Dissimilarity-based multiple instance classification and dictionary learning for bioacoustic signal recognition

In this thesis, two promising and actively researched fields from pattern recognition (PR) and digital signal processing (DSP) are studied, adapted and applied for the automated recognition of bioacoustic signals: (i) learning from weakly-labeled data, and (ii) dictionary-based decomposition. The document begins with an overview of the current methods and techniques applied for the automated recognition of bioacoustic signals, and an analysis of the impact of this technology at global and local scales. This is followed by a detailed description of my research on studying two approaches from the above-mentioned fields, multiple instance learning (MIL) and dictionary learning (DL), as solutions to particular challenges in bioacoustic data analysis. The most relevant contributions and findings of this thesis are the following ones: 1) the proposal of an unsupervised recording segmentation method of audio birdsong recordings that improves species classification with the benefit of an easier implementation since no manual handling of recordings is required; 2) the confirmation that, in the analyzed audio datasets, appropriate dissimilarity measures are those which capture most of the overall differences between bags, such as the modified Hausdorff distance and the mean minimum distance; 3) the adoption of dissimilarity adaptation techniques for the enhancement of dissimilarity-based multiple instance classification, along with the potential further enhancement of the classification performance by building dissimilarity spaces and increasing training set sizes; 4) the proposal of a framework for solving MIL problems by using the one nearest neighbor (1-NN) classifier; 5) a novel convolutive DL method for learning a representative dictionary from a collection of multiple-bird audio recordings; 6) such a DL method is successfully applied to spectrogram denoising and species classification; and, 7) an efficient online version of the DL method that outperforms other state-of-the-art batch and online methods, in both, computational cost and quality of the discovered patternsResumen : En esta tesis se estudian, adaptan y aplican dos prometedoras y activas áreas del reconocimiento de patrones (PR) y procesamiento digital de señales (DSP): (i) aprendizaje débilmente supervisado y (ii) descomposiciones basadas en diccionarios. Inicialmente se hace una revisión de los métodos y técnicas que actualmente se aplican en tareas de reconocimiento automatizado de señales bioacústicas y se describe el impacto de esta tecnología a escalas nacional y global. Posteriormente, la investigación se enfoca en el estudio de dos técnicas de las áreas antes mencionadas, aprendizaje multi-instancia (MIL) y aprendizaje de diccionarios (DL), como soluciones a retos particulares del análisis de datos bioacústicos. Las contribuciones y hallazgos ms relevantes de esta tesis son los siguientes: 1) se propone un método de segmentacin de grabaciones de audio que mejora la clasificación automatizada de especies, el cual es fácil de implementar ya que no necesita información supervisada de entrenamiento; 2) se confirma que, en los conjuntos de datos analizados, las medidas de disimilitudes que capturan las diferencias globales entre bolsas funcionan apropiadamente, tales como la distancia modificada de Hausdorff y la distancia media de los mínimos; 3) la adopción de técnicas de adaptación de disimilitudes para mejorar la clasificación multi-instancia, junto con el incremento potencial del desempeño por medio de la construcción de espacios de disimilitudes y el aumento del tamaño de los conjuntos de entrenamiento; 4) se presenta un esquema para la solución de problemas MIL por medio del clasificador del vecino ms cercano (1-NN); 5) se propone un método novedoso de DL, basado en convoluciones, para el aprendizaje automatizado de un diccionario representativo a partir de un conjunto de grabaciones de audio de múltiples vocalizaciones de aves; 6) dicho mtodo DL se utiliza exitosamente como técnica de reducción de ruido en espectrogramas y clasificación de grabaciones bioacústicas; y 7) un método DL, de procesamiento en línea, que supera otros métodos del estado del arte en costo computacional y calidad de los patrones descubiertosDoctorad

Learning deep models from synthetic data for extracting dolphin whistle contours

We present a learning-based method for extracting whistles of toothed whales (Odontoceti) in hydrophone recordings. Our method represents audio signals as time-frequency spectrograms and decomposes each spectrogram into a set of time-frequency patches. A deep neural network learns archetypical patterns (e.g., crossings, frequency modulated sweeps) from the spectrogram patches and predicts time-frequency peaks that are associated with whistles. We also developed a comprehensive method to synthesize training samples from background environments and train the network with minimal human annotation effort. We applied the proposed learn-from-synthesis method to a subset of the public Detection, Classification, Localization, and Density Estimation (DCLDE) 2011 workshop data to extract whistle confidence maps, which we then processed with an existing contour extractor to produce whistle annotations. The F1-score of our best synthesis method was 0.158 greater than our baseline whistle extraction algorithm (~25% improvement) when applied to common dolphin (Delphinus spp.) and bottlenose dolphin (Tursiops truncatus) whistles.Postprin

A new method for ecoacoustics? Toward the extraction and evaluation of ecologically-meaningful soundscape components using sparse coding methods

Passive acoustic monitoring is emerging as a promising non-invasive proxy for ecological complexity with potential as a tool for remote assessment and monitoring (Sueur and Farina, 2015). Rather than attempting to recognise species-specific calls, either manually or automatically, there is a growing interest in evaluating the global acoustic environment. Positioned within the conceptual framework of ecoacoustics, a growing number of indices have been proposed which aim to capture community-level dynamics by (e.g. Pieretti et al., 2011; Farina, 2014; Sueur et al., 2008b) by providing statistical summaries of the frequency or time domain signal. Although promising, the ecological relevance and efficacy as a monitoring tool of these indices is still unclear. In this paper we suggest that by virtue of operating in the time or frequency domain, existing indices are limited in their ability to access key structural information in the spectro-temporal domain. Alternative methods in which time-frequency dynamics are preserved are considered. Sparse-coding and source separation algorithms (specifically, shift-invariant probabilistic latent component analysis in 2D) are proposed as a means to access and summarise time- frequency dynamics which may be more ecologically-meaningful

Automated bioacoustics:methods in ecology and conservation and their potential for animal welfare monitoring

Vocalizations carry emotional, physiological and individual information. This suggests that they may serve as potentially useful indicators for inferring animal welfare. At the same time, automated methods for analysing and classifying sound have developed rapidly, particularly in the fields of ecology, conservation and sound scene classification. These methods are already used to automatically classify animal vocalizations, for example, in identifying animal species and estimating numbers of individuals. Despite this potential, they have not yet found widespread application in animal welfare monitoring. In this review, we first discuss current trends in sound analysis for ecology, conservation and sound classification. Following this, we detail the vocalizations produced by three of the most important farm livestock species: chickens (Gallus gallus domesticus), pigs (Sus scrofa domesticus) and cattle (Bos taurus). Finally, we describe how these methods can be applied to monitor animal welfare with new potential for developing automated methods for large-scale farming

Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/ licenses/by/4.0

Classification and ranking of environmental recordings to facilitate efficient bird surveys

This thesis contributes novel computer-assisted techniques to facilitating bird species surveys from a large number of environmental audio recordings. These techniques are applicable to both manual and automated recognition of bird species by removing irrelevant audio data and prioritising those relevant data for efficient bird species detection. This work also represents a significant step towards using automated techniques to support experts and the general public to explore and gain a better understanding of vocal species

A new method for ecoacoustics? Toward the extraction and evaluation of ecologically-meaningful soundscape components using sparse coding methods

Passive acoustic monitoring is emerging as a promising non-invasive proxy for ecological complexity with potential as a tool for remote assessment and monitoring (Sueur & Farina, 2015). Rather than attempting to recognise species-specific calls, either manually or automatically, there is a growing interest in evaluating the global acoustic environment. Positioned within the conceptual framework of ecoacoustics, a growing number of indices have been proposed which aim to capture community-level dynamics by (e.g., Pieretti, Farina & Morri, 2011; Farina, 2014; Sueur et al., 2008b) by providing statistical summaries of the frequency or time domain signal. Although promising, the ecological relevance and efficacy as a monitoring tool of these indices is still unclear. In this paper we suggest that by virtue of operating in the time or frequency domain, existing indices are limited in their ability to access key structural information in the spectro-temporal domain. Alternative methods in which time-frequency dynamics are preserved are considered. Sparse-coding and source separation algorithms (specifically, shift-invariant probabilistic latent component analysis in 2D) are proposed as a means to access and summarise time-frequency dynamics which may be more ecologically-meaningful

Listening forward: approaching marine biodiversity assessments using acoustic methods

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Mooney, T. A., Di Iorio, L., Lammers, M., Lin, T., Nedelec, S. L., Parsons, M., Radford, C., Urban, E., & Stanley, J. Listening forward: approaching marine biodiversity assessments using acoustic methods. Royal Society Open Science, 7(8), (2020): 201287, doi:10.1098/rsos.201287.Ecosystems and the communities they support are changing at alarmingly rapid rates. Tracking species diversity is vital to managing these stressed habitats. Yet, quantifying and monitoring biodiversity is often challenging, especially in ocean habitats. Given that many animals make sounds, these cues travel efficiently under water, and emerging technologies are increasingly cost-effective, passive acoustics (a long-standing ocean observation method) is now a potential means of quantifying and monitoring marine biodiversity. Properly applying acoustics for biodiversity assessments is vital. Our goal here is to provide a timely consideration of emerging methods using passive acoustics to measure marine biodiversity. We provide a summary of the brief history of using passive acoustics to assess marine biodiversity and community structure, a critical assessment of the challenges faced, and outline recommended practices and considerations for acoustic biodiversity measurements. We focused on temperate and tropical seas, where much of the acoustic biodiversity work has been conducted. Overall, we suggest a cautious approach to applying current acoustic indices to assess marine biodiversity. Key needs are preliminary data and sampling sufficiently to capture the patterns and variability of a habitat. Yet with new analytical tools including source separation and supervised machine learning, there is substantial promise in marine acoustic diversity assessment methods.Funding for development of this article was provided by the collaboration of the Urban Coast Institute (Monmouth University, NJ, USA), the Program for the Human Environment (The Rockefeller University, New York, USA) and the Scientific Committee on Oceanic Research. Partial support was provided to T.A.M. from the National Science Foundation grant OCE-1536782

Public Engagement Technology for Bioacoustic Citizen Science

Inexpensive mobile devices offer new capabilities for non-specialist use in the field for the purpose of conservation. This thesis explores the potential for such devices to be used by citizen scientists interacting with bioacoustic data such as birdsong. This thesis describes design research and field evaluation, in collaboration with conservationists and educators, and technological artefacts implemented as mobile applications for interactive educational gaming and creative composition. This thesis considers, from a participant-centric collaborative design approach, conservationists' demand for interactive artefacts to motivate engagement in citizen science through gameful and playful interactions. Drawing on theories of motivation, frequently applied to the study of Human-Computer Interaction (HCI), and on approaches to designing for motivational engagement, this thesis introduces a novel pair of frameworks for the analysis of technological artefacts and for assessing participant engagement with bioacoustic citizen science from both game interaction design and citizen science project participation perspectives. This thesis reviews current theories of playful and gameful interaction developed for collaborative learning, data analysis, and ground-truth development, describes a process for design and analysis of motivational mobile games and toys, and explores the affordances of various game elements and mechanics for engaging participation in bioacoustic citizen science. This thesis proposes research into progressions for scaffolding engagement with citizen science projects where participants interact with data collection and analysis artefacts. The research process includes the development of multiple designs, analyses of which explore the efficacy of game interactions to motivate engagement through interaction progressions, given proposed analysis frameworks. This thesis presents analysed results of experiments examining the usability of, and data-quality from, several prototypes and software artefacts, in both laboratory conditions and the field. This thesis culminates with an assessment of the efficacy of proposed design analysis frameworks, an analysis of designed artefacts, and a discussion of how these designs increase intrinsic and extrinsic motivation for participant engagement and affect resultant bioacoustic citizen science data quantity and quality.Non