoustic features that efficiently recognise species from large-scale field-collected recordings at a larger scale. 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).