@MastersThesis{Kumar2003,
  author      = {Anurag Kumar},
  title       = {Estimation of abundance of blue whale calls off central california using a seafloor-mounted hydrophone},
  year        = {2003},
  comment     = {Blue whales have two alternating types of vocalizations that are frequently
	heard in our region: the “A” call, which is a pulsed, amplitude-modulated
	signal centered at 16.5 Hz, and a “B” call, which is a down-swept,
	frequency-modulated signal that sweeps from about 18 to 16.5 Hz (Rivers,
	1997).
	
	
	Auto-detection methods detect signals based on how similar they are
	in comparison with a reference signal. The performance of the detector
	is highly dependent on signal-to-noise ratio (SNR) and how well the
	reference signal matches the data.
	
	
	In the OAO recordings, the strongest component of the “A” call was
	the fourth harmonic, which centered around 90 Hz (Fig. 2). For the
	“B” calls the strongest, most consistently apparent component was
	the second harmonic, centered around 51 Hz (Fig. 2). The detectors
	were designed to exploit these components of the calls, and the frequency
	band spacing allowed for the reduction in the probability of falsely
	classifying by type. The fundamental frequency was not selected due
	to overlapping energies (“A” = 18Hz, “B” = 17Hz) which would increase
	the risk of falsely classifying by type.
	
	
	The false detection rate for this study was set low, 0.3%, to minimize
	error in call counts - which likely means that the probability of
	missing true calls was high!
	
	
	NEED TRANSLATION!: OAO recordings were conditioned before running
	the matched filter detector by band-passed filtering to remove out-of-band
	noise, base-banded, normalized, de-meaned, then down-sampled.
	
	
	To assess the performance of each auto-detection routine, the probability
	of detection was evaluated during all possible noise conditions.
	The effectiveness of detecting blue whale “A” and “B” calls using
	the matched filter detectors was assessed by visually reviewing 48
	hours of data for “A” calls and 120 hours for “B” calls, each containing
	various levels of ambient noise commonly found. Spectrograms (Fast
	Fourier Transform (FFT) with a sample duration of 0.5 – 1.0 seconds,
	a 96% overlap, and a Hanning shading window) of the data were visually
	scanned for the signals. The visual survey provided a ground truth
	to evaluate detector performance. The detectors were scored on the
	number of correct, missed, and false detections.
	
	
	Figure 7. The Receiver Operator Characteristics (ROC) for both the
	“A” and “B” call detectors. As SNR (Signal to Noise Ratio) increased
	so did the probability of detection. A fixed false detection rate,
	p(FD), of 0.3% was selected as the constant, therefore, the probability
	of detection determined by SNR.},
  file        = {Kumar2003.pdf:Kumar2003.pdf:PDF},
  owner       = {Tiago},
  school      = {College of Science and Mathematics, California State University, Fresno},
  subdatabase = {postdoc},
  timestamp   = {2007.09.10},
}

{"_id":"XcCNYhZBSTXfsPwgo","bibbaseid":"kumar-estimationofabundanceofbluewhalecallsoffcentralcaliforniausingaseafloormountedhydrophone-2003","authorIDs":[],"author_short":["Kumar, A."],"bibdata":{"bibtype":"mastersthesis","type":"mastersthesis","author":[{"firstnames":["Anurag"],"propositions":[],"lastnames":["Kumar"],"suffixes":[]}],"title":"Estimation of abundance of blue whale calls off central california using a seafloor-mounted hydrophone","year":"2003","comment":"Blue whales have two alternating types of vocalizations that are frequently heard in our region: the “A” call, which is a pulsed, amplitude-modulated signal centered at 16.5 Hz, and a “B” call, which is a down-swept, frequency-modulated signal that sweeps from about 18 to 16.5 Hz (Rivers, 1997). Auto-detection methods detect signals based on how similar they are in comparison with a reference signal. The performance of the detector is highly dependent on signal-to-noise ratio (SNR) and how well the reference signal matches the data. In the OAO recordings, the strongest component of the “A” call was the fourth harmonic, which centered around 90 Hz (Fig. 2). For the “B” calls the strongest, most consistently apparent component was the second harmonic, centered around 51 Hz (Fig. 2). The detectors were designed to exploit these components of the calls, and the frequency band spacing allowed for the reduction in the probability of falsely classifying by type. The fundamental frequency was not selected due to overlapping energies (“A” = 18Hz, “B” = 17Hz) which would increase the risk of falsely classifying by type. The false detection rate for this study was set low, 0.3%, to minimize error in call counts - which likely means that the probability of missing true calls was high! NEED TRANSLATION!: OAO recordings were conditioned before running the matched filter detector by band-passed filtering to remove out-of-band noise, base-banded, normalized, de-meaned, then down-sampled. To assess the performance of each auto-detection routine, the probability of detection was evaluated during all possible noise conditions. The effectiveness of detecting blue whale “A” and “B” calls using the matched filter detectors was assessed by visually reviewing 48 hours of data for “A” calls and 120 hours for “B” calls, each containing various levels of ambient noise commonly found. Spectrograms (Fast Fourier Transform (FFT) with a sample duration of 0.5 – 1.0 seconds, a 96% overlap, and a Hanning shading window) of the data were visually scanned for the signals. The visual survey provided a ground truth to evaluate detector performance. The detectors were scored on the number of correct, missed, and false detections. Figure 7. The Receiver Operator Characteristics (ROC) for both the “A” and “B” call detectors. As SNR (Signal to Noise Ratio) increased so did the probability of detection. A fixed false detection rate, p(FD), of 0.3% was selected as the constant, therefore, the probability of detection determined by SNR.","file":"Kumar2003.pdf:Kumar2003.pdf:PDF","owner":"Tiago","school":"College of Science and Mathematics, California State University, Fresno","subdatabase":"postdoc","timestamp":"2007.09.10","bibtex":"@MastersThesis{Kumar2003,\r\n author = {Anurag Kumar},\r\n title = {Estimation of abundance of blue whale calls off central california using a seafloor-mounted hydrophone},\r\n year = {2003},\r\n comment = {Blue whales have two alternating types of vocalizations that are frequently\r\n\theard in our region: the “A” call, which is a pulsed, amplitude-modulated\r\n\tsignal centered at 16.5 Hz, and a “B” call, which is a down-swept,\r\n\tfrequency-modulated signal that sweeps from about 18 to 16.5 Hz (Rivers,\r\n\t1997).\r\n\t\r\n\t\r\n\tAuto-detection methods detect signals based on how similar they are\r\n\tin comparison with a reference signal. The performance of the detector\r\n\tis highly dependent on signal-to-noise ratio (SNR) and how well the\r\n\treference signal matches the data.\r\n\t\r\n\t\r\n\tIn the OAO recordings, the strongest component of the “A” call was\r\n\tthe fourth harmonic, which centered around 90 Hz (Fig. 2). For the\r\n\t“B” calls the strongest, most consistently apparent component was\r\n\tthe second harmonic, centered around 51 Hz (Fig. 2). The detectors\r\n\twere designed to exploit these components of the calls, and the frequency\r\n\tband spacing allowed for the reduction in the probability of falsely\r\n\tclassifying by type. The fundamental frequency was not selected due\r\n\tto overlapping energies (“A” = 18Hz, “B” = 17Hz) which would increase\r\n\tthe risk of falsely classifying by type.\r\n\t\r\n\t\r\n\tThe false detection rate for this study was set low, 0.3%, to minimize\r\n\terror in call counts - which likely means that the probability of\r\n\tmissing true calls was high!\r\n\t\r\n\t\r\n\tNEED TRANSLATION!: OAO recordings were conditioned before running\r\n\tthe matched filter detector by band-passed filtering to remove out-of-band\r\n\tnoise, base-banded, normalized, de-meaned, then down-sampled.\r\n\t\r\n\t\r\n\tTo assess the performance of each auto-detection routine, the probability\r\n\tof detection was evaluated during all possible noise conditions.\r\n\tThe effectiveness of detecting blue whale “A” and “B” calls using\r\n\tthe matched filter detectors was assessed by visually reviewing 48\r\n\thours of data for “A” calls and 120 hours for “B” calls, each containing\r\n\tvarious levels of ambient noise commonly found. Spectrograms (Fast\r\n\tFourier Transform (FFT) with a sample duration of 0.5 – 1.0 seconds,\r\n\ta 96% overlap, and a Hanning shading window) of the data were visually\r\n\tscanned for the signals. The visual survey provided a ground truth\r\n\tto evaluate detector performance. The detectors were scored on the\r\n\tnumber of correct, missed, and false detections.\r\n\t\r\n\t\r\n\tFigure 7. The Receiver Operator Characteristics (ROC) for both the\r\n\t“A” and “B” call detectors. As SNR (Signal to Noise Ratio) increased\r\n\tso did the probability of detection. A fixed false detection rate,\r\n\tp(FD), of 0.3% was selected as the constant, therefore, the probability\r\n\tof detection determined by SNR.},\r\n file = {Kumar2003.pdf:Kumar2003.pdf:PDF},\r\n owner = {Tiago},\r\n school = {College of Science and Mathematics, California State University, Fresno},\r\n subdatabase = {postdoc},\r\n timestamp = {2007.09.10},\r\n}\r\n\r\n","author_short":["Kumar, A."],"key":"Kumar2003","id":"Kumar2003","bibbaseid":"kumar-estimationofabundanceofbluewhalecallsoffcentralcaliforniausingaseafloormountedhydrophone-2003","role":"author","urls":{},"downloads":0,"html":""},"bibtype":"mastersthesis","biburl":"http://distancelive.xyz/MainBibFile.bib","creationDate":"2020-06-16T14:23:34.785Z","downloads":0,"keywords":[],"search_terms":["estimation","abundance","blue","whale","calls","central","california","using","seafloor","mounted","hydrophone","kumar"],"title":"Estimation of abundance of blue whale calls off central california using a seafloor-mounted hydrophone","year":2003,"dataSources":["RjvoQBP8rG4o3b4Wi"]}