Antarctic Blue Whale calls detection based on an improved version of the stochastic matched filter

Bouffaut, Léa; Dreo, Richard; Labat, Valérie; Boudraa, Abdel-Ouahab; Barruol, Guilhem

doi:10.23919/eusipco.2017.8081624

Cited by 5 publications

(10 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It consists in calculating the eigenelements of , projecting the measured data onto each eigenvectors and summing ≥ 1 powers of these terms. [2,3,4].…”

Section: A the Smfmentioning

confidence: 99%

The Multicriteria Constrained Stochastic Matched Filter For Underwater Bioacoustic Signals

Xerri

Borloz

Chagmani

2019

OCEANS 2019 - Marseille

View full text Add to dashboard Cite

The aim of this paper is the detection of a bioacoustic signal embedded in several noises such as sea noise and other bioacoustic signals (dolphins, sperm whales). All the signals are real world signals.Only second order statistics are use through the estimated correlation matrices of the signals. This paper proposes an extension of the Constrained Stochastic Matched Filter (CSMF) based on the optimization of the Signal to Noise Ratio after linear filtering. The approach proposed is a multicriteria one, merging three different versions of the CSMF, and is named Multicriteria CSMF (MCSMF).The objective is that the results obtained are better than the other methods, or at least equal to the best among the three.The results are provided on ROC curves and the method is compared to the classical method Stochastic Matched Filter (SMF).

show abstract

“…It consists in calculating the eigenelements of , projecting the measured data onto each eigenvectors and summing ≥ 1 powers of these terms. [2,3,4].…”

Section: A the Smfmentioning

confidence: 99%

The Multicriteria Constrained Stochastic Matched Filter For Underwater Bioacoustic Signals

Xerri

Borloz

Chagmani

2019

OCEANS 2019 - Marseille

View full text Add to dashboard Cite

show abstract

“…First results and realdata application are presented in Ref. 13, but with no performance assessments.…”

Section: Detection Strategymentioning

confidence: 99%

“…Although it is quite convenient for supervised detection on relatively short records, it becomes tedious and unpractical for automatic detection on several-hours-long passive acoustic monitoring datasets with highly varying background noise. Consequently, the following development deals with finding a way to blindly take into account noise variations despite frequency dependence and high energy events occurrence, to perform accurate estimation of C N 0 N 0 even in presence of the signal of interest S. 13 The strategy for the noise's covariance matrix estimation relies on time-frequency analysis. As for a spectrogram, the observation is segmented in time using weighted overlapping windows.…”

Section: Online Noise's Covariance Matrix Estimationmentioning

confidence: 99%

“…The Antarctic blue whale (ABW) stereotyped call (named Z-call) occurring in the [18-26.8] Hz bandwidth, is a good candidate for automatic detection. 6,[11][12][13] However, numerous sound sources overlap in the same frequency band such as high energy noises due to seismic activity or ship radiated tonal noises. 14 Passive acoustic monitoring automatic detection strategies are usually based on signal cross-correlation functions such as temporal MFs 12 or spectrogram-based template matching.…”

Section: Introductionmentioning

confidence: 99%

“…Preliminary results on ABW call detection and scuba-divers breathing detection have illustrated the potential of this filter at low SNR, with various types of noises. 13 The work presented in this paper aims to evaluate the SMF performances with strong SNR variations. It is realized using a ground truth dataset, where a set of 845 Z-calls are manually annotated by an expert.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Passive stochastic matched filter for Antarctic blue whale call detection

Bouffaut

Dreo

Labat

et al. 2018

The Journal of the Acoustical Society of America

Self Cite

View full text Add to dashboard Cite

As a first step to Antarctic blue whale (ABW) monitoring using passive acoustics, a method based on the stochastic matched filter (SMF) is proposed. Derived from the matched filter (MF), this filter-based denoising method enhances stochastic signals embedded in an additive colored noise by maximizing its output signal to noise ratio (SNR). These assumptions are well adapted to the passive detection of ABW calls where emitted signals are modified by the unknown impulse response of the propagation channel. A filter bank is computed and stored based on knowledge of the signal second order statistics and simulated colored sea-noise. Then, the detection relies on background noise and SNR estimation, realized using time-frequency analysis. The SMF output is cross-correlated with the signal's reference (SMF + MF). Its performances are assessed on an ccean bottom seismometer-recorded ground truth dataset of 845 ABW calls, where the location of the whale is known. This dataset provides great SNR variations in diverse soundscapes. The SMF + MF performances are compared to the commonly used MF and to the Z-detector (a sub-space detector for ABW calls). Mostly, the benefits of the use of the SMF + MF are revealed on low signal to noise observations: in comparison to the MF with identical detection threshold, the false alarm rate drastically decreases while the detection rate stays high. Compared to the Z-detector, it allows the extension of the detection range of 30 km in presence of ship noise with equivalent false discovery rate.

show abstract

Baleen whale distribution and seasonal occurrence revealed by an ocean bottom seismometer network in the Western Indian Ocean

Dreo

Bouffaut

Leroy

et al. 2019

Deep Sea Research Part II: Topical Studies in Oceanography

Self Cite

View full text Add to dashboard Cite

From the acoustic data acquired by the RHUM-RUM (Réunion Hotspot and Upper Mantle Réunions Unterer Mantel) Ocean Bottom Seismometer (OBS) network between October 2012 and November 2013, this study revealed baleen whale occurrence in the western Indian Ocean (IO). Low-frequency songs from three species (Antarctic Blue Whales, Pygmy Blue Whales and Fin Whales) as well as P-calls (or Spot-calls) from an unknown species were recorded on the dataset. The wide arrangement of the OBS network (2000 km × 2000 km) provided valuable information to draw seasonal patterns of occurrence and distribution all over the area. These species occurred sympatrically in the western IO, at least during austral autumn months emphasizing the importance of this region for these populations. This data set helped to refine the knowledge on their spatio-temporal distribution and complete the picture built by previous studies. A tighter sub-network of 8 OBSs deployed on the South West Indian Ridge provided ideal inter-sensor spacing for whale tracking. We demonstrated the capability of such array of detecting and tracking the three different whale species up to 50 km and for several hours. As a result and to understand the effect of acoustic wave propagation, songs from the tracking were described at a close and remote distance of the sensor. This work could also help to understand the local behavior of these species during austral autumn months in this area of the western Indian Ocean.

show abstract

Antarctic Blue Whale calls detection based on an improved version of the stochastic matched filter

Cited by 5 publications

References 13 publications

The Multicriteria Constrained Stochastic Matched Filter For Underwater Bioacoustic Signals

The Multicriteria Constrained Stochastic Matched Filter For Underwater Bioacoustic Signals

Passive stochastic matched filter for Antarctic blue whale call detection

Baleen whale distribution and seasonal occurrence revealed by an ocean bottom seismometer network in the Western Indian Ocean

Contact Info

Product

Resources

About