Wavelet-based birdsong recognition for conservation : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand

Listed in 2017 Dean's List of Exceptional ThesesAccording to the International Union for the Conservation of Nature Red Data List nearly a quarter of the world's bird species are either threatened or at risk of extinction. To be able to protect endangered species, we need accurate survey methods that reliably estimate numbers and hence population trends. Acoustic monitoring is the most commonly-used method to survey birds, particularly cryptic and nocturnal species, not least because it is non-invasive, unbiased, and relatively time-effective. Unfortunately, the resulting data still have to be analysed manually. The current practice, manual spectrogram reading, is tedious, prone to bias due to observer variations, and not reproducible. While there is a large literature on automatic recognition of targeted recordings of small numbers of species, automatic analysis of long field recordings has not been well studied to date. This thesis considers this problem in detail, presenting experiments demonstrating the true efficacy of recorders in natural environments under different conditions, and then working to reduce the noise present in the recording, as well as to segment and recognise a range of New Zealand native bird species. The primary issues with field recordings are that the birds are at variable distances from the recorder, that the recordings are corrupted by many different forms of noise, that the environment affects the quality of the recorded sound, and that birdsong is often relatively rare within a recording. Thus, methods of dealing with faint calls, denoising, and effective segmentation are all needed before individual species can be recognised reliably. Experiments presented in this thesis demonstrate clearly the effects of distance and environment on recorded calls. Some of these results are unsurprising, for example an inverse square relationship with distance is largely true. Perhaps more surprising is that the height from which a call is transmitted has a signifcant effect on the recorded sound. Statistical analyses of the experiments, which demonstrate many significant environmental and sound factors, are presented. Regardless of these factors, the recordings have noise present, and removing this noise is helpful for reliable recognition. A method for denoising based on the wavelet packet decomposition is presented and demonstrated to significantly improve the quality of recordings. Following this, wavelets were also used to implement a call detection algorithm that identifies regions of the recording with calls from a target bird species. This algorithm is validated using four New Zealand native species namely Australasian bittern (Botaurus poiciloptilus), brown kiwi (Apteryx mantelli ), morepork (Ninox novaeseelandiae), and kakapo (Strigops habroptilus), but could be used for any species. The results demonstrate high recall rates and tolerate false positives when compared to human experts

Data from: AviaNZ: a future-proofed program for annotation and recognition of animal sounds in long-time field recordings

The routine collection of long‐time acoustic recordings of animals in the field presents new challenges in data analysis. While many terabytes of data are collected annually, effective use of this noisy, highly variable data require skilled humans to manually identify calls. While computer programs to automatically analyse these recordings are becoming available, it is important that they are user‐friendly and easy‐to‐use, so that everybody – citizen scientists, wildlife managers, researchers – can take advantage of them, and that they keep the human in the loop so analyses carried out this year are comparable both to manual call counts from the past, and more accurate automated analyses performed in the future. We present the AviaNZ program, which is designed to achieve these goals: the software includes methods for simple, rapid manual annotation of recordings, denoising and segmentation methods, and a training procedure by which the user can prepare their own filters to automatically recognize individual species. The software can run in batch mode, automatically processing folders of field recordings, and then present the outputs to enable the quick and easy review of the results. Finally, the outputs are presented in a variety of spreadsheets to enable different statistical analyses to be performed. We describe the various workflows of manually and semi‐automatically processing sound files, annotating them to train automatic filters, using those filters in batch mode, and how the software facilitates rapid evaluation of the automated analysis. A demonstration of the software, comparing manual and automatic detection of calls of the little spotted kiwi Apteryx owenii is given. It shows that while the automatic detection does produce false positives, human correction of these is far faster than manual review of the whole sound file. AviaNZ is a freely available open‐source standalone program. Our experience shows that it can be used by anybody quickly and easily. However, for experienced users it is easily customizable and extendable. By enabling everybody involved with acoustic bird recording to quickly and easily analyse their own data, while future‐proofing it by keeping the human in the loop, we are enabling acoustic field recordings to meet their potential

Zealandia Nocturnal Soundscape Oct 6th 2018

This is the sound (.wav) and annotation files (.data; AviaNZ format) for the soundscape of Zealandia Ecosanctuary (Wellington, New Zealand) for the night of 6th October 2018. There are also trained AviaNZ filters for Little Spotted Kiwi (Apteryx owenii) and the sound files for the training data. The contents are in the README

Birdsong Denoising Using Wavelets

<div><p>Automatic recording of birdsong is becoming the preferred way to monitor and quantify bird populations worldwide. Programmable recorders allow recordings to be obtained at all times of day and year for extended periods of time. Consequently, there is a critical need for robust automated birdsong recognition. One prominent obstacle to achieving this is low signal to noise ratio in unattended recordings. Field recordings are often very noisy: birdsong is only one component in a recording, which also includes noise from the environment (such as wind and rain), other animals (including insects), and human-related activities, as well as noise from the recorder itself. We describe a method of denoising using a combination of the wavelet packet decomposition and band-pass or low-pass filtering, and present experiments that demonstrate an order of magnitude improvement in noise reduction over natural noisy bird recordings.</p></div

Box plot view of Table 5.

<p>(a) call series and (b) segmented calls.</p

Examples of bird calls with various degrees of noise, the effect of band-pass filtering and power spectrum of white and pink noise.

<p>The top row of each sound figure displays the oscillogram and the second row the spectrogram. (a) A less noisy example of kakapo <i>chinging</i> with limited noise and (b) a noisy example of kakapo <i>chinging</i>. (c) An original male kiwi whistle and (d) its noise filtered (band-pass) signal. Noise is visible as a grey background in the spectrogram surrounding the sound depiction and most of the high-frequency variation in the oscillogram. Power spectrum of (e) white noise and (f) pink noise.</p

Denoising entire songs and long series of calls.

<p>(a) A North Island kaka song, (b) a marsh wren song, (c) a western meadowlark song, and (d) a series of kakapo <i>chinging</i>.</p

Box plot view of the results in (a) Table 3 and (b) Table 4.

<p>Box plot view of the results in (a) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146790#pone.0146790.t003" target="_blank">Table 3</a> and (b) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146790#pone.0146790.t004" target="_blank">Table 4</a>.</p

Spectrogram representations of various bird species showing some of the typical appearances of sounds.

<p>(a) A fox sparrow (<i>Passerella iliaca</i>) song illustrating its syllables, phrases, and elements (S = syllable and E = element). (b)-(e) show representations of lines: (b) tui (<i>Prosthemadera novaeseelandiae</i>); (c) the <i>more-pork</i> sound of ruru (<i>Ninox novaeseelandiae</i>); (d) kakapo (<i>Strigops habroptilus</i>) <i>booming</i>; (e) brewer’s sparrow (<i>Spizella breweri</i>). (f)-(h) demonstrate blocks: (f) (long billed) marsh wren (<i>Cistothorus palustris</i>); (g) female North Island brown kiwi (<i>Apteryx mantelli</i>) call; (h) kakapo <i>chinging</i>. (i)-(j) show stacked harmonics: (i) male North Island brown kiwi whistles; (j) ruru <i>trill</i>. (k) oscillations: North Island saddleback (<i>Philesturnus rufusater</i>).</p

Different mother wavelets produce different results.

<p>Same excerpt of a male kiwi whistle (a) original whistle and (b)—(e) denoised with different mother wavelets.</p