Data mining in large audio collections of dolphin signals

The study of dolphin cognition involves intensive research of animal vocal- izations recorded in the field. In this dissertation I address the automated analysis of audible dolphin communication. I propose a system called the signal imager that automatically discovers patterns in dolphin signals. These patterns are invariant to frequency shifts and time warping transformations. The discovery algorithm is based on feature learning and unsupervised time series segmentation using hidden Markov models. Researchers can inspect the patterns visually and interactively run com- parative statistics between the distribution of dolphin signals in different behavioral contexts. The required statistics for the comparison describe dolphin communication as a combination of the following models: a bag-of-words model, an n-gram model and an algorithm to learn a set of regular expressions. Furthermore, the system can use the patterns to automatically tag dolphin signals with behavior annotations. My results indicate that the signal imager provides meaningful patterns to the marine biologist and that the comparative statistics are aligned with the biologists’ domain knowledge.Ph.D

Large-scale analysis of frequency modulation in birdsong data bases

DS & MP are supported by an EPSRC Leadership Fellowship EP/G007144/1. Our thanks to Alan McElligott for helpful advice while preparing the manuscript; Sašo Muševič for discussion and for making his DDM software available; and Rémi Gribonval and team at INRIA Rennes for discussion and software development during a research visit

The Importance of Bioacoustics for Dolphin Welfare: Soundscape Characterization with Implications for Management

Sound is the primary sensory modality for dolphins, yet policies mitigating anthropogenic sound exposure are limited in wild populations and even fewer noise policies or guidelines have been developed for governing dolphin welfare under human care. Concerns have been raised that dolphins under human care live in facilities that are too noisy, or are too acoustically sterile. However, these claims have not been evaluated to characterize facility soundscapes, and further, how they compare to wild soundscapes. The soundscape of a wild dolphin habitat off the coast of Quintana, Roo, Mexico was characterized based on Passive Acoustic Monitoring (PAM) recordings over one year. Snapping shrimp were persistent and broadband, following a diel pattern. Fish sound production was pulsed and prominent in low frequencies (100 ─ 1000 Hz), and abiotic surface wave action contributed to noise in higher frequencies (15 ─ 28 kHz). Boat motors were the main anthropogenic sound source. While sporadic, boat motors were responsible for large spikes in the noise, sometimes exceeding the ambient noise (in the absence of a boat) by 20 dB root-mean-squared sound pressure level, and potentially higher at closer distances. Boat motor sounds can potentially mask cues and communication sounds of dolphins. The soundscapes of four acoustically distinct outdoor dolphin facilities in Quintana Roo, Mexico were also characterized based on PAM, and findings compared with one another and with the measurements from the wild dolphin habitat. Recordings were made for at least 24 hours to encompass the range of daily activities. The four facilities differed in non-dolphin species present (biological sounds), bathymetry complexity, and method of water circulation. It was hypothesized that the greater the biological and physical differences of a pool from the ocean habitat, the greater the acoustic differences would be from the natural environment. Spectral analysis and audio playback revealed that the site most biologically and physically distinct from the ocean habitat also differed greatly from the other sites acoustically, with the most common and high amplitude sound being pump noise versus biological sounds at the other sites. Overall the dolphin facilities were neither clearly noisier nor more sterile than the wild site, but rather differed in particular characteristics. The findings are encouraging for dolphin welfare for several reasons. Sound levels measured were unlikely to cause threshold shifts in hearing. At three of four facilities, prominent biological sounds in the wild site ─ snapping shrimp and fish sounds ─ were present, meaning that the dolphins at these facilities are experiencing biotic features of the soundscape they would experience in the wild. Additionally, the main anthropogenic sounds experienced at the facilities (construction and cleaning sounds) did not reach the levels of the anthropogenic sounds experienced at the wild site (boat motor sounds), and the highest noise levels for anthropogenic sounds fall outside the dolphins\u27 most sensitive range of hearing. However, there are anthropogenic contributors to the soundscape that are of particular interest and possible concern that should be investigated further, particularly pump noise and periodic or intermittent construction noise. These factors need to be considered on a facility-by-facility basis and appropriate mitigation procedures incorporated in animal handling to mitigate potential responses to planned or anticipated sound producing events, e.g. animal relocation or buffering sound producing activities. The central role of bioacoustics for dolphins means that PAM is a basic life support requirement along with water and food testing. Periodic noise is of highest concern, and PAM is needed to inform mitigation of noise from periodic sources. Priority actions are more widespread and long-term standardized monitoring, further research on habituation, preference, coupling and pool acoustics, implementation of acoustics training, standardization of measurements, and improved information access

Sounding the call for a global library of underwater biological sounds

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Parsons, M., Lin, T.-H., Mooney, T., Erbe, C., Juanes, F., Lammers, M., Li, S., Linke, S., Looby, A., Nedelec, S., Van Opzeeland, I., Radford, C., Rice, A., Sayigh, L., Stanley, J., Urban, E., & Di Iorio, L. Sounding the call for a global library of underwater biological sounds. Frontiers in Ecology and Evolution, 10, (2022): 810156, https://doi.org/10.3389/fevo.2022.810156.Aquatic environments encompass the world’s most extensive habitats, rich with sounds produced by a diversity of animals. Passive acoustic monitoring (PAM) is an increasingly accessible remote sensing technology that uses hydrophones to listen to the underwater world and represents an unprecedented, non-invasive method to monitor underwater environments. This information can assist in the delineation of biologically important areas via detection of sound-producing species or characterization of ecosystem type and condition, inferred from the acoustic properties of the local soundscape. At a time when worldwide biodiversity is in significant decline and underwater soundscapes are being altered as a result of anthropogenic impacts, there is a need to document, quantify, and understand biotic sound sources–potentially before they disappear. A significant step toward these goals is the development of a web-based, open-access platform that provides: (1) a reference library of known and unknown biological sound sources (by integrating and expanding existing libraries around the world); (2) a data repository portal for annotated and unannotated audio recordings of single sources and of soundscapes; (3) a training platform for artificial intelligence algorithms for signal detection and classification; and (4) a citizen science-based application for public users. Although individually, these resources are often met on regional and taxa-specific scales, many are not sustained and, collectively, an enduring global database with an integrated platform has not been realized. We discuss the benefits such a program can provide, previous calls for global data-sharing and reference libraries, and the challenges that need to be overcome to bring together bio- and ecoacousticians, bioinformaticians, propagation experts, web engineers, and signal processing specialists (e.g., artificial intelligence) with the necessary support and funding to build a sustainable and scalable platform that could address the needs of all contributors and stakeholders into the future.Support for the initial author group to meet, discuss, and build consensus on the issues within this manuscript was provided by the Scientific Committee on Oceanic Research, Monmouth University Urban Coast Institute, and Rockefeller Program for the Human Environment. The U.S. National Science Foundation supported the publication of this article through Grant OCE-1840868 to the Scientific Committee on Oceanic Research

Classification of Animal Sound Using Convolutional Neural Network

Recently, labeling of acoustic events has emerged as an active topic covering a wide range of applications. High-level semantic inference can be conducted based on main audioeffects to facilitate various content-based applications for analysis, efficient recovery and content management. This paper proposes a flexible Convolutional neural network-based framework for animal audio classification. The work takes inspiration from various deep neural network developed for multimedia classification recently. The model is driven by the ideology of identifying the animal sound in the audio file by forcing the network to pay attention to core audio effect present in the audio to generate Mel-spectrogram. The designed framework achieves an accuracy of 98% while classifying the animal audio on weekly labelled datasets. The state-of-the-art in this research is to build a framework which could even run on the basic machine and do not necessarily require high end devices to run the classification