Abstract
Animal vocal communication is a broad and multi-disciplinary field of research. Studying various aspects of communication can provide key elements for understanding animal behavior, evolution, and cognition. Given the large amount of acoustic data accumulated from automated recorders, for which manual annotation and analysis is impractical, there is a growing need to develop algorithms and automatic methods for analyzing and identifying animal sounds. In this study we developed an automatic detection and analysis system based on audio signal processing algorithms and deep learning that is capable of processing and analyzing large volumes of data without human bias. We selected the White Spectacled Bulbul (Pycnonotus xanthopygos) as our bird model because it has a complex vocal communication system with a large repertoire which is used by both sexes, year-round. It is a common, widespread passerine in Israel, which is relatively easy to locate and record in a broad range of habitats. Like many passerines, the Bulbul’s vocal communication consists of two primary hierarchies of utterances, syllables and words. To extract each of these units’ characteristics, the fundamental frequency contour was modeled using a low degree Legendre polynomial, enabling it to capture the different patterns of variation from different vocalizations, so that each pattern could be effectively expressed using very few coefficients. In addition, a mel-spectrogram was computed for each unit, and several features were extracted both in the time-domain (e.g., zero-crossing rate and energy) and frequency-domain (e.g., spectral centroid and spectral flatness). We applied both linear and non-linear dimensionality reduction algorithms on feature vectors and validated the findings that were obtained manually, namely by listening and examining the spectrograms visually. Using these algorithms, we show that the Bulbul has a complex vocabulary of more than 30 words, that there are multiple syllables that are combined in different words, and that a particular syllable can appear in several words. Using our system, researchers will be able to analyze hundreds of hours of audio recordings, to obtain objective evaluation of repertoires, and to identify different vocal units and distinguish between them, thus gaining a broad perspective on bird vocal communication.
Highlights
Vocal communication is an essential tool for transferring information
Here we present a set of quantitative automatic methods that make up a pipeline of automatic detection of Bulbul calls, and an analysis of these vocal units that allows classification into different groups, both by supervised and unsupervised learning
A correct identification rate of 75% (True Positive Rate, or recall, i.e., the ratio between the number of Bulbul vocalization segments correctly identified, to the total number of segments with Bulbul vocalization in the test recording set) was yielded by the Convolutional Neural Networks (CNN) described in section “Detection of Bulbul Calls,” with a relatively low False Positive occurrences of less than one third (27%) of the True detections
Summary
Vocal communication is an essential tool for transferring information. It serves a diverse range of species and is a topic of multi-disciplinary interest. While being an essential part of various species’ biology, the study of vocal attributes and the inference of the signaling properties remains a major challenge. This is because the information conveyed by vocal communication includes many components and facets that include physical attributes such as amplitude, frequency, rhythm, and intensity, as well as more complex aspects such as syllables, words, phrases and more (Kershenbaum et al, 2016). Audio recordings produce a vast amount of digital data per vocalization These parameters may be expressed differently, which leads to different patterns and correlations between populations and individuals that can be difficult to identify and even predict. This raises intriguing questions about the meaning of animal sounds (Bruno and Tchernichovski, 2019)
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