Ildar R. Urazghildiiev

Variability in ambient noise levels and call parameters of North Atlantic right whales in three habitat areas

The North Atlantic right whale inhabits the coastal waters off the east coasts of the United States and Canada, areas characterized by high levels of shipping and fishing activities. Acoustic communication plays an important role in the social behavior of these whales and increases in low-frequency noise may be leading to changes in their calling behavior. This study characterizes the ambient noise levels, including both natural and anthropogenic sources, and right whale upcall parameters in three right whale habitat areas. Continuous recordings were made seasonally using autonomous bottom-mounted recorders in the Bay of Fundy, Canada (2004, 2005), Cape Cod Bay, (2005, 2006), and off the coast of Georgia (2004-2005, 2006-2007). Consistent interannual trends in noise parameters were found for each habitat area, with both the band level and spectrum level measurements higher in the Bay of Fundy than in the other areas. Measured call parameters varied between habitats and between years within the same habitat area, indicating that habitat area and noise levels alone are not sufficient to predict variability in call parameters. These results suggest that right whales may be responding to the peak frequency of noise, rather than the absolute noise level in their environment.

Vehicle Classification Based on the Radar Measurement of Height Profiles

The problem of classifying road vehicles according to vehicle type is considered. The proposed solution is based on using vehicle height and length and height profiles obtained by a microwave (MW) radar sensor. We show that if the radar sensor satisfies certain requirements, then a precise feature vector can be extracted, and simple deterministic algorithms can be applied to determine the vehicle class. Field trials using a spread-spectrum MW radar sensor system operating on these principles have been carried out. They confirm that accurate classification of a large number of vehicle classes can be reached

Acoustic detection of North Atlantic right whale contact calls using the generalized likelihood ratio test

This paper addresses the problem of passive acoustic detection of contact calls produced by the highly endangered North Atlantic right whale Eubalaena glacialis. The proposed solution is based on using a generalized likelihood ratio test detector of polynomial-phase signals with unknown amplitude and polynomial coefficients observed in the presence of locally stationary Gaussian noise. The closed form representation for a minimal sufficient statistic is derived and a realizable detection scheme is developed. The receiver operation characteristic curves are calculated using empirical data recordings containing known right whale calls. The curves demonstrate that the proposed detector provides superior detection performance as compared with other known detection techniques for northern right whale contact calls.

Detection and Recognition of North Atlantic Right Whale Contact Calls in the Presence of Ambient Noise

In this paper, the problem of detecting and recognizing North Atlantic right whale (NARW), Eubalaena glacialis, contact calls in the presence of ambient noise is considered. A proposed solution is based on a multistage, hypothesis-testing technique that involves the generalized likelihood ratio test (GLRT) detector, spectrogram testing, and feature vector testing algorithms. The main contributions of this paper are the inclusion of noise kernels for signals likely to produce false alarms and a second stage classification algorithm which extracts parameters from candidate contact calls and constructs a scaled squared error statistic for parameters which lie outside the range of expected calls. Closed-form representations of the algorithms are derived and realizable detection schemes are developed. Test results show that the proposed technique is able to detect approximately 80% of the contact calls detected by the human operator with about 26 false alarms per 24 h of observation. Testing data set included 44 227 right whale contact calls detected by eight human operators who performed visual and aural inspection of the data spectrogram. Data were collected in different periods from March 2001 to February 2007, in Cape Cod Bay, Great South Channel, and in the coastal waters of Georgia.

Acoustic detection of North Atlantic right whale contact calls using spectrogram-based statistics

This paper considers the problem of detection of contact calls produced by the critically endangered North Atlantic right whale, Eubalaena glacialis. To reduce computational time, the class of acceptable detectors is constrained by the detectors implemented as a bank of two-dimensional linear FIR filters and using the data spectrogram as the input. The closed form representations for the detectors are derived and the detection performance is compared with that of the generalized likelihood ratio test (GLRT) detector. The test results demonstrate that in the presence of impulsive noise, the spectrogram-based detector using the French hat wavelet as the filter kernel outperforms the GLRT detector and decreases computational time by a factor of 6.

North Atlantic Right Whale acoustic signal processing: Part I. comparison of machine learning recognition algorithms

This paper compares three different approaches currently used in recognizing contact calls made from the North Atlantic Right Whale (NRW), Eubalaena glacialis. We present two new approaches consisting of machine learning algorithms based on artificial neural networks (NET) and the classification and regression tree classifiers (CART), and compare their performance with earlier work that employs multi-Stage feature vector testing (FVT) approach. A combined total of over 100,000 noise and NRW up-call events were used in the study. Calls were primarily recorded from two areas, Cape Cod Bay and Great South Channel. Of the three classifiers, the CART had the highest assignment rates, overall 86.45% with highest false positive rates (≪100 per hour). The FVT Method had exceptionally low false positive rates, with ≪50 per hour. However, it had an overall assignment rate less than the NET. The CART had statistically the same false positive rate as the NET with the highest assignment rates, 2.2% higher than the NET and 11.75% greater than the FVT Method. Details of the results are shown and extensions to the research are discussed.

Detection performances of experienced human operators compared to a likelihood ratio based detector

This paper compares the detection performances of experienced human operators and an automatic detector that is based on the generalized likelihood ratio test (GLRT). Test data consist of polynomial phase signals and additive white Gaussian noise with signal-to-noise-ratios of 18, 20, 24, and 24 dB. Test results demonstrate that, for a given human operator false alarm probability, the GLRT-based detector provided a higher probability of detecting signals than human operators.

North Atlantic right whale acoustic signal processing: Part II. improved decision architecture for auto-detection using multi-classifier combination methodology

Autonomous signal detection of the North Atlantic right whale (NRW), Eubalaena glacialis, is becoming an important factor in monitoring and conservation for this highly endangered species. Both online and offline systems exist to help study and protect animals within this population. In both cases auto-detection of species-specific calls plays a vital role in localizing individual animal by searching time-frequency passive acoustic data. This research presents an experimental system, referred to as the NRW-CRITIC, for automatic detection of the NRW contact call. In general, the CRITIC uses a combinatorial classifier approach to integrate a series of existing machine learning algorithms; each designed specifically for NRW contact call identification. The proposed configuration consists of several recognition methods running in parallel; these include linear discriminant analysis, artificial neural network (NET) and classification regression tree (CART). This paper presents the details for the NRW-CRITIC and discusses the approach used to combine multiple independent decisions into a single result. A side-by-side performance comparison, between the CRITIC and a well-known method, the feature vector testing (FVT), is summarized. Performance metrics are evaluated based on a large database of acoustic recordings consisting of over 58,000 NRW contact calls from various locations, including two critical habitats, Great South Channel and Cape Cod Bay. Results indicate the FVT algorithm yields a 74.7% detection probability with an error rate of 4.35%. In comparison the CRITIC, operating at similar information level yields a 78.02% detection probability with a 3.25% error rate, exceeding the performance of the FVT. Performance was also measured using data from a multi-channel acoustic array located in Massachusetts Bay. A side-by-side comparison of array presence is discussed for two separate days. Results show that with the FVT and CRITIC operating at 0% error for array presence, the FVT method had 18,769 and 24,469 false positives for the Massachusetts Bay datasets respectively. With the same 0% error condition the CRITIC provided successful detection with significantly lower number of false positive rates: 1,072 and 2,324 calls, respectively. Future extensions of this experimental work are also discussed.

High-Resolution Estimation of Ranges Using Multiple-Frequency CW Radar

The problem of ranging multiple vehicles traveling at similar speeds using multiple-frequency continuous-wave (CW) radar is considered. A new method based on the compensation of vehicle movement and MUSIC-based time delay estimator is presented. The method is tested using a commercially available multiple-frequency CW radar sensor in real traffic situations. Test results show that the proposed method makes it possible to estimate the positions of two vehicles moving at similar speeds. Test results also demonstrate that under a given bandwidth, the multiple-frequency CW radar can provide about ten times higher resolution as compared with the coherent spread spectrum radar

Acoustic detection and recognition of fin whale and North Atlantic right whale sounds

The problem of detecting and recognizing the sounds of fin whales, Balaenoptera physalus, and North Atlantic right whales, Eubalaena glacialis, in the presence ambient noise is considered. A proposed solution is based on a multiple-stage hypothesis-testing technique. The closed form representations of the algorithms are derived, and realizable detection schemes are developed. Empirical tests were conducted using data recordings collected in 2007 off the coast of Massachusetts. Results reveal that the proposed technique is able to detect approximately 80% of the calls detected by the human operator and to produce an average of 12.0 - 33.5 false alarms per 24 h of observation.