Wavelet filters for automated recognition of birdsong in long‐time field recordings

Abstract

Ecoacoustics has the potential to provide a large amount of information about the abundance of many animal species at a relatively low cost. Acoustic recording units are widely used in field data collection, but the facilities to reliably process the data recorded – recognizing calls that are relatively infrequent, and often significantly degraded by noise and distance to the microphone – are not well‐developed yet. We propose a call detection method for continuous field recordings that can be trained quickly and easily on new species, and degrades gracefully with increased noise or distance from the microphone. The method is based on the reconstruction of the sound from a subset of the wavelet nodes (elements in the wavelet packet decomposition tree). It is intended as a preprocessing filter, therefore we aim to minimize false negatives: false positives can be removed in subsequent processing, but missed calls will not be looked at again. We compare our method to standard call detection methods, and also to machine learning methods (using as input features either wavelet energies or Mel‐Frequency Cepstral Coefficients) on real‐world noisy field recordings of six bird species. The results show that our method has higher recall (proportion detected) than the alternative methods: 87% with 85% specificity on >53 hr of test data, resulting in an 80% reduction in the amount of data that needed further verification. It detected >60% of calls that were extremely faint (far away), even with high background noise. This preprocessing method is available in our AviaNZ bioacoustic analysis program and enables the user to significantly reduce the amount of subsequent processing required (whether manual or automatic) to analyse continuous field recordings collected by spatially and temporally large‐scale monitoring of animal species. It can be trained to recognize new species without difficulty, and if several species are sought simultaneously, filters can be run in parallel.

Using intrinsic and contextual information associated with automated signal detections to improve call recognizer performance: A case study using the cryptic and critically endangered Night Parrot Pezoporus occidentalis

“…There are several options for researchers wanting to construct a call recognizer. They vary in complexity, from commercial off‐the‐shelf programmes such as Kaleidoscope (Wildlife Acoustics Inc.), to more recently, advanced machine learning algorithms (Koops, van Balen, & Wiering, 2014; Salamon & Bello, 2017), acoustic indices (Towsey, Wimmer, Williamson, & Roe, 2014), and wavelet‐based approaches (Priyadarshani, Marsland, Juodakis, Castro, & Listanti, 2020). Although the computational processes behind each differ, the basic premise remains the same; a computer is trained to detect and evaluate acoustic signals by comparing them to a known target signal.…”

Deep learning for supervised classification of temporal data in ecology

“…A common aim of ecologists using these data concerns assigning them into predefined classes, such as ecological states or biological entities. Typical examples include the recognition of bird species from sound recordings (e.g., Priyadarshani, Marsland, Juodakis, Castro, & Listanti 2020), the distinction between phases in the annual life cycle of plants (i.e., ‘phenophases’) from spectral time series (Melaas, Friedl, & Zhu 2013), or the recognition of behavioural states from animal movement data (Shamoun-Baranes, Bouten, van Loon, Meijer, & Camphuysen 2016). Many other examples exist, with scopes of application that range from the molecular level (Jaakkola, Diekhans, & Haussler 2000) to the global scale (e.g., Schneider, Friedl, & Potere 2010).…”

“…The assignment of time series into one of two or more predefined classes (hereafter referred to as ‘time series classification’; Keogh and Kasetty 2003) can be performed using a variety of different approaches, ranging from manual, expert-based, classification (Priyadarshani et al, 2020) to fully automated procedures (see Bagnall, Lines, Bostrom, Large, & Keogh 2017 for examples). In ecology, time series classification is generally approached by processing the time series data into a new set of ‘static’ variables - using hand-designed transformations, or techniques such as Fourier or wavelet transforms - and then using these variables as predictors in ‘classical’ classification algorithms, such as logistic or multinomial regressions or random forests (e.g., Reside, VanDerWal, Kutt, & Perkins 2010; Shamoun-Baranes et al, 2016; Dyderski, Paź, Frelich, & Jagodziński 2017; Capinha, 2019; Priyadarshani et al, 2020). In machine learning terminology, this approach is known as ‘feature-based’, where the ‘features’ are the variables that are extracted from the time series.…”

“…The assignment of time series into one of two or more predefined classes (hereafter referred to as 'time series classification'; Keogh and Kasetty, 2003) can be performed using a variety of different approaches, ranging from manual, expert-based, classification (Priyadarshani et al, 2020) to fully automated procedures (see Bagnall et al, 2017 for examples). In ecology, time series classification is generally approached by processing the time series data into a set of summary variables − using hand-designed transformations, or techniques such as Fourier or wavelet transforms − and then using these variables as predictors in 'classical' supervised classification algorithms, such as logistic or multinomial regressions or random forests (e.g.…”

Kızılgerdan Kuş Popülasyonu Biyoakustik Kayıtlarının Takibi İçin Kuş Sesi Tanıma Yöntemi Geliştirilmesi

“…Priyadarshani ve ark., dalgacık düğümlerinin bir alt kümesinden sesin yeniden yapılandırılmasına dayanan, yeni türler üzerinde hızlı ve kolay bir şekilde eğitilebilecek, sürekli saha kayıtları için bir kuş çağrı sesi algılama yöntemi önermiştir. Çalışmalarında Rocky Dağı Biyoloji Laboratuvarına (Rocky Mountain Biological Laboratory -RMBL) ait Amerika Kızılgerdanları veri setini de kullanmışlardır [10].…”