Bioacoustic Signals Research Articles

Passive‐acoustic monitoring of odontocetes using a Seaglider: First results of a field test in Hawaiian waters.

In fall 2009 the University of Washington, Applied Physics Laboratory conducted in collaboration with the Oregon State University, a comprehensive field test of a passive‐acoustic Seaglider along the western shelf‐break of the island of Hawaii. During the 3 week mission, a total of approximately 170 h of broadband acoustic data [194 kHz sampling rate] were collected. The recordings were manually analyzed by an experienced analyst for beaked whale (Ziphiidae), dolphin (Delphinidae), and sperm whale (Physeter macrocephalus) echolocation clicks as well as echo sounder pings emitted by boats in the area. Here we present and discuss first results of these data analysis, which revealed that more than 50% of the recorded files (each of 1‐minute duration) contain bioacoustic signals. Furthermore the recorded data and the results of the manual analysis are used to validate and optimize an automated classifier for odontocete echolocation clicks, which was developed in a collaborative effort with San Diego State University. The algorithm is intended to be implemented on the Seaglider to enable species identification by classifying detected echolocation clicks in (near) real‐time during sea trials. [This work is funded by the Office of Naval Research and the U.S. Navy’s Environmental Readiness Division.]

Acoustic classification of Australian anurans based on hybrid spectral-entropy approach

A new hybrid method for automated frog sound identification, using spectral centroid, Shannon entropy and Rényi entropy is proposed. The advantage of using entropy based information theoretic approach for analyzing complexity of bioacoustics signals in animal vocalization is discussed. Sound samples from nine species of Microhylidae frogs are first segmented into syllables. Fourier spectral centroid, Shannon entropy and Rényi entropy of the syllables are then determined. Finally, nonparametric k-th nearest neighbour ( k-NN) classifier is used to recognize the frog species based on these three extracted features. Result shows that the k-NN classifier based on these selected features is capable to identify the species of the frogs with an average accuracy of 98%. It is found that the accuracy reduces significantly only when the noise levels higher than −20 dB.

A review and inventory of cabled and radio-linked hydrophones for passive acoustic monitoring of marine mammals.

Fixed cabled hydrophone (FCH) and radio-linked hydrophone (RLH) systems are permanently, or semi-permanently, installed acoustic monitoring systems that are located on or moored to the seafloor. These systems have the capability to passively monitor bio-acoustic signals from marine mammals and, therefore, have great potential for monitoring and mitigation of potential impacts caused by anthropogenic activities. As part of a Joint Industries Programme sponsored effort, we reviewed past and present FCH and RLH systems with respect to their capabilities, advantages/disadvantages, and effectiveness for monitoring marine mammals in relation to oil and gas exploration/production activities. Based on this review, we provide examples and applications of these technologies. FCHs are typically powered by an external source and send data continuously to a receiving station that is usually located on shore. RLHs are moored or fixed to the seafloor, and transmit acoustic signals via radio-waves to a receiving station on shore. Both these systems allow acoustic data to be remotely monitored and processed in (or near) real-time. Hybrid systems can offer a good compromise between cost and capability by providing near real-time data transmission/processing with greater flexibility in deployment possibilities, but are usually limited in longevity and bandwidth of monitoring.

Probabilistic bioacoustic signal extraction within spectrograms.

A methodology for probabilistic bioacoustic signal extraction within spectrograms of natural sound recordings is proposed. Probabilistic models of signal attributes are described for generating a host of likelihoods used to estimate the probability that individual pixels within a spectrogram represent part of a bioacoustic signal. The pixel probabilities result in a transformation of the spectrogram into a probability map of bioacoustic signal presence. It is shown that these probability maps create a dramatic increase in the bioacoustic signal-to-noise ratio within the spectrogram. These probability maps along with threshold filtering provide a means for image segmentation of the spectrogram, creating blocks of pixels that represent bioacoustic signals, facilitating feature and signal extraction. This methodology is applied to natural sound recordings of three quality types in a wide range of signal-to-noise ratios. In each instance, the probability mapping greatly increases the signal-to-noise ratio and, when applied as a threshold filter, is far more selective with pixel inclusion than threshold filtering applied based on sound level. Suggested applications include automated call recognition of birds, frogs, and insects from field recordings within a wide range of ambient noise.

Screening large data sets and real‐time data streams for bioacoustic signals.

This presentation will discuss a major challenge of passive‐acoustic monitoring systems: the analysis of large data sets to identify the occurrence of bioacoustic signals of interest. Triggered by the rapid development of digital audio technology and the increasing capacity of memory devices, it has become easier than ever to produce large, long‐term acoustic data sets. However, analyzing these data sets is challenging, as suitable automated detection and classification systems are needed to perform the analysis in a reasonable amount of time. One such large data set is currently produced by the Perennial Acoustic Observatory in the Antarctic Ocean. This passive acoustic observatory has been operated by the Alfred Wegener Institute since January 2006 and has generated more than 10,000 hours of data to date. The observatory features a data link via satellite, which allows analysis of the recorded hydroacoustic data in real time in Germany. However, to be able to run custom algorithms over the entire data set in several times real‐time speed, a distributed computing system was developed and applied. Here we provide a detailed description of this system and discuss further possible applications.

Distinguishing FM‐modulated bowhead whale calls from airgun and other biologic signals using image processing, feature extraction, and neural networks.

The detection and identification of repetitive or stereotyped bioacoustic signals in raw acoustic data are typically accomplished via matched filtering or spectrogram correlation techniques. However, the detection of FM‐modulated sweeps that have variable frequency‐modulated contours or bandwidths is still an active challenge. The problem is made more complex by the possible presence of other frequency‐modulated signals such as airguns or other biologics. To provide a specific example, in 2007 and 2008 directional autonomous recording packages (DASARs) were deployed in the Beaufort Sea to monitor the annual migration of the bowhead whale (Balaena mysticetus) population during seismic exploration activities. A portion of these data sets has been manually analyzed, providing an opportunity to characterize the miss and false alarm rates of various FM‐modulated detection and classification methods. Here literature and software on bioacoustic FM‐contour tracing and classification are reviewed, with a particula...

Entropy estimation using pattern matching in bioacoustic signals.

Many bioacoustic signals consist of a sequence of discrete stereotyped sounds occurring in repeated patterns. A natural question to ask is how best to characterize the underlying structure of the source producing the sequence of sounds. The structure of the source manifests itself as constraints on the patterns observed in the sequence of sounds. These constraints determine how predictable the order of the sounds is. The information entropy of a discrete symbol sequence is a quantitative measure of how unpredictable the sequence is. A straightforward but biased technique for estimating the entropy of an unknown source is to substitute observed symbol frequencies into parametric models such as Markov models. More general nonparametric entropy estimators exploit the relationship between the entropy and the average length of matching patterns within the sequence. This nonparametric entropy estimate forms an upper bound on the amount of information conveyed by the sequence of sounds. Additionally, comparing entropy estimates from the parametric and nonparametric models provides a hypothesis test determining whether the parametric model sufficiently captures the constraints of the source. These techniques are illustrated in analyses of humpback whale songs and leopard seal calling bouts.

A Texture-Based Classification of Crackles and Squawks Using Lacunarity

An automatic classification method to efficiently discriminate the types of discontinuous breath sounds (DBSs), i.e., fine crackles (FCs), coarse crackles (CC), and squawks (SQ), is presented in this paper. Using the lacunarity of the acquired DBS, the proposed classification method, namely LAC, introduces a texture-based approach that captures the differences in the distribution of FC, CC, and SQ across the breathing cycle, which may lead to more accurate characterization of the pulmonary acoustical changes due to the related pathology. Prior to the lacunarity analysis, wavelet-based denoising of DBS is employed to eliminate effects of the vesicular sound (background noise) to DBS oscillatory pattern. LAC analysis builds its classification power both upon the use of lacunarity at an optimum scale and the approximation of its trajectory across an optimum range of scales using a three-parameter hyperbola model. LAC is applied to 363 DBS corresponding to 25 cases included in four lung sound databases. Results show that LAC efficiently classifies the three DBS categories in the comparison groups of FC-CC, FC-SQ (both with mean accuracy of 100%), CC-SQ (mean accuracy of 99.62%-100%), and FC-CC-SQ (mean accuracy of 99.75%-100%). When compared to other classification tools, LAC seems quite attractive, since, without employing high computational complexity, it results in high classification accuracy. Moreover, LAC introduces a "texture" concept in the analysis of breath sounds, something that strongly relates to the perception of the bioacoustic signals by the physician. Due to its simplicity, LAC could be implemented in a real-time context and be used in clinical medicine as a module of an integrated intelligent patient evaluation system.

Feature Vector Selection and Use With Hidden Markov Models to Identify Frequency-Modulated Bioacoustic Signals Amidst Noise

This paper describes an effective process for automated detection and classification of frequency-modulated sounds from birds, crickets, and frogs that have a narrow short-time frequency bandwidth. An algorithm is provided for extracting these signals from background noise using a frequency band threshold filter on spectrograms. Feature vectors are introduced and demonstrated to accurately model the resultant bioacoustic signals with hidden Markov models. Additionally, sequences of sounds are successfully modeled with composite hidden Markov models, allowing for a wider range of automated species recognition.

Perceptually motivated wavelet packet transform for bioacoustic signal enhancement

A significant and often unavoidable problem in bioacoustic signal processing is the presence of background noise due to an adverse recording environment. This paper proposes a new bioacoustic signal enhancement technique which can be used on a wide range of species. The technique is based on a perceptually scaled wavelet packet decomposition using a species-specific Greenwood scale function. Spectral estimation techniques, similar to those used for human speech enhancement, are used for estimation of clean signal wavelet coefficients under an additive noise model. The new approach is compared to several other techniques, including basic bandpass filtering as well as classical speech enhancement methods such as spectral subtraction, Wiener filtering, and Ephraim-Malah filtering. Vocalizations recorded from several species are used for evaluation, including the ortolan bunting (Emberiza hortulana), rhesus monkey (Macaca mulatta), and humpback whale (Megaptera novaeanglia), with both additive white Gaussian noise and environment recording noise added across a range of signal-to-noise ratios (SNRs). Results, measured by both SNR and segmental SNR of the enhanced wave forms, indicate that the proposed method outperforms other approaches for a wide range of noise conditions.

Altitudinal variations of acoustic organs in anurans: A case study from China

Bioacoustic signals are considered to be essential to the social communication of anuran amphibians. However, some anuran species lack a vocal sac or tympanum, or both. We hypothesise that the variation of anuran acoustic organs correlates to altitude. We reviewed published literature concerning anuran species which inhabit China, and tested the general relationship between anuran acoustic organs and altitude. The result showed that both the vocal sac and the tympanum had a significantly negative trend to altitude, and that the vocal sac also significantly correlated to the tympanum. The interaction by phylogenetic relationships could be excluded. We suggested that anuran acoustic organs decline with increasing altitude. With increasing altitude, lower population density, low temperature, hypoxia and food shortage could be responsible for such degeneration. Nontympanic pathways of sound reception and ultrasonic signal system may be utilised to compensate for anuran acoustic organ degeneration.

Computer aided design of audio signal classifier systems

The problem of programming classifier systems for deterministic events in bioacoustic signals is seen as a knowledge engineering problem. Advantages and disadvantages of traditional approaches are discussed. A knowledge engineering approach for interactive visual classifier system design is described and advantages of the approach are highlighted. The basic architecture of a general purpose knowledge engineering framework for the acoustics domain is outlined. Emphasis is placed on both expert knowledge and on the role of data mining within the classifier system design process. Several examples illustrate how the approach can be used to create classifier systems for bioacoustic patterns in the time‐frequency domain. Examples include multiscale spectrographic visualization, techniques to create and interactively modify subsymbolic and symbolic classifier systems for time‐frequency patterns as well as the usage of three‐dimensional visualizations to display hierarchical dependencies within complex classifier...

Methods for automatically analyzing humpback song units

This paper presents mathematical techniques for automatically extracting and analyzing bioacoustic signals. Automatic techniques are described for isolation of target signals from background noise, extraction of features from target signals and unsupervised classification (clustering) of the target signals based on these features. The only user-provided inputs, other than raw sound, is an initial set of signal processing and control parameters. Of particular note is that the number of signal categories is determined automatically. The techniques, applied to hydrophone recordings of humpback whales (Megaptera novaeangliae), produce promising initial results, suggesting that they may be of use in automated analysis of not only humpbacks, but possibly also in other bioacoustic settings where automated analysis is desirable.

An iterative kurtosis-based technique for the detection of nonstationary bioacoustic signals

A novel technique for the detection of nonstationary bioacoustic signals, such as explosive lung and bowel sounds, in clinical auscultative recordings is presented. The technique uses kurtosis (zero-lag fourth-order statistics) to form an iterative kurtosis-based detector (IKD). The IKD iteratively detects important peaks of the kurtosis, estimated within a sliding window along the signal under investigation, which indicate the presence of non-Gaussianity in the raw signal. The efficiency of the IKD has been illustrated by experimental results that demonstrate its ability to clearly detect bioacoustic signals of diagnostic interest in the presence of background signal with high amplitude.

Automated categorization of bioacoustic signals: Avoiding perceptual pitfalls

Dividing the acoustic repertoires of animals into biologically relevant categories presents a widespread problem in the study of animal sound communication, essential to any comparison of repertoires between contexts, individuals, populations, or species. Automated procedures allow rapid, repeatable, and objective categorization, but often perform poorly at detecting biologically meaningful sound classes. Arguably this is because many automated methods fail to address the nonlinearities of animal sound perception. We present a new method of categorization that incorporates dynamic time-warping and an adaptive resonance theory (ART) neural network. This method was tested on 104 randomly chosen whistle contours from four captive bottlenose dolphins (Tursiops truncatus), as well as 50 frequency contours extracted from calls of transient killer whales (Orcinus orca). The dolphin data included known biologically meaningful categories in the form of 42 stereotyped whistles produced when each individual was isolated from its group. The automated procedure correctly grouped all but two stereotyped whistles into separate categories, thus performing as well as human observers. The categorization of killer whale calls largely corresponded to visual and aural categorizations by other researchers. These results suggest that this methodology provides a repeatable and objective means of dividing bioacoustic signals into biologically meaningful categories.

Wavelet-Based Enhancement of Lung and Bowel Sounds Using Fractal Dimension Thresholding— Part I: Methodology

An efficient method for the enhancement of lung sounds (LS) and bowel sounds (BS), based on wavelet transform (WT), and fractal dimension (FD) analysis is presented in this paper. The proposed method combines multiresolution analysis with FD-based thresholding to compose a WT-FD filter, for enhanced separation of explosive LS (ELS) and BS (EBS) from the background noise. In particular, the WT-FD filter incorporates the WT-based multiresolution decomposition to initially decompose the recorded bioacoustic signal into approximation and detail space in the WT domain. Next, the FD of the derived WT coefficients is estimated within a sliding window and used to infer where the thresholding of the WT coefficients has to happen. This is achieved through a self-adjusted procedure that iteratively "peels" the estimated FD signal and isolates its peaks produced by the WT coefficients corresponding to ELS or EBS. In this way, two new signals are constructed containing the useful and the undesired WT coefficients, respectively. By applying WT-based multiresolution reconstruction to these two signals, a first version of the desired signal and the background noise is provided, accordingly. This procedure is repeated until a stopping criterion is met, finally resulting in efficient separation of the ELS or EBS from the background noise. The proposed WT-FD filter introduces an alternative way to the enhancement of bioacoustic signals, applicable to any separation problem involving nonstationary transient signals mixed with uncorrelated stationary background noise. The results from the application of the WT-FD filter to real bioacoustic data are presented and discussed in an accompanying paper.

Wavelet-Based Enhancement of Lung and Bowel Sounds Using Fractal Dimension Thresholding —Part II: Application Results

The application of the wavelet transform-fractal dimension-based (WT-FD) filter of Part I of this paper to real bioacoustic data, which include explosive lung sounds (ELS) and explosive bowel sounds (EBS) recorded from patients with pulmonary or gastrointestinal dysfunction, respectively, is presented in this paper. The objective of the latter is the evaluation of the performance of the WT-FD filter on different types of bioacoustic signals, varying not only in their structural morphology but also in the degree of their noise contamination. As it is thoroughly described in Part I of this paper, the WT-FD filter uses the fractal dimension to form an efficient way of thresholding the WT coefficients at different resolution scales, keeping, thus, only those that can contribute to the accurate reconstruction of the ELS and EBS signals. Quantitative and qualitative analysis of the experimental results show an efficient performance of the WT-FD filter to circumvent the noise presence (100% detectability rate, 100% sensitivity, 100% specificity) by faithfully extracting the authentic structure of ELS and EBS from the background noise. The WT-FD filter does not require any noise reference signal or noise reference templates. The results from a noise stress test (mean cross-correlation index of the original and the estimated signal converging to 100%; mean normalized maximum amplitude error converging to 0.7%) prove its robustness to various noise levels (0-20 dB), enabling its potential use in similar noise cases met in everyday clinical medicine. Furthermore, the efficient performance of the WT-FD filter facilitates the physician to better interpret the auscultation findings. Due to its simplicity and low computational cost, the WT-FD filter can possibly be implemented in a real-time context to serve as a tool for the continuous ELS and EBS screening.

Automated categorization of bioacoustic signals: Avoiding perceptual pitfalls

Dividing a species acoustic repertoire into biologically relevant categories presents a widespread problem in the study of animal sound communication. Such categorization is fundamental to any attempt to compare repertoires between contexts, individuals, populations or species. Automated procedures allow rapid, repeatable and objective categorization, however, in the past they have often performed poorly at detecting biologically meaningful sound classes. Arguably this is because automated methods have often failed to address the nonlinearities of animal sound perception and a method that incorporates dynamic time warping and an adaptive resonance theory (ART) neural network to avoid some of the problems is presented here. The method was tested on 104 randomly chosen whistles recorded from four captive bottlenose dolphins (Tursiops truncatus). This data set includes known biologically meaningful categories in the form of 42 distinctive stereotyped whistles produced when each individual was kept in isolation. The automated procedure correctly grouped all but two of the stereotyped whistles into their respective categories, and thus performed as good as human observers. However, compared to humans, the automated system provided finer categorization of the remaining non-stereotyped whistles. These results suggest that this methodology provides a repeatable and objective means of splitting bioacoustic signals into biologically meaningful categories.

Automated identification of field-recorded songs of four British grasshoppers using bioacoustic signal recognition.

Recognition of Orthoptera species by means of their song is widely used in field work but requires expertise. It is now possible to develop computer-based systems to achieve the same task with a number of advantages including continuous long term unattended operation and automatic species logging. The system described here achieves automated discrimination between different species by utilizing a novel time domain signal coding technique and an artificial neural network. The system has previously been shown to recognize 25 species of British Orthoptera with 99% accuracy for good quality sounds. This paper tests the system on field recordings of four species of grasshopper in northern England in 2002 and shows that it is capable of not only correctly recognizing the target species under a range of acoustic conditions but also of recognizing other sounds such as birds and man-made sounds. Recognition accuracies for the four species of typically 70-100% are obtained for field recordings with varying sound intensities and background signals.

Chemical communication in an archaic anuran amphibian

Bioacoustic signals appear to be so essential to the social communication of anuran amphibians that other sensory modalities have been largely ignored. We studied the abilities of Leiopelma hamiltoni, a species evolutionarily basal to most living anurans, to communicate by means of chemosignals. We collected frogs in the field, held them in captivity for 72 h, and then tested their preferences for substrates that they had marked themselves to those marked by conspecifics. Individuals preferred paper towels that they had marked themselves to those marked by frogs collected from other home ranges. Preferences were greater, on average, when the conspecific had been collected farther away. Frogs did not discriminate between their own odor and those of other individuals with which they had shared a home range. Individuals preferred their own odor to a blank unmarked towel but also avoided odors of unfamiliar conspecifics when paired with a blank. The discovery of chemical communication in an archaic anuran offers a window into how frogs may have communicated before mechanisms of bioacoustic signaling evolved. We suggest, however, that chemical signaling may be widespread in anuran amphibians. Copyright 2004.