The output vector from the AudioSet convolutional neural net (CNN Gemmeke et al., 2017 Hershey et al., 2017) is an attractive replacement for Analytical Indices. This may result from a lack of standardization: differing index selection, data storage methods, and recording protocols, which all lead to unassessed variation in experimental outputs (Araya-Salas et al., 2019 Bradfer-Lawrence et al., 2019 Sugai et al., 2019). These approaches have provided novel insight into ecosystems across the world (Buxton et al., 2018 Eldridge et al., 2018 Fuller et al., 2015 Sueur et al., 2019) but are not foolproof and often have poor transferability (Bohnenstiehl et al., 2018 Mammides et al., 2017). These are commonly used in combination to compare the occupancy of acoustic niches, temporal variation, and the general level of acoustic activity (Bradfer-Lawrence et al., 2019) across ecological gradients or in classification tasks (Gómez et al., 2018). Analytical Indices are a type of acoustic index which are summary statistics that describe the distribution of acoustic energy within the recording (Towsey et al., 2014)-over 60 of which have been designed to capture aspects of biodiversity (Buxton et al., 2018 Sueur et al., 2014). Soundscape composition is primarily assessed using acoustic indices which describe the soundscape in an abstracted form. Advances in portable computing now permit remote recording of soundscapes, but produce a volume of data that is very time-consuming to review manually, leading to the development of automated, or semiautomated, methods of analysis (Sethi, Jones, et al., 2020 Towsey et al., 2016). Previously, the use of in situ expert listeners to monitor species presence and abundance was common (Huff et al., 2000) but is costly and time-consuming can damage habitats and is prone to narrow focus and observer bias (Costello et al., 2016 Fitzpatrick et al., 2009). Monitoring is crucial to effectively respond to threats such as disease, species loss, and overlogging (Rapport, 1989 Rapport et al., 1998). These soundscapes can be recorded and quantified across large temporal and spatial dimensions to monitor species populations or infer community-level metrics such as biodiversity (Eldridge et al., 2018 Gómez et al., 2018 Roca & Proulx, 2016). These recommendations allow the efficient use of restricted data storage whilst permitting comparability of results between different studies.Īnimal vocalizations come together with abiotic and human-made sounds to form soundscapes. The AudioSet Fingerprint can be compressed further to a Constant Bit Rate encoding of 64 kb/s (8% file size) without any detectable effect. If data storage is a bottleneck to a study, we recommend Variable Bit Rate encoded compression (quality = 0) to reduce file size to 23% file size without affecting most Analytical Index values.
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