Analysis of underwater mammal vocalisations using time–frequency-phase tracker

Ioana, Cornel; Gervaise, Cédric; Stéphan, Y.; Mars, Jérôme

doi:10.1016/j.apacoust.2010.04.009

Cited by 22 publications

(16 citation statements)

References 11 publications

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“…Possibly because of this, most detection algorithms still fall short of the performance required for routine usage. Some researchers have moved away from the spectrographic paradigm for detection (e.g., Ioana et al, 2010;Johansson and White, 2011;Ou et al, 2012), but spectrographic representation undoubtedly provides a parsimonious encoding of the detailed frequency variation. Therefore when the goal is the accurate characterization of the frequency modulation of tonal signals rather than signal detection, there is a clear benefit to extract that information from the Fourier transformed signal.…”

Section: Discussionmentioning

confidence: 99%

“…In other words, two joined maxima should form sequential points in a true whistle, rather than being arbitrary noise that is coincidentally correlated in time and frequency. Various techniques have been proposed for this including Kalman filtering (Mallawaarachchi, 2008a), heuristic rules (Mellinger et al, 2011), phase tracking (Ioana et al, 2010; Johannson and White 2011), particle filters (Roch et al, 2011;White and Hadley, 2008), and graphbased techniques (Roch et al, 2011). Some of these algorithms produce good detection rates that may be useful in the ecological assessment of cetacean populations (e.g., Marques et al, 2009).…”

Section: Introductionmentioning

confidence: 98%

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An image processing based paradigm for the extraction of tonal sounds in cetacean communications

Kershenbaum

Roch

2013

The Journal of the Acoustical Society of America

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Dolphins and whales use tonal whistles for communication, and it is known that frequency modulation encodes contextual information. An automated mathematical algorithm could characterize the frequency modulation of tonal calls for use with clustering and classification. Most automatic cetacean whistle processing techniques are based on peak or edge detection or require analyst assistance in verifying detections. An alternative paradigm is introduced using techniques of image processing. Frequency information is extracted as ridges in whistle spectrograms. Spectral ridges are the fundamental structure of tonal vocalizations, and ridge detection is a well-established image processing technique, easily applied to vocalization spectrograms. This paradigm is implemented as freely available matlab scripts, coined IPRiT (image processing ridge tracker). Its fidelity in the reconstruction of synthesized whistles is compared to another published whistle detection software package, silbido. Both algorithms are also applied to real-world recordings of bottlenose dolphin (Tursiops trunactus) signature whistles and tested for the ability to identify whistles belonging to different individuals. IPRiT gave higher fidelity and lower false detection than silbido with synthesized whistles, and reconstructed dolphin identity groups from signature whistles, whereas silbido could not. IPRiT appears to be superior to silbido for the extraction of the precise frequency variation of the whistle.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

An image processing based paradigm for the extraction of tonal sounds in cetacean communications

Kershenbaum

Roch

2013

The Journal of the Acoustical Society of America

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“…Adam (2008) extracted calls of killer whales using the Hilbert-Huang transform. Ioana et al (2010) proposed a method to extract tonals when the signal's phase track can be approximated by a polynomial. The strongest signal is estimated by the product highorder ambiguity function (Barbarossa et al, 1998), subtracted, and the process is iterated to find the next strongest signal.…”

Section: Introductionmentioning

confidence: 99%

Automated extraction of odontocete whistle contours

Roch

Brandes

Patel

et al. 2011

The Journal of the Acoustical Society of America

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Many odontocetes produce frequency modulated tonal calls known as whistles. The ability to automatically determine time × frequency tracks corresponding to these vocalizations has numerous applications including species description, identification, and density estimation. This work develops and compares two algorithms on a common corpus of nearly one hour of data collected in the Southern California Bight and at Palmyra Atoll. The corpus contains over 3000 whistles from bottlenose dolphins, long- and short-beaked common dolphins, spinner dolphins, and melon-headed whales that have been annotated by a human, and released to the Moby Sound archive. Both algorithms use a common signal processing front end to determine time × frequency peaks from a spectrogram. In the first method, a particle filter performs Bayesian filtering, estimating the contour from the noisy spectral peaks. The second method uses an adaptive polynomial prediction to connect peaks into a graph, merging graphs when they cross. Whistle contours are extracted from graphs using information from both sides of crossings. The particle filter was able to retrieve 71.5% (recall) of the human annotated tonals with 60.8% of the detections being valid (precision). The graph algorithm's recall rate was 80.0% with a precision of 76.9%.

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“…There exist a variety of types of TFA methods, and they can be normally divided into two categories: the parametric TFA (PTFA) methods and the nonparametric TFA (NPTFA) methods. PTFA methods, such as polynomial [1,15], spline-kernelled chirplet transform (SCT) [16], and sinusoidal models [17], often involve the high-dimensional search of the IFs, which is very time consuming. Moreover, the predesigned parametric models may be only suitable for special applications.…”

Section: Related Workmentioning

confidence: 99%

“…To recover the expected components, we need to deal with nonlinear equations in (15). Since this inverse problem is commonly ill-posed, the kernel sparse learning is employed to process real data.…”

Section: Decomposition By Kernel Sparse Learning One Can Rewrite Equmentioning

confidence: 99%

Intrinsic Mode Chirp Multicomponent Decomposition with Kernel Sparse Learning for Overlapped Nonstationary Signals Involving Big Data

Sun

Miao

2018

Complexity

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We focus on the decomposition problem for nonstationary multicomponent signals involving Big Data. We propose the kernel sparse learning (KSL), developed for the T-F reassignment algorithm by the path penalty function, to decompose the instantaneous frequencies (IFs) ridges of the overlapped multicomponent from a time-frequency representation (TFR). The main objective of KSL is to minimize the error of the prediction process while minimizing the amount of training samples used and thus to cut the costs interrelated with the training sample collection. The IFs first extraction is decided using the framework of the intrinsic mode polynomial chirp transform (IMPCT), which obtains a brief local orthogonal TFR of signals. Then, the IFs curves of the multicomponent signal can be easily reconstructed by the T-F reassignment. After the IFs are extracted, component decomposition is performed through KSL. Finally, the performance of the method is compared when applied to several simulated micro-Doppler signals, which shows its effectiveness in various applications.

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Analysis of underwater mammal vocalisations using time–frequency-phase tracker

Cited by 22 publications

References 11 publications

An image processing based paradigm for the extraction of tonal sounds in cetacean communications

An image processing based paradigm for the extraction of tonal sounds in cetacean communications

Automated extraction of odontocete whistle contours

Intrinsic Mode Chirp Multicomponent Decomposition with Kernel Sparse Learning for Overlapped Nonstationary Signals Involving Big Data

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