Acoustic and psychoacoustic analysis of the noise produced by the police force firearms

Police officers are exposed to impact noise coming from firearms, which may cause irreversible injuries to the hearing system.Aim: To evaluate the noise exposure in shooting stands during gunfire exercises, to analyze the acoustic impact of the noise produced by the firearms and to associate it with tonal audiometry results.Study design: Cross-sectional.Materials and methods: To measure noise intensity we used a digital sound level meter, and the acoustic analysis was carried out by means of the oscillations and cochlear response curves provided by the Praat software. 30 police officers were selected (27 males and 3 females).Results: The peak level measured was 113.1 dB(C) from a .40 pistol and 116.8 dB(C) for a .38 revolver. The values obtained for oscillation and Praat was 17.9 +/- 0.3 Barks, corresponding to the rate of 4,120 and 4,580 Hz. Audiometry indicated greater hearing loss at 4,000Hz in 86.7% of the cases.Conclusion: With the acoustic analysis it was possible to show cause and effect between the main areas of energy excitation of the cochlea (Praat cochlear response curve) and the frequencies of low hearing acuity.

Acoustic Riddles of Cultural Heritage

It is shown, that in the process of evolution, a relationship between some hidden parameters of acoustic signals and expected emotional response to them has been formed. This relationship is a necessary condition for a living creature to survive. The efficiency of the concept represented is illustrated using examples from the field of classical music, ethnic African music, as well as biolinguistic signals of animals. Reasons of a particular impact of bell rings are analyzed using acoustic signals of some Bulgarian and Russian bell rings and chimes.

Evaluation of damage in foam sandwich components of aircraft

Tn the current set of investigations foam sandwich panels and some components of an aircraft comprising of two layer Glass Fiber Reinforced Plastic(GFRP) face sheets of thickness 1mm each with polyurethene foam as filler of thickness 8mm were examined for detection of debonds and defects. Known defects were introduced in the panels in the form of teflon insert, full foam removal,half foam removal and edge delamination by inserting a teflon and removing it after curing. Two such panels were subjected to acoustic impact and analysis was carried out in both time and frequency domains. These panels were ultrasonically scanned to obtain C-SCAN images as reference to evaluate Acoustic Impact Test (AIT) results. In addition both Fokker bond testing and AIT(woodpecker) were carried out on the same panels and also some critical joints on the actual component. The results obtained from these tests are presented and discussed in this paper.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

North Pacific Acoustic Laboratory : environmental impact statement.

Mode of access: Internet.

Impact of cognitive load and frustration on drivers’ speech [Abstract]

Secondary tasks such as cell phone calls or interaction with automated speech dialog systems (SDSs) increase the driver’s cognitive load as well as the probability of driving errors. This study analyzes speech production variations due to cognitive load and emotional state of drivers in real driving conditions. Speech samples were acquired from 24 female and 17 male subjects (approximately 8.5 h of data) while talking to a co-driver and communicating with two automated call centers, with emotional states (neutral, negative) and the number of necessary SDS query repetitions also labeled. A consistent shift in a number of speech production parameters (pitch, first format center frequency, spectral center of gravity, spectral energy spread, and duration of voiced segments) was observed when comparing SDS interaction against co-driver interaction; further increases were observed when considering negative emotion segments and the number of requested SDS query repetitions. A mel frequency cepstral coefficient based Gaussian mixture classifier trained on 10 male and 10 female sessions provided 91% accuracy in the open test set task of distinguishing co-driver interactions from SDS interactions, suggesting—together with the acoustic analysis—that it is possible to monitor the level of driver distraction directly from their speech.

Acoustic keyword spotting in speech with applications to data mining

Keyword Spotting is the task of detecting keywords of interest within continu- ous speech. The applications of this technology range from call centre dialogue systems to covert speech surveillance devices. Keyword spotting is particularly well suited to data mining tasks such as real-time keyword monitoring and unre- stricted vocabulary audio document indexing. However, to date, many keyword spotting approaches have su®ered from poor detection rates, high false alarm rates, or slow execution times, thus reducing their commercial viability. This work investigates the application of keyword spotting to data mining tasks. The thesis makes a number of major contributions to the ¯eld of keyword spotting. The ¯rst major contribution is the development of a novel keyword veri¯cation method named Cohort Word Veri¯cation. This method combines high level lin- guistic information with cohort-based veri¯cation techniques to obtain dramatic improvements in veri¯cation performance, in particular for the problematic short duration target word class. The second major contribution is the development of a novel audio document indexing technique named Dynamic Match Lattice Spotting. This technique aug- ments lattice-based audio indexing principles with dynamic sequence matching techniques to provide robustness to erroneous lattice realisations. The resulting algorithm obtains signi¯cant improvement in detection rate over lattice-based audio document indexing while still maintaining extremely fast search speeds. The third major contribution is the study of multiple veri¯er fusion for the task of keyword veri¯cation. The reported experiments demonstrate that substantial improvements in veri¯cation performance can be obtained through the fusion of multiple keyword veri¯ers. The research focuses on combinations of speech background model based veri¯ers and cohort word veri¯ers. The ¯nal major contribution is a comprehensive study of the e®ects of limited training data for keyword spotting. This study is performed with consideration as to how these e®ects impact the immediate development and deployment of speech technologies for non-English languages.

Characterizing the signal pattern of a four-cylinder diesel engine using acoustic emission and vibration analysis

Condition monitoring of diesel engines can prevent unpredicted engine failures and the associated consequence. This paper presents an experimental study of the signal characteristics of a 4-cylinder diesel engine under various loading conditions. Acoustic emission, vibration and in-cylinder pressure signals were employed to study the effectiveness of these techniques for condition monitoring and identifying symptoms of incipient failures. An event driven synchronous averaging technique was employed to average the quasi-periodic diesel engine signal in the time domain to eliminate or minimize the effect of engine speed and amplitude variations on the analysis of condition monitoring signal. It was shown that acoustic emission (AE) is a better technique than vibration method for condition monitor of diesel engines due to its ability to produce high quality signals (i.e., excellent signal to noise ratio) in a noisy diesel engine environment. It was found that the peak amplitude of AE RMS signals correlating to the impact-like combustion related events decreases in general due to a more stable mechanical process of the engine as the loading increases. A small shift in the exhaust valve closing time was observed as the engine load increases which indicates a prolong combustion process in the cylinder (to produce more power). On the contrary, peak amplitudes of the AE RMS attributing to fuel injection increase as the loading increases. This can be explained by the increase fuel friction caused by the increase volume flow rate during the injection. Multiple AE pulses during the combustion process were identified in the study, which were generated by the piston rocking motion and the interaction between the piston and the cylinder wall. The piston rocking motion is caused by the non-uniform pressure distribution acting on the piston head as a result of the non-linear combustion process of the engine. The rocking motion ceased when the pressure in the cylinder chamber stabilized.

Vestibular rehabilitation following the removal of an acoustic neuroma : a systematic review of randomized trials

An acoustic neuroma (also known as a vestibular schwannoma) is an intracranial tumour of the vestibular nerve that is commonly treated by surgical resection. Following resection of an acoustic neuroma, patients may experience a range of symptoms that include deficits in gaze stability, mobility and balance. Vestibular rehabilitation may be useful in reducing the severity and minimizing the impact of these symptoms.

Detection of rain in acoustic recordings of the environment

Environmental monitoring has become increasingly important due to the significant impact of human activities and climate change on biodiversity. Environmental sound sources such as rain and insect vocalizations are a rich and underexploited source of information in environmental audio recordings. This paper is concerned with the classification of rain within acoustic sensor re-cordings. We present the novel application of a set of features for classifying environmental acoustics: acoustic entropy, the acoustic complexity index, spectral cover, and background noise. In order to improve the performance of the rain classification system we automatically classify segments of environmental recordings into the classes of heavy rain or non-rain. A decision tree classifier is experientially compared with other classifiers. The experimental results show that our system is effective in classifying segments of environmental audio recordings with an accuracy of 93% for the binary classification of heavy rain/non-rain.

Performance analysis of surface acoustic wave matched filters

SAW matched filter is commonly used in spread spectrum communication receivers in order to maximize the SNR prior to detection, At times the receiver would be a mobile one while the signal is processed at the IF level, In that case frequency deviations due to Doppler shift or temperature dependence of the acoustic medium used for SAW device would, severely effect it's performance, The impact of these errors on the receiver performance is analyzed on a generalised basis.

Ultrasonic evaluation of delamination in quasi-isotropic CFRP laminates subjected to low-velocity impact

Ultrasonic C-Scan is used very often to detect flaws and defects in the composite components resulted during fabrication and damages resulting from service conditions. Evaluation and characterization of defects and damages of composites require experience and good understanding of the material as they are distinctly different in composition and behavior as compared to conventional metallic materials. The failure mechanisms in composite materials are quite complex. They involve the interaction of matrix cracking, fiber matrix interface debonding, fiber pullout, fiber fracture and delamination. Generally all of them occur making the stress and failure analysis very complex. Under low-velocity impact loading delamination is observed to be a major failure mode. In composite materials the ultrasonic waves suffer high acoustic attenuation and scattering effect, thus making data interpretation difficult. However these difficulties can be overcome to a greater extent by proper selection of probe, probe parameter settings like pulse width, pulse amplitude, pulse repetition rate, delay, blanking, gain etc., and data processing which includes image processing done on the image obtained by the C-Scan.

Fast acoustic likelihood computation using low-rank matrix approximation

Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech processing problems. Computing likelihoods against a large set of Gaussians is required as a part of many speech processing systems and it is the computationally dominant phase for LVCSR systems. We express the likelihood computation as a multiplication of matrices representing augmented feature vectors and Gaussian parameters. The computational gain of this approach over traditional methods is by exploiting the structure of these matrices and efficient implementation of their multiplication.In particular, we explore direct low-rank approximation of the Gaussian parameter matrix and indirect derivation of low-rank factors of the Gaussian parameter matrix by optimum approximation of the likelihood matrix. We show that both the methods lead to similar speedups but the latter leads to far lesser impact on the recognition accuracy. Experiments on a 1138 word vocabulary RM1 task using Sphinx 3.7 system show that, for a typical case the matrix multiplication approach leads to overall speedup of 46%. Both the low-rank approximation methods increase the speedup to around 60%, with the former method increasing the word error rate (WER) from 3.2% to 6.6%, while the latter increases the WER from 3.2% to 3.5%.

Acoustic Noise Characterization of Space-Vector Modulated Induction Motor Drives-An Experimental Approach

This paper presents an experimental procedure to determine the acoustic and vibration behavior of an inverter-fed induction motor based on measurements of the current spectrum, acoustic noise spectrum, overall noise in dB, and overall A-weighted noise in dBA. Measurements are carried out on space-vector modulated 8-hp and 3-hp induction motor drives over a range of carrier frequencies at different modulation frequencies. The experimental data help to distinguish between regions of high and low acoustic noise levels. The measurements also bring out the impact of carrier frequency on the acoustic noise. The sensitivity of the overall noise to carrier frequency is indicative of the relative dominance of the high-frequency electromagnetic noise over mechanical and aerodynamic components of noise. Based on the measured current and acoustic noise spectra, the ratio of dynamic deflection on the stator surface to the product of fundamental and harmonic current amplitudes is obtained at each operating point. The variation of this ratio of deflection to current product with carrier frequency indicates the resonant frequency clearly and also gives a measure of the amplification of vibration at frequencies close to the resonant frequency. This ratio is useful to predict the magnitude of acoustic noise corresponding to significant time-harmonic currents flowing in the stator winding.