Ambient Noise in the Kitimat Fjord System

Sound is an important medium for communication and marine organisms have evolved to capitalize on the efficiency with which sound energy travels through water. Anthropogenic and natural sound sources contribute to ocean ambient noise, which can interfere with the use of this sensory modality by marine animals. Anthropogenic noise sources have been increasing steadily over recent decades largely due to coastal population growth, increased global transportation, and offshore industrialization. Understanding the potential impacts of anthropogenic noise requires the establishment of ambient acoustic baselines from which to measure change. Establishing baselines, especially in quiet areas still largely unaffected by anthropogenic stressors, is particularly crucial in the face of the expansion of offshore industries, increasing coastal population and growing reliance on the ocean for global transportation. Global demand for liquid natural gas (LNG), catalyzed primarily by a growing Asian market, is expected to increase significantly in the next 20 years. The geographic position of British Columbia relative to these markets, a growing supply of LNG and new technology for extraction and shipping situate British Columbia as a strong competitor in the lucrative market. The LNG industry could have many adverse impacts on these territories and ecosystems. The Kitimat Fjord System is slated for the development of these LNG export facilities increasing shipping traffic for the port and thus increasing ambient noise in the fjord system. The purpose of this study is to 1) quantify the existing sound levels in the area surrounding Gil Island and 2) identify potential source mechanisms in order to provide a baseline study of the acoustic environment in the Kitimat Fjord system prior to potential increases from LNG shipping.

Soundscape Ecology of Hawaiian Spinner Dolphin Resting Bays

Sound is a key sensory modality for Hawaiian spinner dolphins. Like many other marine animals, these dolphins rely on sound and their acoustic environment for many aspects of their daily lives, making it is essential to understand soundscape in areas that are critical to their survival. Hawaiian spinner dolphins rest during the day in shallow coastal areas and forage offshore at night. In my dissertation I focus on the soundscape of the bays where Hawaiian spinner dolphins rest taking a soundscape ecology approach. I primarily relied on passive acoustic monitoring using four DSG-Ocean acoustic loggers in four Hawaiian spinner dolphin resting bays on the Kona Coast of Hawai‛i Island. 30-second recordings were made every four minutes in each of the bays for 20 to 27 months between January 8, 2011 and March 30, 2013. I also utilized concomitant vessel-based visual surveys in the four bays to provide context for these recordings. In my first chapter I used the contributions of the dolphins to the soundscape to monitor presence in the bays and found the degree of presence varied greatly from less than 40% to nearly 90% of days monitored with dolphins present. Having established these bays as important to the animals, in my second chapter I explored the many components of their resting bay soundscape and evaluated the influence of natural and human events on the soundscape. I characterized the overall soundscape in each of the four bays, used the tsunami event of March 2011 to approximate a natural soundscape and identified all loud daytime outliers. Overall, sound levels were consistently louder at night and quieter during the daytime due to the sounds from snapping shrimp. In fact, peak Hawaiian spinner dolphin resting time co-occurs with the quietest part of the day. However, I also found that humans drastically alter this daytime soundscape with sound from offshore aquaculture, vessel sound and military mid-frequency active sonar. During one recorded mid-frequency active sonar event in August 2011, sound pressure levels in the 3.15 kHz 1/3rd-octave band were as high as 45.8 dB above median ambient noise levels. Human activity both inside (vessels) and outside (sonar and aquaculture) the bays significantly altered the resting bay soundscape. Inside the bays there are high levels of human activity including vessel-based tourism directly targeting the dolphins. The interactions between humans and dolphins in their resting bays are of concern; therefore, my third chapter aimed to assess the acoustic response of the dolphins to human activity. Using days where acoustic recordings overlapped with visual surveys I found the greatest response in a bay with dolphin-centric activities, not in the bay with the most vessel activity, indicating that it is not the magnitude that elicits a response but the focus of the activity. In my fourth chapter I summarize the key results from my first three chapters to illustrate the power of multiple site design to prioritize action to protect Hawaiian spinner dolphins in their resting bays, a chapter I hope will be useful for managers should they take further action to protect the dolphins.

Detection and Classification of Whale Acoustic Signals

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.

In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.

In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.

Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.

We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.