WarbleR: an r package to streamline analysis of animal acoustic signals

The current trend in the development of mechatronic systems is the use of multi-drive frame configurations. Such objects, as a rule, consist of a large number of interacting elements. The relative displacements of these elements generate vibrations that can critically affect the operation of precision mechatronic systems. This can lead to limiting operating conditions of the equipment, and in some cases, its failure. An effective means of preventing accidents, identifying critical modes of operation, diagnosing faults in equipment of electromechanical systems are the methods of functional diagnostics.Methods of functional diagnostics in real time require a large number of calculations, mathematical modeling of the object, processing a large amount of information, which leads to the mandatory use of computer technology. The main methods of processing the measurement results are the Fourier and Laplace transforms, classical methods for analyzing time series, multiple-scale wavelet analysis, etc. The disadvantage of the approaches used is computational complexity in the implementation of discrete transformations and the need for significant amounts of memory to store reference values. The considered methods are based on the analysis of the original signal, however, the increments of the signal of different orders may also be informative.The aim of the research is to develop methods for analyzing acoustic signals generated by working equipment of electromechanical complexes, in increments suitable for use in functional diagnostics systems when monitoring the operating modes of electric drives.In the process of research, an analysis of acoustic noise signals from a working multi-drive unit, obtained as a result of a series of experiments at two high-speed modes, was performed. To speed up the process of analyzing signals and eliminate their redundancy, a data aggregation method has been applied. The degree of permissible aggregation is determined using a multi-scale analysis. The developed method for analyzing aggregated acoustic signals in increments in phase space can be used for functional diagnostics of multi-drive units.

Contiene: Processing and analysis of acoustic signals. Acoustic signals of animals. Recording field measurements, analysis and description. Digital signal acquisition and representation.Digital signal analysis, editing, and synthesis. Application of filters in bioacoustics. Applying linear predictive coding (LPC) to frequency-spectrum analysis of animal acoustic signals. Sound production and transmission. Ultrasound and infrasound. Measuring and modeling sprech production. Sound production in birds: acoustics and physiology revisited. Assessing biologically important responses.

The creation of algorithms for the detection of unmanned aerial vehicles (UAVs) based on the analysis of acoustic signals is an actual problem of the present. To solve such problem, it is necessary to correctly take into account the conditions for the propagation of acoustic signals in the atmosphere, as well as the characteristics of UAV flight, the height of which can vary up to several kilometers, and the speed can reach 200 km/h.The purpose of this study is to improve the UAV detection method by the results of spectral analysis of their acoustic signal. The investigations of the spectrum of acoustic signals from various types of UAV showed the presence of several characteristic harmonics in it. The criterion for a reliable detection of UAV is finding at least three characteristic harmonics in a received acoustic signal. The input values for this algorithm are samples of the input acoustic signal passing through the bandpass filter. To represent the signal in the frequency domain, the method of spectral estimation based on the periodogram without a window function has been selected.In this work the improved method and the complex layout for detection of unmanned aerial vehicles (UAV) based on the results of spectral analysis of acoustic signals are proposed. To implement the proposed algorithm, a specialized software and mathematical support for detecting an acoustic signal from the UAV were created. They allow with high reliability to detect an acoustic signal from the UAV at distances of about 1 kilometer. The efficiency of the proposed algorithm is verified using the layout of the acoustic detection complex in real conditions. Unmanned aerial systems "Fury" and "Leleka 100" were used in the experiment.Thus, it is shown that the acoustic signal created by UAV can be represented as the sum of harmonic components. The criterion for a reliable detection of UAV is finding at least three characteristic harmonics in a received acoustic signal.

Acoustic vibration signal analysis and coal bunker level recognition system in ball mill is used as a tool of acoustic vibration signal analysis system to extract characteristic variable to control the coal bunker level. With this system, the ball mill can operate in a safely condition, and work efficiency can be improved. In this paper, the development of acoustic vibration signal analysis, coal bunker level recognition system, and the result of test in practice are introduced. Hilbert transform and the working principle of the ball mill is analyzed. By the envelope of acoustic vibration signal is extracted. The most important part of the paper is the envelope analysis of acoustic vibration signal. Using wavelet transform we Extract characteristic vector and Using BP neural network is made by the mapping between the characteristic and the coal bunker level actualize the coal bunker level recognition.

Although well studied in other taxa, geographic variation in signal structure has been poorly studied in fishes. Signal divergence in marine fishes depends on larval form; pelagic larvae disperse more widely than demersal forms, limiting opportunities for isolation and subsequent divergence. Freshwater fishes, especially those restricted to headwater habitat, are isolated by drainage. There are many examples of species radiations in groups of North American freshwater fishes within drainage networks, including darters, minnows, and catfishes, some of which are restricted to single streams. Our data demonstrate divergence of acoustic signals among populations of stream and riverine fishes at multiple scales, and often in the absence of apparent morphological variation. Two model species, Longear Sunfish and Whitetail Shiner, differed in the temporal components of calls, while darter and sturgeon models showed variation in both temporal and spectral call components. In the case of the sturgeon, the populations were genetically distinct. Furthermore, data for Whitetail Shiner suggest that calls associated with courtship were strongly associated with geographic isolation, while divergence in those characteristics associated with aggression may be driven by genetic drift. We suggest that variation in acoustic signal structure may be common in freshwater fishes and discuss implications for mate choice.

Dependencies and Ill-designed Parameters Within High-speed Videoendoscopy and Acoustic Signal Analysis

Leak detection and calibration of pipe internal roughness in a water distribution network are significant issue for environment pollution around the world. In recent years the problem of leak detection in pipelines, tanks, and process vessels has been the focus of many man-hours of effort. Acquisition the acoustic signal around the leakage to determine the location and size of leaks is emerging as an important tool. A double acoustic data acquisition and signal processing system based on the TI digital signal processor TMS320VC5410 and analog to digital converter TLC320AD50C is presented in this paper. The system design is introduced, with emphasis on the digital signal processor minimal system and TMS320VC5410 interface circuit which consists of two chips of A/D TLC320AD50C. The software development of the data acquisition and signal analysis is introduced. The system can be used in the application of real-time acoustic signal acquisition and leak location.

A change in the technical condition of mechanical components of internal combustion engines may not be detected by on-board diagnostic systems installed in vehicles. In similar cases, measurements and analyses of vibroacoustic signals being recorded prove useful. Since there are certain limitations to vibration measurements, including those related to the vibration transmission and the engine’s high temperature at measurement points, the authors of this paper have proposed that measurements and analyses of acoustic signals should be applied for the sake of assessment of the internal combustion engine technical condition. However, such an assessment requires new acoustic signal processing methods to be developed, and so this subject has been elaborated in the paper. The article provides a discussion on the option of applying a wavelet packet decomposition while filtering the internal combustion engine’s acoustic signal in order to diagnose an excessive valve clearance. The authors prepared an algorithm enabling selection of the chosen details and approximation of the wavelet analysis to low-frequency components, which constitute the noise, as well as high-frequency components comprising information on the possible enlarged engine valve clearance. Next, based on the selected high-frequency acoustic signal components, a method for automatic detection of enlarged clearance valves was developed, assuming that energy participations of the acoustic signal being emitted were to be determined while opening and closing individual valves. Under the study discussed, identification tests were conducted using two 4-cylinder internal combustion engines featuring valves of different clearances to consequently confirm the efficiency of the algorithm developed for the acoustic signal filtration and automatic detection of enlarged clearance valves.

In order to determine the technical condition of energetic objects with the objective of ensuring their operational reliability, durability and safety, systems of noise diagnostics, which are based on the analysis of acoustic diagnostic signals. A promising area of noise diagnostics are cumulant methods, based on cumulant analysis, which involves the use of cumulants and cumulant coefficients. In known literature no characteristics of detection of a signal within an interference-containing additive mixture with the use of a second-order cumulant (variance) can be found. That is why the objective of the paper is to study the use of cumulant method on the basis of point estimations of variance for a sample of momentary values for detection of an acoustic signal against the background of noise interference. The research was carried out by way of modeling the additive mixture of signal and interference using the MATLAB® software package. Interference is a model of a noise acoustic signal, which accompanies the operation of properly functional equipment. Signal is a model of an acoustic signal which is created with the occurrence of a malfunction. Signal and interference are independent random variables, so the property of additivity of cumulants was used – the variance of a mixture equals the sum of variances of signal and interference. The decision about the presence of a signal was made on the basis of testing two statistical hypotheses. The null hypothesis – the signal is absent, variance equals to the variance of the interference. The first hypothesis – the signal is present, variance equals to the variance of the mixture. Additional parameters: probability of a Type I error 0,01, probability of correct determination 0,99. The relative error of estimation determined the minimal sample size. These values allowed for the calculation of the threshold value, upon the exceeding of which by the variance estimation, the decision on the presence of signal is made. For each sample, assessments of variance were made. Experimental probability of correct determination is calculated as a total number of decisions taken regarding the presence of a signal, divided by the number of realizations, and corresponds to the value of the specified probability of correct determination. Its relative error was calculated in order to control the validity of the results. Also, kernel density estimation of the probability of the variance assessment for the case of a signal with normal distribution. As shown by the graphs, the assessments have a distribution that is close to normal. The conducted study proves that a variance -based cumulant method allows to detect a signal against the background of noise interference. The necessary sample size, which shows the number of the necessary momentary values, is given in the paper. That is to say that with the help of the frequency of an analogue digital converter the needed duration of the recording of a real for assessment of its variance can be obtained, and the decision on the presence or absence of a signal is to be made on the basis of the specified threshold values. The results of the study can be added to the known sample sizes and threshold values for the coefficients of asymmetry and excess with different distributions. Application of the described method requires additional testing on real acoustic signals and has the areas of use in systems of noise diagnostics.

Acoustic emission signals are generated when leak occurs, and leak detection can be realized through the analysis of acoustic emission signals. This is an online nondestructive testing method, which is an important part of spacecraft leak detection. This paper describes the basic principle of acoustic emission leak detection of spacecraft, and introduces the basic composition and main parameters of the ground simulated leak experiment system. Using spectrum analysis, the characteristics of the acoustic signals from three different diameters leakage of 1.0 mm, 1.5 mm and 2.0 mm are obtained and compared with the background noise. The experiment data show that the main frequency of mechanical pump operating background noise is below 5 kHz, while the gas leak acoustic signal is a broadband signal, which is obviously distinguished from the background noise in the high frequency above 10 kHz. The smaller the diameter of the leakage hole is, the smaller the energy of the leak acoustic emission signal is and the more proportion of the high-frequency components is. Thus, the identification of different sizes leakage can be realized. The results of this paper are of great significance to the detection of spacecraft leak in orbit.

The switchgear acoustic signal collected in the field environment is usually non-stationary and nonlinear, with a complex composition, mainly composed of noise, effective sound vibration waveform and harmonic superposition. At the same time, due to the weak energy of the fault feature signal, it is often submerged by the noise, and it is difficult to find and extract the fault signal with periodic and shock characteristics. This paper presents an acoustic vibration signal analysis method based on variational mode decomposition (VMD) to decompose the raw data into narrow-band eigenmode functions to mine fault information. Practical tests show that this method works well.

This manuscript deals with a complex analysis of acoustic emission signals that were recorded during freeze-thaw cycles in test specimens produced from air-entrained concrete. An assessment of the resistance of concrete to the effects of freezing and thawing was conducted on the basis of a signal analysis. Since the experiment simulated testing of concrete in a structure, a concrete block with the height of 2.4 m and width of 1.8 m was produced to represent a real structure. When the age of the concrete was two months, samples were obtained from the block by core drilling and were subsequently used to produce test specimens. Testing of freeze-thaw resistance of concrete employed both destructive and non-destructive methods including the measurement of acoustic emission, which took place directly during the freeze-thaw cycles. The recorded acoustic emission signals were then meticulously analysed. The aim of the conducted experiments was to verify whether measurement using the acoustic emission method during Freeze-thaw (F-T) cycles are more sensitive to the degree of damage of concrete than the more commonly employed construction testing methods. The results clearly demonstrate that the acoustic emission method can reveal changes (e.g., minor cracks) in the internal structure of concrete, unlike other commonly used methods. The analysis of the acoustic emission signals using a fast Fourier transform revealed a significant shift of the dominant frequency towards lower values when the concrete was subjected to freeze-thaw cycling.

Acoustic myography is the recording of sounds produced by contracting muscle. These sounds become louder with increasing force of contraction. We have compared muscle sounds with surface EMG to monitor the dissociation of electrical from mechanical events (presumably, the loss of excitation-contraction coupling) which occur with motor unit fatigue. Acoustic signals were amplified using a standard phonocardiograph, recorded on FM magnetic tape, and digitally analyzed. Muscles were examined at rest, with intermittent contractions, and with sustained contractions. We found that with fatigue, the acoustic amplitude decayed, but the surface EMG amplitude did not. With decreased effort, however, the acoustic and the surface EMG amplitudes declined simultaneously. By simultaneously recording acoustic signals and needle EMG, individual motor units were resolved acoustically in two muscles with decreased numbers of motor units and increased motor unit size. Fasciculations also produced acoustic signals, although no acoustic signal has yet been found that correlates with fibrillations. Analysis of acoustic signals from muscle provides a noninvasive method for monitoring motor unit fatigue in vivo. It may also be useful in distinguishing muscle fatigue from decreased volition.

Perceptual relevance of species-specific differences in acoustic signal structure in Streptopelia doves

This paper deals with spectral analysis of acoustic emission signals recorded during liquid aluminium crystallization process. The impact of aluminium-titan-boron ligature to these signals is described. The paper is intended to reveal relationships between acoustic emission signal’s spectral characteristics and structure of aluminium with a certain percentage of aluminium-titan-boron ligature. Model of process of aluminium crystallization based on Stefan boundary problem is described. Experimental measuring device is used for recording acoustic emission signals. To identify some relationships between percentage of aluminium-titan-boron ligature and acoustic emission signals these signals are being analysed in frequency domain. For processing acoustic emission data and results visualization the Matlab R2015b is applied.