Permutation entropy based real-time chatter detection using audio signal in turning process

Machine tool chatter is an unfavorable phenomenon during metal cutting, which results in heavy vibration of cutting tool. With increase in depth of cut, the cutting regime changes from chatter-free cutting to one with chatter. In this paper, we propose the use of permutation entropy (PE), a conceptually simple and computationally fast measurement to detect the onset of chatter from the time series using sound signal recorded with a unidirectional microphone. PE can efficiently distinguish the regular and complex nature of any signal and extract information about the dynamics of the process by indicating sudden change in its value. Under situations where the data sets are huge and there is no time for preprocessing and fine-tuning, PE can effectively detect dynamical changes of the system. This makes PE an ideal choice for online detection of chatter, which is not possible with other conventional nonlinear methods. In the present study, the variation of PE under two cutting conditions is analyzed. Abrupt variation in the value of PE with increase in depth of cut indicates the onset of chatter vibrations. The results are verified using frequency spectra of the signals and the nonlinear measure, normalized coarse-grained information rate (NCIR).

Speech Analysis using Modern Techniques of Nonlinear Dynamics

Medical fields requires fast, simple and noninvasive methods of diagnostic techniques. Several methods are available and possible because of the growth of technology that provides the necessary means of collecting and processing signals. The present thesis details the work done in the field of voice signals. New methods of analysis have been developed to understand the complexity of voice signals, such as nonlinear dynamics aiming at the exploration of voice signals dynamic nature. The purpose of this thesis is to characterize complexities of pathological voice from healthy signals and to differentiate stuttering signals from healthy signals. Efficiency of various acoustic as well as non linear time series methods are analysed. Three groups of samples are used, one from healthy individuals, subjects with vocal pathologies and stuttering subjects. Individual vowels/ and a continuous speech data for the utterance of the sentence "iruvarum changatimaranu" the meaning in English is "Both are good friends" from Malayalam language are recorded using a microphone . The recorded audio are converted to digital signals and are subjected to analysis.Acoustic perturbation methods like fundamental frequency (FO), jitter, shimmer, Zero Crossing Rate(ZCR) were carried out and non linear measures like maximum lyapunov exponent(Lamda max), correlation dimension (D2), Kolmogorov exponent(K2), and a new measure of entropy viz., Permutation entropy (PE) are evaluated for all three groups of the subjects. Permutation Entropy is a nonlinear complexity measure which can efficiently distinguish regular and complex nature of any signal and extract information about the change in dynamics of the process by indicating sudden change in its value. The results shows that nonlinear dynamical methods seem to be a suitable technique for voice signal analysis, due to the chaotic component of the human voice. Permutation entropy is well suited due to its sensitivity to uncertainties, since the pathologies are characterized by an increase in the signal complexity and unpredictability. Pathological groups have higher entropy values compared to the normal group. The stuttering signals have lower entropy values compared to the normal signals.PE is effective in charaterising the level of improvement after two weeks of speech therapy in the case of stuttering subjects. PE is also effective in characterizing the dynamical difference between healthy and pathological subjects. This suggests that PE can improve and complement the recent voice analysis methods available for clinicians. The work establishes the application of the simple, inexpensive and fast algorithm of PE for diagnosis in vocal disorders and stuttering subjects.

Permutation Entropy Based Analysis of Complex Signals for Characterising Change in System Dynamics

Timely detection of sudden change in dynamics that adversely affect the performance of systems and quality of products has great scientific relevance. This work focuses on effective detection of dynamical changes of real time signals from mechanical as well as biological systems using a fast and robust technique of permutation entropy (PE). The results are used in detecting chatter onset in machine turning and identifying vocal disorders from speech signal.Permutation Entropy is a nonlinear complexity measure which can efficiently distinguish regular and complex nature of any signal and extract information about the change in dynamics of the process by indicating sudden change in its value. Here we propose the use of permutation entropy (PE), to detect the dynamical changes in two non linear processes, turning under mechanical system and speech under biological system.Effectiveness of PE in detecting the change in dynamics in turning process from the time series generated with samples of audio and current signals is studied. Experiments are carried out on a lathe machine for sudden increase in depth of cut and continuous increase in depth of cut on mild steel work pieces keeping the speed and feed rate constant. The results are applied to detect chatter onset in machining. These results are verified using frequency spectra of the signals and the non linear measure, normalized coarse-grained information rate (NCIR).PE analysis is carried out to investigate the variation in surface texture caused by chatter on the machined work piece. Statistical parameter from the optical grey level intensity histogram of laser speckle pattern recorded using a charge coupled device (CCD) camera is used to generate the time series required for PE analysis. Standard optical roughness parameter is used to confirm the results.Application of PE in identifying the vocal disorders is studied from speech signal recorded using microphone. Here analysis is carried out using speech signals of subjects with different pathological conditions and normal subjects, and the results are used for identifying vocal disorders. Standard linear technique of FFT is used to substantiate thc results.The results of PE analysis in all three cases clearly indicate that this complexity measure is sensitive to change in regularity of a signal and hence can suitably be used for detection of dynamical changes in real world systems. This work establishes the application of the simple, inexpensive and fast algorithm of PE for the benefit of advanced manufacturing process as well as clinical diagnosis in vocal disorders.

Realization of a Scanning Photoacoustic Technique: Thermal properties of Bulk and Film Samples

Among the large number of photothcrmal techniques available, photoacoustics assumes a very significant place because of its essential simplicity and the variety of applications it finds in science and technology. The photoacoustic (PA) effect is the generation of an acoustic signal when a sample, kept inside an enclosed volume, is irradiated by an intensity modulated beam of radiation. The radiation absorbed by the sample is converted into thermal waves by nonradiative de-excitation processes. The propagating thermal waves cause a corresponding expansion and contraction of the gas medium surrounding the sample, which in tum can be detected as sound waves by a sensitive microphone. These sound waves have the same frequency as the initial modulation frequency of light. Lock-in detection method enables one to have a sufficiently high signal to noise ratio for the detected signal. The PA signal amplitude depends on the optical absorption coefficient of the sample and its thermal properties. The PA signal phase is a function of the thermal diffusivity of the sample.Measurement of the PA amplitude and phase enables one to get valuable information about the thermal and optical properties of the sample. Since the PA signal depends on the optical and thennal properties of the sample, their variation will get reflected in the PA signal. Therefore, if the PA signal is collected from various points on a sample surface it will give a profile of the variations in the optical/thennal properties across the sample surface. Since the optical and thermal properties are affected by the presence of defects, interfaces, change of material etc. these will get reflected in the PA signal. By varying the modulation frequency, we can get information about the subsurface features also. This is the basic principle of PA imaging or PA depth profiling. It is a quickly expanding field with potential applications in thin film technology, chemical engineering, biology, medical diagnosis etc. Since it is a non-destructive method, PA imaging has added advantages over some of the other imaging techniques. A major part of the work presented in this thesis is concemed with the development of a PA imaging setup that can be used to detect the presence of surface and subsmface defects in solid samples.Determination of thermal transport properties such as thermal diffusivity, effusivity, conductivity and heat capacity of materials is another application of photothennal effect. There are various methods, depending on the nature of the sample, to determine these properties. However, there are only a few methods developed to determine all these properties simultaneously. Even though a few techniques to determine the above thermal properties individually for a coating can be found in literature, no technique is available for the simultaneous measurement of these parameters for a coating. We have developed a scanning photoacoustic technique that can be used to determine all the above thermal transport properties simultaneously in the case of opaque coatings such as paints. Another work that we have presented in this thesis is the determination of thermal effusivity of many bulk solids by a scanning photoacoustic technique. This is one of the very few methods developed to determine thermal effiisivity directly.

Photoacoustic investigation of the optical and thermal properties of selected amorphous chalcogenide semiconductors

This thesis work has mainly concentrated on the investigation of the ,optical and thermal properties of binary semiconducting chalcogenide glasses belonging to the AivB¥5x and AZBXEX families. The technique used for these studies is a relatively new one namely, the photoacoustic (PA) technique. This technique is based on the detection of acoustic signal produced in an enclosed volume when the sample is irradiated by an intensity modulated radiation. The signal produced depends upon the optical properties of the sample, and the thermal properties of the sample, backing material and the surrounding gas. For the present studies an efficient signal beam gas-microphone PA spectrometer, consisting of a high power Xenon lamp, monochromator, light beam chopper, PA cell with microphone and lock-in amplifier, has been set up. Two PA cells have been fabricated: one for room temperature measurements and another for measurements at high temperatures. With the high temperature PA cell measurements can be taken upto 250°C. Provisions are incorporated. in both the cells to change the volume and to use different backing materials for the sample. The cells have been calibrated by measuring the frequency response of the cells using carbon black as the sample

Feature Extraction Methods Based on Linear Predictive Coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam

Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations

PERFORMANCE OF DIFFERENT CLASSIFIERS IN SPEECH RECOGNITION

Speech is the most natural means of communication among human beings and speech processing and recognition are intensive areas of research for the last five decades. Since speech recognition is a pattern recognition problem, classification is an important part of any speech recognition system. In this work, a speech recognition system is developed for recognizing speaker independent spoken digits in Malayalam. Voice signals are sampled directly from the microphone. The proposed method is implemented for 1000 speakers uttering 10 digits each. Since the speech signals are affected by background noise, the signals are tuned by removing the noise from it using wavelet denoising method based on Soft Thresholding. Here, the features from the signals are extracted using Discrete Wavelet Transforms (DWT) because they are well suitable for processing non-stationary signals like speech. This is due to their multi- resolutional, multi-scale analysis characteristics. Speech recognition is a multiclass classification problem. So, the feature vector set obtained are classified using three classifiers namely, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Naive Bayes classifiers which are capable of handling multiclasses. During classification stage, the input feature vector data is trained using information relating to known patterns and then they are tested using the test data set. The performances of all these classifiers are evaluated based on recognition accuracy. All the three methods produced good recognition accuracy. DWT and ANN produced a recognition accuracy of 89%, SVM and DWT combination produced an accuracy of 86.6% and Naive Bayes and DWT combination produced an accuracy of 83.5%. ANN is found to be better among the three methods.

Go Mic review

Short review of the Go Mic portable microphone by Samson which is ideal for podcasting

Verificación de los niveles de atenuación de dos tipos de protectores auditivos tipo copa en trabajadores de una empresa del sector Hidrocarburos. Barrancabermeja-Colombia 2013.

El presente estudio se realizó con el fin de verificar los niveles de atenuación de dos tipos de protectores auditivos tipo copa, bajo condiciones reales de operación en una empresa del sector hidrocarburos en Colombia. Esta verificación se llevó a cabo determinando las diferencias existentes entre los niveles de atenuación establecidos por el fabricante (NRR Teórico), los niveles de atenuación ajustados de acuerdo a la metodología propuesta por NIOSH (NRR Ajustado) y los niveles de atenuación obtenidos experimentalmente (NRR Experimental). El estudio se desarrolló sobre la evaluación de los niveles equivalentes de presión sonora, mediante dosimetrías de doble canal, valorando los niveles de ruido al interior y al exterior del pabellón auditivo de forma simultánea. Los valores obtenidos en las mediciones se ajustaron a los criterios establecidos en la norma ISO 11904 parte 1 considerando la estimación de niveles de inmisión sonora mediante técnica MIRE (Microphone In a Real Ear) corregidos a condiciones de campo libre. Los resultados obtenidos permitieron establecer diferencias significativas entre los niveles de atenuación teóricos, ajustados y experimentales, aspecto que brinda oportunidades de mejora cuando se trata de seleccionar y mantener elementos de protección personal auditiva como estrategia preventiva primaria en el propósito de la disminución de la probabilidad de ocurrencia de hipoacusia neurosensorial inducida por ruido en el lugar de trabajo.

The calibration and use of probe-test microphones

This paper examines the characteristics of the probe-tube microphone and its use in measuring sound pressure in the ear canal. Specifically the paper studies the free field and the coupler calibrations of several probe-tube microphones with tubes of different sizes and determines which characteristics of a probe tube are necessary for accurate measurements in both a free field and in a closed coupler.

Hearing-aid volume control adjustment by deaf children

This paper examines hearing aid volume control adjustments by deaf children. Specifically the study looks at how accurately deaf children set the volume controls of their hearing aids, if deaf children are able to comprehend the effects of talker-microphone distance on stimulus intensity, and do deaf children use their hearing aids to monitor their own voices. Access to thesis is restricted. Contact Archives and Rare Books.

Cancellation of siren noise from two way voice communications inside emergency vehicles

Sirens used by police, fire and paramedic vehicles have been designed so that they can be heard over large distances, but unfortunately the siren noise enters the vehicle and corrupts intelligibility of voice communications from the emergency vehicle to the control room. Often the siren needs to be turned off to enable the control room to hear what is being said. This paper discusses a siren noise filter system that is capable of removing the siren noise picked up by the two-way radio microphone inside the vehicle. The removal of the siren noise improves the response time for emergency vehicles and thus save lives. To date, the system has been trialed within a fire tender in a non-emergency situation, with good results.

Cancellation of siren noise from two-way voice communication inside emergency vehicles

The development of an adaptive filter system, capable of reducing significantly the effect of siren noise within the cab of an emergency vehicle, is described. The system is capable of removing the siren noise picked up by the radio microphone inside the vehicle, without degrading the wanted voice signal, thus allowing the siren to be used at all times.

Optical sensors for monitoring water uptake in plants

The monitoring of water uptake in plants is becoming increasingly important. Optical sensors offer considerable advantages over conventional methods and several sensors have been developed including an optical potometer that monitors water uptake from individual roots, the detection of xylem cavitation using audio acoustic emissions with an interferometric force feedback microphone, and an optical fiber displacement transducer that detects changes in leaf thickness in relation to leaf-water potential.