Noise-induced hearing lossNoise-induced hearing loss (NIHL) is a hearing impairment resulting from exposure to loud sound. People may have a loss of perception of a narrow range of frequencies or impaired perception of sound including sensitivity to sound or ringing in the ears. When exposure to hazards such as noise occur at work and is associated with hearing loss, it is referred to as occupational hearing loss. Hearing may deteriorate gradually from chronic and repeated noise exposure (such as to loud music or background noise) or suddenly from exposure to impulse noise, which is a short high intensity noise (such as a gunshot or airhorn).
Hearing lossHearing loss is a partial or total inability to hear. Hearing loss may be present at birth or acquired at any time afterwards. Hearing loss may occur in one or both ears. In children, hearing problems can affect the ability to acquire spoken language, and in adults it can create difficulties with social interaction and at work. Hearing loss can be temporary or permanent. Hearing loss related to age usually affects both ears and is due to cochlear hair cell loss. In some people, particularly older people, hearing loss can result in loneliness.
Viterbi algorithmThe Viterbi algorithm is a dynamic programming algorithm for obtaining the maximum a posteriori probability estimate of the most likely sequence of hidden states—called the Viterbi path—that results in a sequence of observed events, especially in the context of Markov information sources and hidden Markov models (HMM). The algorithm has found universal application in decoding the convolutional codes used in both CDMA and GSM digital cellular, dial-up modems, satellite, deep-space communications, and 802.
Sensorineural hearing lossSensorineural hearing loss (SNHL) is a type of hearing loss in which the root cause lies in the inner ear or sensory organ (cochlea and associated structures) or the vestibulocochlear nerve (cranial nerve VIII). SNHL accounts for about 90% of reported hearing loss . SNHL is usually permanent and can be mild, moderate, severe, profound, or total. Various other descriptors can be used depending on the shape of the audiogram, such as high frequency, low frequency, U-shaped, notched, peaked, or flat.
Markov decision processIn mathematics, a Markov decision process (MDP) is a discrete-time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying optimization problems solved via dynamic programming. MDPs were known at least as early as the 1950s; a core body of research on Markov decision processes resulted from Ronald Howard's 1960 book, Dynamic Programming and Markov Processes.
Modeling and simulationModeling and simulation (M&S) is the use of models (e.g., physical, mathematical, behavioral, or logical representation of a system, entity, phenomenon, or process) as a basis for simulations to develop data utilized for managerial or technical decision making. In the computer application of modeling and simulation a computer is used to build a mathematical model which contains key parameters of the physical model. The mathematical model represents the physical model in virtual form, and conditions are applied that set up the experiment of interest.
Frame synchronizationIn telecommunication, frame synchronization or framing is the process by which, while receiving a stream of framed data, incoming frame alignment signals (i.e., a distinctive bit sequences or syncwords) are identified (that is, distinguished from data bits), permitting the data bits within the frame to be extracted for decoding or retransmission. If the transmission is temporarily interrupted, or a bit slip event occurs, the receiver must re-synchronize.
Hidden Markov modelA hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process — call it — with unobservable ("hidden") states. As part of the definition, HMM requires that there be an observable process whose outcomes are "influenced" by the outcomes of in a known way.
Markov information sourceIn mathematics, a Markov information source, or simply, a Markov source, is an information source whose underlying dynamics are given by a stationary finite Markov chain. An information source is a sequence of random variables ranging over a finite alphabet , having a stationary distribution. A Markov information source is then a (stationary) Markov chain , together with a function that maps states in the Markov chain to letters in the alphabet .
Minimum mean square errorIn statistics and signal processing, a minimum mean square error (MMSE) estimator is an estimation method which minimizes the mean square error (MSE), which is a common measure of estimator quality, of the fitted values of a dependent variable. In the Bayesian setting, the term MMSE more specifically refers to estimation with quadratic loss function. In such case, the MMSE estimator is given by the posterior mean of the parameter to be estimated.
Weighted arithmetic meanThe weighted arithmetic mean is similar to an ordinary arithmetic mean (the most common type of average), except that instead of each of the data points contributing equally to the final average, some data points contribute more than others. The notion of weighted mean plays a role in descriptive statistics and also occurs in a more general form in several other areas of mathematics. If all the weights are equal, then the weighted mean is the same as the arithmetic mean.
Shrinkage (statistics)In statistics, shrinkage is the reduction in the effects of sampling variation. In regression analysis, a fitted relationship appears to perform less well on a new data set than on the data set used for fitting. In particular the value of the coefficient of determination 'shrinks'. This idea is complementary to overfitting and, separately, to the standard adjustment made in the coefficient of determination to compensate for the subjunctive effects of further sampling, like controlling for the potential of new explanatory terms improving the model by chance: that is, the adjustment formula itself provides "shrinkage.
