Dynamic Time Warping and Affinity Propagation Clustering for the categorisation of bird species: A case study Simone Clemente, Marco Gamba, Daniela Pessani, Livio Favaro Department of Life Sciences and Systems Biology, University of Turin, Italy Correspondence to:
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AIMS
INTRODUCTION The unsupervised acoustic analysis is becoming the method of choice in animal vocal communication studies (Stowell et al. 2014). Recently, Dynamic Time Warping (DTW) algorithms have been proved reliable to extract the biologically meaningful information encoded in vertebrate calls (e.g. Chen et al. 2012; Riondato et al. 2013). Moreover, the combination of DTW and unsupervised clustering requires very limited a priori specifications (Gamba et al. 2015) and without having to satisfy the restrictive assumptions needed by subjective classification.
METHODS Study area Riserva Naturale Orientata delle Baragge, Northwest Italy. Data were collected in a large woodland close to the Fiat Chrysler Automobiles (FCA) Verrone plant (45°29’14”N, . 8°07’45”E) Vocal recordings Diurnal calls of 26 bird species were recorded from February to July 2014 with a RØDE NTG2 condenser transducer microphone (frequency response 20 Hz to 20 kHz, max SPL 131dB) mounted on a RØDE PG2 Pistol Grip and connected to a TASCAM DR-680 digital recorder (44.1 kHz sampling rate).
In this study, we tested the reliability of a Dynamic Time Warping algorithm combined to an Affinity Propagation Clustering for the pairwise acoustic dissimilarity calculation and categorisation of wild bird calls.
RESULTS The Adjusted Rand Index showed the highest values for the clustering solution with 71 leaves. When seen in the light of the vocalising species within a segment (Fig. 1), the clustering solution showed an agreement of 90% to the bird species or assemblage that we observed in the field.
88%
94% 94% 95%
Spectrographic analysis Bird vocalisations were selected from the original files and coupled with the information collected in the field about the vocalising species. A spectrographic inspection left us with a total of 2058 calls. Vocal analysis We calculated a Pairwise Dissimilarity Matrix using a Dynamic Time Warping algorithm with a custom-built script in Python (Python Software Foundation). The matrix was then submitted to a clustering process in R (R Core Team) using the Affinity Propagation algorithm. The most robust clusterisation was evaluated between successive clustering processes using the Adjusted Rand Index.
Acknowledgements
We are grateful to Luciano Capacci (FCA Verrone Plant) for his assiduous participation and support to this project. We also thank Giovanni Soldato and Arianna Cibin who helped with field recordings.
References
Chen et al 2012. Comput. Math. Apll. 64:1270-1281; Gamba et al 2015. Int. J. Primatol. In press; Riondato et al 2013. Viet. J. Primatol. 2:75-82; Stowell et al 2014. PeerJ 2:e488.
Figure 1. The agreement between the clustering
solutions and the number of bird species recorded in the files (single species 88%, two species 94%, three species 94%, more than one species 95%).
CONCLUSIONS The combination of Dynamic Time Warping and Affinity Propagation Clustering is a powerful tool for the categorisation of wild bird calls. We recommend additional investigations to evaluate whether this approach could classify vocalisations in real-time and, therefore, be suitable to develop effective passive acoustic monitoring systems.
