Conventional detection of humpback vocalizations is often based on frequency summation of band-limited spectrograms under the assumption that energy (square of the Fourier amplitude) is the appropriate metric. Power-law detectors allow for a higher power of the Fourier amplitude, appropriate when the signal occupies a limited but unknown subset of these frequencies. Shipping noise is non-stationary and colored and problematic for many marine mammal detection algorithms. Modifications to the standard power-law form are introduced to minimize the effects of this noise. These same modifications also allow for a fixed detection threshold, applicable to broadly varying ocean acoustic environments. The detection algorithm is general enough to detect all types of humpback vocalizations. Tests presented in this paper show this algorithm matches human detection performance with an acceptably small probability of false alarms (PFA < 6%) for even the noisiest environments. The detector outperforms energy detection techniques, providing a probability of detection PD = 95% for PFA < 5% for three acoustic deployments, compared to PFA > 40% for two energy-based techniques. The generalized power-law detector also can be used for basic parameter estimation and can be adapted for other types of transient sounds.

1.
R.
Payne
and
S.
McVay
, “
Songs of humpback whales
,”
Science
173
,
585
597
(
1971
).
2.
S.
Cerchio
,
J.
Jacobsen
, and
T.
Norris
, “
Temporal and geographical variation in songs of humpback whales, Megaptera novaeangliae: Synchronous change in Hawaiian and Mexican breeding assemblages
,”
Anim. Behav.
62
,
313
329
(
2001
).
3.
D.
Mellinger
and
C.
Clark
, “
Recognizing transient low-frequency whale sounds by spectrogram correlation
,”
J. Acoust. Soc. Am.
107
,
3518
3529
(
2000
).
4.
J.
Potter
,
D.
Mellinger
, and
C.
Clark
, “
Marine mammal call discrimination using artificial neural networks
,”
J. Acoust. Soc. Am.
96
,
1255
1262
(
1994
).
5.
J.
Brown
and
P.
Smaragdis
, “
Hidden Markov and Gaussian mixture models for automatic call classification
,”
J. Acoust. Soc. Am.
125
,
EL221
EL224
(
2009
).
6.
P.
Rickwood
and
A.
Taylor
, “
Methods for automatically analyzing humpback song units
,”
J. Acoust. Soc. Am.
123
,
1763
1772
(
2008
).
7.
X.
Mouy
,
M.
Bahoura
, and
Y.
Simard
, “
Automatic recognition of fin and blue whale calls for real-time monitoring in the St. Lawrence
,”
J. Acoust. Soc. Am.
126
,
2918
2928
(
2009
).
8.
T.
Abbot
,
V.
Premus
, and
P.
Abbot
, “
A real-time method for autonomous passive acoustic detection-classification of humpback whales
,”
J. Acoust. Soc. Am.
127
,
2894
2903
(
2010
).
9.
D.
Mellinger
, “
Ishmael 1.0 users guide
,” NOAA Technical Memorandum OAR PMEL-120, available from NOAA/PMEL, Seattle, WA (
2001
).
10.
H.
Figueroa
,
xbat, Version 5
(
Cornell University Bioacoustics Research Program
,
2007
).
11.
D.
Gillespie
,
D.
Mellinger
,
J.
Gordon
,
D.
McLaren
,
P.
Redmond
,
R.
McHugh
,
P.
Trinder
,
X.
Deng
, and
A.
Thode
, “
pamguard: Semiautomated, open source software for real-time acoustic detection and localization of cetaceans
,”
J. Acoust. Soc. Am.
125
,
2547
2547
(
2009
).
12.
C.
Erbe
and
A.
King
, “
Automatic detection of marine mammals using information entropy
,”
J. Acoust. Soc. Am.
124
,
2833
2840
(
2008
).
13.
A.
Nuttall
, “
Detection performance of power-law processors for random signals of unknown location, structure, extent, and strength
,” NUWC-NPT Technical Report, Newport, RI (
1994
).
14.
A.
Nuttall
, “
Near-optimum detection performance of power-law processors for random signals of unknown locations, structure, extent, and arbitrary strengths
,” NUWC-NPT Technical Report, Newport, RI (
1996
).
15.
S.
Wiggins
, “
Autonomous Acoustic Recording Packages (ARPs) for long-term monitoring of whale sounds
,”
Marine Tech. Soc. J.
37
,
13
22
(
2003
).
16.
S.
Wiggins
,
M.
Roch
, and
J.
Hildebrand
, “
Triton software package: Analyzing large passive acoustic monitoring data sets using matlab
,”
J. Acoust. Soc. Am.
128
,
2299
2299
(
2010
).
17.
Z.
Wang
and
P.
Willett
, “
All-purpose and plug-in power-law detectors for transient signals
,”
IEEE Trans. Signal Process.
49
,
2454
2466
(
2001
).
18.
A.
Stuart
and
K.
Ord
,
Kendall’s Advanced Theory of Statistics. Distribution Theory
(
Wiley
,
New York
,
2009
), Vol.
1
, Chaps. 1–5, 8–11.
19.
W.
Struzinski
and
E.
Lowe
, “
A performance comparison of four noise background normalization schemes proposed for signal detection systems
,”
J. Acoust. Soc. Am.
76
,
1738
1742
(
1984
).
20.
R.
Charif
,
C.
Clark
, and
K.
Fristrup
(
2004
), “
Raven 1.2 users manual, Appendix B: A Biologists Introduction to Spectrum Analysis
,”
Cornell Laboratory of Ornithology
,
Ithaca
,
New York
.
21.
M.
Beecher
, “
Spectrographic analysis of animal vocalizations: Implications of the uncertainty principle
,”
Bioacoustics
1
,
187
208
(
1988
).
22.
R.
Lowdermilk
and
F.
Harris
,
“Using the FFT as an arbitrary function generator,”
in
Proceedings of AUTOTESTCON (2005) (IEEE)
, pp.
408
412
.
23.
S.
Kay
,
Fundamentals of Statistical Signal Processing: Detection Theory
(
Prentice-Hall
,
Englewood Cliffs, NJ
,
1998
), Vol.
2
, pp.
61
, 74, 269.
24.
A.
Martin
,
G.
Doddington
,
T.
Kamm
,
M.
Ordowski
, and
M.
Przybocki
,
“The DET curve in assessment of detection task performance,”
in
Proceedings of Eurospeech
, Rhodes, Greece (
1997
), Vol.
97
, pp.
1895
1898
.
25.
R.
Nielsen
,
Sonar Signal Processing
(
Artech House, Inc.
,
Norwood, MA
,
1991
), pp.
145
147
.
You do not currently have access to this content.