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

Exploring Animal Behavior Through Sound: Volume 1

This open-access book empowers its readers to explore the acoustic world of animals. By listening to the sounds of nature, we can study animal behavior, distribution, and demographics; their habitat characteristics and needs; and the effects of noise. Sound recording is an efficient and affordable tool, independent of daylight and weather; and recorders may be left in place for many months at a time, continuously collecting data on animals and their environment. This book builds the skills and knowledge necessary to collect and interpret acoustic data from terrestrial and marine environments. Beginning with a history of sound recording, the chapters provide an overview of off-the-shelf recording equipment and analysis tools (including automated signal detectors and statistical methods); audiometric methods; acoustic terminology, quantities, and units; sound propagation in air and under water; soundscapes of terrestrial and marine habitats; animal acoustic and vibrational communication; echolocation; and the effects of noise. This book will be useful to students and researchers of animal ecology who wish to add acoustics to their toolbox, as well as to environmental managers in industry and government

Acoustic monitoring of wildlife in inaccessible areas and automatic detection of bird songs from continuous recordings

The use of new technology for wildlife monitoring comes with both possible benefits and challenges. Unmanned aerial vehicles (UAVs) and automatic recording units (ARUs) can allow researchers to automatically record videos, photographs, and audio recordings of animals in unusual or inaccessible locations. However, new acoustic monitoring techniques require innovative methods to extract and utilize data from acoustic recordings. In this project we developed novel technology to record bird songs in inaccessible areas and demonstrated a useful method for extracting and classifying songs from continuous recordings. The autonomous aerial acoustic recording system (AAARS) was a UAV developed at the University of Tennessee capable of generating high-quality WAV recordings of bird songs in a variety of landscapes. The AAARS was completely silent in flight controlled by a ground-based computer monitoring station. I developed a model to convert the AAARS GPS-based flight path into a microphone exposure surface to relate species-specific acoustic signals recorded to area of microphone coverage. The vocalizations per unit area per unit time for a given focal species could then be used as an index of relative abundance or as an input in density estimation. Once collected, extraction and classification of birdsongs from acoustic recordings remains a major technological challenge. I used quadratic discrimination analysis to differentiate between inter- and intra-specific bird songs using up to sixteen acoustic measurements on human-extracted signals from audio spectrograms of five focal songbird species. Measurement-based classification was successful at separating the five species apart with only ≤5% classification error. I then used a template-matching model to extract target birdsongs from continuous field recordings and investigated the efficiency of different analytical options for classification of five focal songbird species. Decision trees, neural networks, and quadratic discriminant analysis all produced similar classification results. The means to optimize the analytical approach varied by species. I concluded that a species-specific approach should be used to accurately extract and classify songs from continuous recordings