Using image processing to detect and classify narrow-band cricket and frog calls

Mark–recapture of individually distinctive calls—a case study with signature whistles of bottlenose dolphins (Tursiops truncatus)

Robust abundance estimates of wild animal populations are needed to inform management policies and are often obtained through mark–recapture (MR) studies. Visual methods are commonly used, which limits data collection to daylight hours and good weather conditions. Passive acoustic monitoring offers an alternative, particularly if acoustic cues are naturally produced and individually distinctive. Here we investigate the potential of using individually distinctive signature whistles in a MR framework and evaluate different components of study design. We analyzed signature whistles of common bottlenose dolphins, Tursiops truncatus, using data collected from static acoustic monitoring devices deployed in Walvis Bay, Namibia. Signature whistle types (SWTs) were identified using a bout analysis approach (SIGnature IDentification [SIGID]—Janik et al. 2013). We investigated spatial variation in capture by comparing 21 synchronized recording days across four sites, and temporal variation from 125 recording days at one high-use site (Aphrodite Beach). Despite dolphin vocalizations (i.e., echolocation clicks) being detected at each site, SWTs were not detected at all sites and there was high variability in capture rates among sites where SWTs were detected (range 0–21 SWTs detected). At Aphrodite Beach, 53 SWTs were captured over 6 months and discovery curves showed an initial increase in newly detected SWTs, approaching asymptote during the fourth month. A Huggins closed capture model constructed from SWT capture histories at Aphrodite Beach estimated a population of 54–68 individuals from acoustic detection, which overlaps with the known population size (54–76 individuals—Elwen et al. 2019). This study demonstrates the potential power of using signature whistles as proxies for individual occurrence and in MR abundance estimation, but also highlights challenges in using this approach.

Automatic classification of a taxon-rich community recorded in the wild

There is a rich literature on automatic species identification of a specific target taxon as regards various vocalizing animals. Research usually is restricted to specific species--in most cases a single one. It is only very recently that the number of monitored species has started to increase for certain habitats involving birds. Automatic acoustic monitoring has not yet been proven to be generic enough to scale to other taxa and habitats than the ones described in the original research. Although attracting much attention, the acoustic monitoring procedure is neither well established yet nor universally adopted as a biodiversity monitoring tool. Recently, the multi-instance multi-label framework on bird vocalizations has been introduced to face the obstacle of simultaneously vocalizing birds of different species. We build on this framework to integrate novel, image-based heterogeneous features designed to capture different aspects of the spectrum. We applied our approach to a taxon-rich habitat that included 78 birds, 8 insect species and 1 amphibian. This dataset constituted the Multi-label Bird Species Classification Challenge-NIPS 2013 where the proposed approach achieved an average accuracy of 91.25% on unseen data.

Examining the effectiveness of discriminant function analysis and cluster analysis in species identification of male field crickets based on their calling songs

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

A Study of Mexican Free-Tailed Bat Chirp Syllables: Bayesian Functional Mixed Models for Nonstationary Acoustic Time Series

We describe a new approach to analyze chirp syllables of free-tailed bats from two regions of Texas in which they are predominant: Austin and College Station. Our goal is to characterize any systematic regional differences in the mating chirps and assess whether individual bats have signature chirps. The data are analyzed by modeling spectrograms of the chirps as responses in a Bayesian functional mixed model. Given the variable chirp lengths, we compute the spectrograms on a relative time scale interpretable as the relative chirp position, using a variable window overlap based on chirp length. We use 2D wavelet transforms to capture correlation within the spectrogram in our modeling and obtain adaptive regularization of the estimates and inference for the regions-specific spectrograms. Our model includes random effect spectrograms at the bat level to account for correlation among chirps from the same bat, and to assess relative variability in chirp spectrograms within and between bats. The modeling of spectrograms using functional mixed models is a general approach for the analysis of replicated nonstationary time series, such as our acoustical signals, to relate aspects of the signals to various predictors, while accounting for between-signal structure. This can be done on raw spectrograms when all signals are of the same length, and can be done using spectrograms defined on a relative time scale for signals of variable length in settings where the idea of defining correspondence across signals based on relative position is sensible.

A three-parameter model for classifying anurans into four genera based on advertisement calls

The vocalizations of anurans are innate in structure and may therefore contain indicators of phylogenetic history. Thus, advertisement calls of species which are more closely related phylogenetically are predicted to be more similar than those of distant species. This hypothesis was evaluated by comparing several widely used machine-learning algorithms. Recordings of advertisement calls from 142 species belonging to four genera were analyzed. A logistic regression model, using mean values for dominant frequency, coefficient of variation of root-mean square energy, and spectral flux, correctly classified advertisement calls with regard to genus with an accuracy above 70%. Similar accuracy rates were obtained using these parameters with a support vector machine model, a K-nearest neighbor algorithm, and a multivariate Gaussian distribution classifier, whereas a Gaussian mixture model performed slightly worse. In contrast, models based on mel-frequency cepstral coefficients did not fare as well. Comparable accuracy levels were obtained on out-of-sample recordings from 52 of the 142 original species. The results suggest that a combination of low-level acoustic attributes is sufficient to discriminate efficiently between the vocalizations of these four genera, thus supporting the initial premise and validating the use of high-throughput algorithms on animal vocalizations to evaluate phylogenetic hypotheses.

Estimating animal population density using passive acoustics

Reliable estimation of the size or density of wild animal populations is very important for effective wildlife management, conservation and ecology. Currently, the most widely used methods for obtaining such estimates involve either sighting animals from transect lines or some form of capture-recapture on marked or uniquely identifiable individuals. However, many species are difficult to sight, and cannot be easily marked or recaptured. Some of these species produce readily identifiable sounds, providing an opportunity to use passive acoustic data to estimate animal density. In addition, even for species for which other visually based methods are feasible, passive acoustic methods offer the potential for greater detection ranges in some environments (e.g. underwater or in dense forest), and hence potentially better precision. Automated data collection means that surveys can take place at times and in places where it would be too expensive or dangerous to send human observers.

Here, we present an overview of animal density estimation using passive acoustic data, a relatively new and fast-developing field. We review the types of data and methodological approaches currently available to researchers and we provide a framework for acoustics-based density estimation, illustrated with examples from real-world case studies. We mention moving sensor platforms (e.g. towed acoustics), but then focus on methods involving sensors at fixed locations, particularly hydrophones to survey marine mammals, as acoustic-based density estimation research to date has been concentrated in this area. Primary among these are methods based on distance sampling and spatially explicit capture-recapture. The methods are also applicable to other aquatic and terrestrial sound-producing taxa.

We conclude that, despite being in its infancy, density estimation based on passive acoustic data likely will become an important method for surveying a number of diverse taxa, such as sea mammals, fish, birds, amphibians, and insects, especially in situations where inferences are required over long periods of time. There is considerable work ahead, with several potentially fruitful research areas, including the development of (i) hardware and software for data acquisition, (ii) efficient, calibrated, automated detection and classification systems, and (iii) statistical approaches optimized for this application. Further, survey design will need to be developed, and research is needed on the acoustic behaviour of target species. Fundamental research on vocalization rates and group sizes, and the relation between these and other factors such as season or behaviour state, is critical. Evaluation of the methods under known density scenarios will be important for empirically validating the approaches presented here.

Automating identification of avian vocalizations using time-frequency information extracted from the Gabor transform

Based on the Gabor transform, a metric is developed and applied to automatically identify bird species from a sample of 568 digital recordings of songs/calls from 67 species of birds. The Gabor frequency-amplitude spectrum and the Gabor time-amplitude profile are proposed as a means to characterize the frequency and time patterns of a bird song. An approach based on template matching where unknown song clips are compared to a library of known song clips is used. After adding noise to simulate the background environment and using an adaptive high-pass filter to de-noise the recordings, the successful identification rate exceeded 93% even at signal-to-noise ratios as low as 5 dB. Bird species whose songs/calls were dominated by low frequencies were more difficult to identify than species whose songs were dominated by higher frequencies. The results suggest that automated identification may be practical if comprehensive libraries of recordings that encompass the vocal variation within species can be assembled.

Heterospecific Acoustic Interference: Effects on Calling in Oophaga pumilio

Call rate suppression is a common short-term solution for avoiding acoustic interference in animals. It has been widely documented between and within frog species, but the effects of non-anuran calling on frog vocalizations is less well known. Heterospecific acoustic interference on the calling of Oophaga pumilio (Bauer, 1994) (formerly Dendrobates pumilio) males was studied in a lowland, wet tropical forest in SE Nicaragua. Acoustic playback experiments were conducted to characterize the responses of O. pumilio males to interfering calls of cicadas, two species of crickets and a sympatric dendrobatid frog, Phyllobates lugubris. Call rate, call bout duration, percent of time calling, dominant frequency and latency to first-call were analyzed. Significant call rate suppression was observed during all stimulus playbacks, yet no significant differences were found in spontaneous call rates during pre- and post-playback trials. Dominant frequency significantly decreased after P. lugubris playback and first-call latency significantly decreased in response to both cicada and tree cricket playbacks. These results provide robust evidence that O. pumilio males can dynamically modify their calling pattern in unique ways, depending on the source of the heterospecific acoustic interference.

Rapid acoustic survey for biodiversity appraisal

Biodiversity assessment remains one of the most difficult challenges encountered by ecologists and conservation biologists. This task is becoming even more urgent with the current increase of habitat loss. Many methods–from rapid biodiversity assessments (RBA) to all-taxa biodiversity inventories (ATBI)–have been developed for decades to estimate local species richness. However, these methods are costly and invasive. Several animals–birds, mammals, amphibians, fishes and arthropods–produce sounds when moving, communicating or sensing their environment. Here we propose a new concept and method to describe biodiversity. We suggest to forego species or morphospecies identification used by ATBI and RBA respectively but rather to tackle the problem at another evolutionary unit, the community level. We also propose that a part of diversity can be estimated and compared through a rapid acoustic analysis of the sound produced by animal communities. We produced α and β diversity indexes that we first tested with 540 simulated acoustic communities. The α index, which measures acoustic entropy, shows a logarithmic correlation with the number of species within the acoustic community. The β index, which estimates both temporal and spectral dissimilarities, is linearly linked to the number of unshared species between acoustic communities. We then applied both indexes to two closely spaced Tanzanian dry lowland coastal forests. Indexes reveal for this small sample a lower acoustic diversity for the most disturbed forest and acoustic dissimilarities between the two forests suggest that degradation could have significantly decreased and modified community composition. Our results demonstrate for the first time that an indicator of biological diversity can be reliably obtained in a non-invasive way and with a limited sampling effort. This new approach may facilitate the appraisal of animal diversity at large spatial and temporal scales.