RECENT BIOACOUSTIC PUBLICATIONS (mainly 1993)

ABSTRACT

Some acoustic signals produced by small insects are very low in amplitude and attenuate rapidly with distance. To achieve high quality recordings with such signals, the use of specialised microphones or of sound insulation chambers is necessary. This paper presents a simple and efficient method for the recording of acoustic signals emitted by small sources. Its principle is based upon the use of two simultaneous digital recordings from two microphones: one records the ambient noise while the other records the ambient noise plus the signal to analyse. Both these recordings are converted into digital files and then a simple subtraction between the two isolates the signal with a good signal-to-noise ratio. With this method of background noise removal, the recording of low amplitude sounds in an uninsulated room with common microphones becomes possible. We have applied this method to the study of 12 complete courtships of Drosophila melanogaster and particularly to the analysis of pulse sounds produced by the male in presence of a female. The study focuses mainly on the rhythm of production of pulse trains over the course of the courtship.     

Chest conduction properties and ECG equalization

In common practice of detecting and recording biomedical signals, it is often implicitly assumed that the propagation, through the whole circuit human body-electrodes recording devices, is frequency and voltage independent. As a consequence, clinicians are not aware that recorded signals do not correspond faithfully to the original electrical activity of organs under investigation. We have studied the transmission of electrical signals in human body at various voltages and frequencies to understand if and to which extent the most diffused stimulating and recording techniques used in medicine are affected by global body conduction properties. Our results show that, in order to obtain a more faithful detection of electrical activity produced or evoked by human organs (e.g. EGG, electromyography, etc.), it is convenient to 'equalize' recorded signals. To this purpose, two equalization techniques are proposed, based, respectively, on a simple hardware filtering during acquisition, or FFT post-processing of the acquired signals. As an application, we have studied the transmission of electrical signal in human chest and have compared equalized high frequency ECG signals with raw (original) recordings.     

Temporal separation of whale vocalizations from background oceanic noise using a power calculation

The process of analyzing audio signals in search of cetacean vocalizations is in many cases a very arduous task, requiring many complex computations, a plethora of digital processing techniques and the scrutinization of an audio signal with a fine comb to determine where the vocalizations are located. To ease this process, a computationally efficient and noise-resistant method for determining whether an audio segment contains a potential cetacean call is developed here with the help of a robust power calculation for stationary Gaussian noise signals and a recursive method for determining the mean and variance of a given sample frame. The resulting detector is tested on audio recordings containing southern right whale sounds and its performance is compared to a contemporary energy detector and a popular deep learning method. The detector exhibits good performance at moderate-to-high signal-to-noise ratio values. The detector succeeds in being easy to implement, computationally efficient to use and robust enough to accurately detect whale vocalizations in a noisy underwater environment.     

Long Recording Sequences: How to Track the Intra-Individual Variability of Acoustic Signals

Recently developed acoustic technologies - like automatic recording units - allow the recording of long sequences in natural environments. These devices are used for biodiversity survey but they could also help researchers to estimate global signal variability at various (individual, population, species) scales. While sexually-selected signals are expected to show a low intra-individual variability at relatively short time scale, this variability has never been estimated so far. Yet, measuring signal variability in controlled conditions should prove useful to understand sexual selection processes and should help design acoustic sampling schedules and to analyse long call recordings. We here use the overall call production of 36 male treefrogs (Hyla arborea) during one night to evaluate within-individual variability in call dominant frequency and to test the efficiency of different sampling methods at capturing such variability. Our results confirm that using low number of calls underestimates call dominant frequency variation of about 35% in the tree frog and suggest that the assessment of this variability is better by using 2 or 3 short and well-distributed records than by using samples made of consecutive calls. Hence, 3 well-distributed 2-minutes records (beginning, middle and end of the calling period) are sufficient to capture on average all the nightly variability, whereas a sample of 10 000 consecutive calls captures only 86% of it. From a biological point of view, the call dominant frequency variability observed in H. arborea (116Hz on average but up to 470 Hz of variability during the course of the night for one male) challenge about its reliability in mate quality assessment. Automatic acoustic recording units will provide long call sequences in the near future and it will be then possible to confirm such results on large samples recorded in more complex field conditions.     

First automatic passive acoustic tool for monitoring two species of procellarides (Pterodroma baraui and Puffinus bailloni) on Reunion Island,Indian Ocean

Here are proposed two automatic detectors of Barau's petrel (Pterodroma baraui) and tropical shearwater (Puffinus bailloni) vocalisations in noisy audio recordings (1) trained with a low number of positive training instances, and (2) whose performances would be the highest possible. To do so, acoustic recordings were performed in one Barau's petrel colony between February and May 2014 (85 h) and in two tropical shearwater colonies in March and April (21 h). Manual and automatic methods of segmentation were combined. Manual segmentation allowed (1) to miss a very few number of positive segments and (2) to avoid introducing false positive instances. Automatic segmentation provided quickly a diversified set of negative instances. Manual labelling must be regarded as an investment, for current and future works. A random forest classifier and classical methods of acoustic signal characterisation (cepstral coefficients, spectral moments, etc.) were tested. Best models were able to discriminate each target species calls from other sounds of its colony with F1 scores of 88% (Barau's petrel, 1015 samples) and 85% (tropical shearwater, 1217 samples). The acoustic monitoring of nocturnal burrow-nesting seabirds based on (1) data collected by autonomous recording units in harsh, windy and wet environments and (2) automatic analysis tools is feasible. The size of our database was limited. Consequently further works will be necessary to study robustness of models on long time-series data.     

Automatic identification of rainfall in acoustic recordings

The rainfall regime is one of the main abiotic components that can cause modifications in the breeding activity of animal species. It has a direct effect on the environmental conditions, and acts as a modifier of the landscape and soundscape. Variations in water quality and acidity, flooding, erosion, and sound distortion are usually related with the presence of rain. Thereby, ecological studies in populations and communities would benefit from improvements in the estimation of rainfall patterns throughout space and time.In this paper, a method for automatic detection of rainfall in forests by using acoustic recordings is proposed. This approach is based on the estimation of the mean value and signal to noise ratio of the power spectral density in the frequency band in which the sound of the raindrops falling over the vegetation layers of the forest is more prominent (i.e. 600–1200 Hz). The results of this method were compared with human auditory identification and data provided by a pluviometer. We achieved a correlation of 95.23% between the data provided by the pluviometer and the predictions of a regression model. Furthermore, we attained a general accuracy between 92.90% and 99.98% when identifying different intensity levels of rainfall on recordings.Nowadays, passive monitoring recorders have been extensively used to study of acoustic-based breeding processes of several animal species. Our method uses the signals acquired by these recorders in order to identify and quantify rainfall events in short and long time spans. The proposed approach will automatically provide information about the rainfall patterns experienced by target species based on audio recordings.     

Online control of evaluation algorithms of the image applied to identification of deglutition function]

Comprehensive software and hardware have been developed for the processing of biosignals. Such automatic signal processing, however not only has advantages, but also drawbacks. The question as to the reliability of the evaluation algorithm arises when the signal is modified, in the presence of interindividual differences, and in particular when noise is superimposed. This is of great interest for long-term recording when the original signal can no longer be inspected visually. The aim of our work was to display the signals on the screen of a monitor simultaneously with lines marking the points (start, end, extreme value, etc.) processed by the specific signal processing algorithm. The program package permits the on-line recording and monitoring of signals, the parallel processing and marking of detected events on the monitor, as well as storage of the parameters extracted. It is a very effective tool for developing, improving and monitoring of algorithms and their efficiency for signal processing.     

Vocalisation sound pattern identification in young broiler chickens

In this study, we describe the monitoring of young broiler chicken vocalisation, with sound recorded and assessed at regular intervals throughout the life of the birds from day 1 to day 38, with a focus on the first week of life. We assess whether there are recognisable, and even predictable, vocalisation patterns based on frequency and sound spectrum analysis, which can be observed in birds at different ages and stages of growth within the relatively short life of the birds in commercial broiler production cycles. The experimental trials were carried out in a farm where the broiler where reared indoor, and audio recording procedures carried out over 38 days. The recordings were made using two microphones connected to a digital recorder, and the sonic data was collected in situations without disturbance of the animals beyond that created by the routine activities of the farmer. Digital files of 1 h duration were cut into short files of 10 min duration, and these sound recordings were analysed and labelled using audio analysis software. Analysis of these short sound files showed that the key vocalisation frequency and patterns changed in relation to increasing age and the weight of the broilers. Statistical analysis showed a significant correlation (P<0.001) between the frequency of vocalisation and the age of the birds. Based on the identification of specific frequencies of the sounds emitted, in relation to age and weight, it is proposed that there is potential for audio monitoring and comparison with ‘anticipated’ sound patterns to be used to evaluate the status of farmed broiler chicken.     

Computer processing of intracardiac electrograms for conduction studies.

Computer techniques developed to process intracardiac signals recorded in dogs are presented. The signals under measurement are the auricular and ventricular monophasic action potentials and the His bundle electrogram. Computerized measurement of significant timing parameters on simultaneous recordings of these signals can assess quite precisely changes in the normal conduction scheme of the heart provoked by different experimental protocols. Increased accuracy is mainly due to the objective way of defining wave onsets and the processing power of the system used. Signal recording, signal acquisition, automatic waveform measurements, interactive process and production of end result graphs by computer are all described.     

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.     

Bioelectrochemical signal monitoring of in-vitro cultured cells by means of an automated microsystem based on solid state sensor-array

In the last decade, fundamental advances in whole cell based sensors and microsystems have established the extracellular acidification rate monitoring of cell cultures as an important indicator of the global cellular metabolism. Innovative approaches adopting advanced integrated sensor array-based microsystems represent an emerging technique with numerous biomedical applications. This paper reports a cell-based microsystem, for multisite monitoring of the physiological state of cell populations. The functional components of the microsystem are an ion sensitive field effect transistor (ISFET) array-based sensor chip and a CMOS integrated circuit for signal conditioning and sensor signal multiplexing. In order to validate the microsystem capabilities for in-vitro toxicity screening applications, preliminary experimental measurements with Cheratinocytes, and CHO cells are presented. Variations in the acidification rate, imputable to the inhibitory effect of the drug on the metabolic cell activity have been monitored and cell viability during long term measurements has been also demonstrated.     

Detection and Processing Techniques of FECG Signal for Fetal Monitoring

Fetal electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies are becoming very important requirements in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and developed algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature for fetal monitoring. A comparative study has been carried out to show the performance and accuracy of various methods of FECG signal analysis for fetal monitoring. Finally, this paper further focused some of the hardware implementations using electrical signals for monitoring the fetal heart rate. This paper opens up a passage for researchers, physicians, and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system.     

Beta声学指数的特征和应用

随着录音设备性能的提高和硬件价格的降低,基于录音评估声景来反映生境特征和生物多样性的方法得到快速发展。声学指数是对声音整体特征的量化,受到录音生境和生物组成的共同影响,因此可构建声学指数与生境特征和生物组成的关联。按照作用的尺度,声学指数可分为两类:反映录音内信息的alpha声学指数和比较不同录音之间差异的beta声学指数。随着录音设备的普及,以及在大尺度上进行生物监测工作的增加,对不同时间、不同地点的录音进行比较的需求日益迫切。因此,beta声学指数的开发和应用是声学指数研究的重要方向。本文介绍了11个常用的beta声学指数,并探讨了这些指数的数学特征(非负性、同一性、对称性、直递性、有限性)。本文还通过文献检索获取了beta声学指数在实证中的应用情况,发现研究中常使用beta声学指数反映时间节律、生境特征的差异或生物组成的改变。最后,本文指出了beta声学指数研究/应用中迫切需要发展的3个方向:开发新的指数、优化已有指数的计算方式、增加实证研究。     

Influence of the first amplifier stage in MEA systems on extracellular signal shapes

Recording of extracellular signals with planar metal microelectrodes (ME) has already been presented more than 30 years ago. To date, microelectrode array (MEA) systems are able to measure extracellular signals at about 64 sites, simultaneously. This enables monitoring of electrical activity of many cells in a large area. The extracellular recording technique has become a widely used method for neurological, toxicological or pharmacological studies. It already proved its potential to supplement the classical methods in electrophysiology. The interpretation of the recorded signal shapes in order to extract electrophysiological meaningful data--however--is still under discussion. In this article, we analyse the preamplifier circuit for extracellular recording of cardiac myocyte signals. We use a circuit model for the cell-electrode contact including the first amplification stage. In test experiments, we observe different signal shapes, when different shunt resistors are introduced at the input of the preamplifier. According to the frequency spectra of the recordings, we evaluate the transfer function between the source signal and the readout signal. As a result of our studies, an optimum readout electronics for originally, preserved extracellular signal shapes is proposed. Our amplifier design will be most valuable, if the use of small microelectrodes with high input impedances for in vitro as well as for in vivo experiments is desired.     

A machine learning pipeline for classification of cetacean echolocation clicks in large underwater acoustic datasets

Machine learning algorithms, including recent advances in deep learning, are promising for tools for detection and classification of broadband high frequency signals in passive acoustic recordings. However, these methods are generally data-hungry and progress has been limited by challenges related to the lack of labeled datasets adequate for training and testing. Large quantities of known and as yet unidentified broadband signal types mingle in marine recordings, with variability introduced by acoustic propagation, source depths and orientations, and interacting signals. Manual classification of these datasets is unmanageable without an in-depth knowledge of the acoustic context of each recording location. A signal classification pipeline is presented which combines unsupervised and supervised learning phases with opportunities for expert oversight to label signals of interest. The method is illustrated with a case study using unsupervised clustering to identify five toothed whale echolocation click types and two anthropogenic signal categories. These categories are used to train a deep network to classify detected signals in either averaged time bins or as individual detections, in two independent datasets. Bin-level classification achieved higher overall precision (>99%) than click-level classification. However, click-level classification had the advantage of providing a label for every signal, and achieved higher overall recall, with overall precision from 92 to 94%. The results suggest that unsupervised learning is a viable solution for efficiently generating the large, representative training sets needed for applications of deep learning in passive acoustics.     

Empirical mode decomposition based filtering techniques for power line interference reduction in electrocardiogram using various adaptive structures and subtraction methods

Electrocardiogram (ECG) is a vital sign monitoring measurement of the cardiac activity. One of the main problems in biomedical signals like electrocardiogram is the separation of the desired signal from noises caused by power line interference, muscle artifacts, baseline wandering and electrode artifacts. Different types of digital filters are used to separate signal components from unwanted frequency ranges. Adaptive filter is one of the primary methods to filter, because it does not need the signal statistic characteristics. In contrast with Fourier analysis and wavelet methods, a new technique called EMD, a fully data-driven technique is used. It is an adaptive method well suited to analyze biomedical signals. This paper foregrounds an empirical mode decomposition based two-weight adaptive filter structure to eliminate the power line interference in ECG signals. This paper proposes four possible methods and each have less computational complexity compared to other methods. These methods of filtering are fully a signal-dependent approach with adaptive nature, and hence it is best suited for denoising applications. Compared to other proposed methods, EMD based direct subtraction method gives better SNR irrespective of the level of noises.     

Time-frequency filtering of MEG signals with matching pursuit.

Time-frequency signal analysis based on various decomposition techniques is widely used in biomedical applications. Matching Pursuit is a new adaptive approach for time-frequency decomposition of such biomedical signals. Its advantage is that it creates a concise signal approximation with the help of a small set of Gabor atoms chosen iteratively from a large and redundant set. In this paper, the usage of Matching Pursuit for time-frequency filtering of biomagnetic signals is proposed. The technique was validated on artificial signals and its performance was tested for varying signal-to-noise ratios using both simulated and real MEG somatic evoked magnetic field data.     

A Simple and Effective Approach for Digitization of the CTG Signals from CTG Traces

ObjectivesCardiotocography (CTG) is a useful tool for monitoring of the fetal heart rate (FHR) and uterine contractions (UC) during the intrauterine life. Generally, CTG is provided on a printed paper which is hard to save for future evaluations. So, digitization of CTG signals is in demand for future evaluations. A straightforward approach for digitization of the CTG signals is to apply image processing on the scanned CTG printed papers.Material and methodsIn this paper, an automatic procedure is proposed for digitization of the CTG signals. The proposed approach consists of four main stages such as pre-processing, image segmentation, signal extraction and signal calibration. The pre-processing stage covers median filtering and contrasts limited adaptive histogram equalization (CLAHE) for noise removal and contrast enhancement. Image segmentation is used to binarize the CTG images for signal determination using the Otsu's thresholding algorithm. The signal extraction is carried out by a two-stepped algorithm. The acquired CTG signals are then calibrated for obtaining the final CTG signals. We use the correlation coefficient to measure the similarity between the automatically digitized CTG signals and original signals.ResultsIn experimental works, an open-access database, which contains 552 CTG recordings, is employed. The results are quite impressive. According to the obtained results, the average correlation coefficients for FHR and UC signals are 0.9715 ± 0.0168 and 0.9717 ± 0.0465, respectively.ConclusionsThe obtained results show that the proposed method is quite efficient in digitization of the CTG signals. In future works, this tool will be used to digitize the recordings belonging to the antepartum period collected from the obstetrics clinics in Medical Park Hospital in Elazığ, Turkey.     

Review on electromyography signal acquisition and processing

Electromyography (EMG) is a technique for recording biomedical electrical signals obtained from the neuromuscular activities. These signals are used to monitor medical abnormalities and activation levels, and also to analyze the biomechanics of any animal movements. In this article, we provide a short review of EMG signal acquisition and processing techniques. The average efficiency of capture of EMG signals with current technologies is around 70%. Once the signal is captured, signal processing algorithms then determine the recognition accuracy, with which signals are decoded for their corresponding purpose (e.g., moving robotic arm, speech recognition, gait analysis). The recognition accuracy can go as high as 99.8%. The accuracy with which the EMG signal is decoded has already crossed 99%, and with improvements in deep learning technology, there is a large scope for improvement in the design hardware that can efficiently capture EMG signals.