a quantitative study of fault tolerance noise immunity and generalization ability of mlps an analysis of the influence of weight and input perturbations in a multilayer perceptron mlp is made in this article quantitative measurements of fault tolerance noise immunity and generalization ability are provided from the expressions obtained it is possible to justify some previously reported conjectures and experimentally obtained results e g the influence of weight magnitudes the relation between training with noise and the generalization ability the relation between fault tolerance and the generalization ability the measurements introduced here are explicitly related to the mean squared error degradation in the presence of perturbations thus constituting a selection criterion between different alternatives of weight configurations moreover they allow us to predict the degradation of the learning performance of an mlp when its weights or inputs are deviated from their nominal values and thus the behavior of a physical implementation can be evaluated before the weights are mapped on it according to its accuracy

role of mask pattern in intelligibility of ideal binary masked noisy speech intelligibility of ideal binary masked noisy speech was measured on a group of normal hearing individuals across mixture signal to noise ratio snr levels masker types and local criteria for forming the binary mask the binary mask is computed from time frequency decompositions of target and masker signals using two different schemes an ideal binary mask computed by thresholding the local snr within time frequency units and a target binary mask computed by comparing the local target energy against the long term average speech spectrum by depicting intelligibility scores as a function of the difference between mixture snr and local snr threshold alignment of the performance curves is obtained for a large range of mixture snr levels large intelligibility benefits are obtained for both sparse and dense binary masks when an ideal mask is dense with many ones the effect of changing mixture snr level while fixing the mask is significant whereas for more sparse masks the effect is small or insignificant

stochastic bifurcation models we study an ordinary differential equation controlled by a stochastic process we present results on existence and uniqueness of solutions on associated local times trotter and ray knight theorems and on time and direction of bifurcation a relationship with lipschitz approximations to brownian paths is also discussed

fast exact multiplication by the hessian just storing the hessian h the matrix of second derivatives 2e wi wj of the error e with respect to each pair of weights of a large neural network is difficult since a common use of a large matrix like h is to compute its product with various vectors we derive a technique that directly calculates hv where v is an arbitrary vector to calculate hv we first define a differential operator rv f w r f w rv r 0 note that rv w hv and rv w v and then apply rv to the equations used to compute w the result is an exact and numerically stable procedure for computing hv which takes about as much computation and is about as local as a gradient evaluation we then apply the technique to a one pass gradient calculation algorithm backpropagation a relaxation gradient calculation algorithm recurrent backpropagation and two stochastic gradient calculation algorithms boltzmann machines and weight perturbation finally we show that this technique can be used at the heart of many iterative techniques for computing various properties of h obviating any need to calculate the full hessian

learning algorithm for boltzmann machines with additive weight and bias noise this brief presents analytical results on the effect of additive weight bias noise on a boltzmann machine bm in which the unit output is in 1 1 instead of 0 1 with such noise it is found that the state distribution is yet another boltzmann distribution but the temperature factor is elevated thus the desired gradient ascent learning algorithm is derived and the corresponding learning procedure is developed this learning procedure is compared with the learning procedure applied to train a bm with noise it is found that these two procedures are identical therefore the learning algorithm for noise free bms is suitable for implementing as an online learning algorithm for an analog circuit implemented bm even if the variances of the additive weight noise and bias noise are unknown

variable selection in kernel regression using measurement error selection likelihoods abstractthis article develops a nonparametric shrinkage and selection estimator via the measurement error selection likelihood approach recently proposed by stefanski wu and white the measurement error kernel regression operator mekro has the same form as the nadaraya watson kernel estimator but optimizes a measurement error model selection likelihood to estimate the kernel bandwidths much like lasso or cosso solution paths mekro results in solution paths depending on a tuning parameter that controls shrinkage and selection via a bound on the harmonic mean of the pseudo measurement error standard deviations we use small sample corrected aic to select the tuning parameter large sample properties of mekro are studied and small sample properties are explored via monte carlo experiments and applications to data supplementary materials for this article are available online

learning unrealizable tasks from minimum entropy queries in supervised learning learning from queries rather than from random examples can improve generalization performance significantly we study the performance of query learning for unrealizable tasks where the student cannot learn from the perfectly as a simple model scenario of this kind we consider a linear perceptron student learning a general nonlinear perceptron teacher two kinds of queries for maximum information gain i e minimum entropy are investigated minimum student space entropy msse queries which are appropriate if the teacher space is unknown and minimum teacher space entropy mtse queries which can be used if the teacher space is assumed to be known but a student of a simpler form has deliberately been chosen we find that for msse queries the structure of the student space determines the efficacy of query learning mtse queries on the other hand which we investigate for the extreme case of a binary perceptron teacher lead to a higher generalization error than random examples due to a lack of feedback about the progress of the student in the way queries are selected

on skew brownian motion we consider the stochastic equation x t w t beta l x 0 t where w is a standard wiener process and l x 0 cdot is the local time at zero of the unknown process x there is a unique solution x and it is adapted to the fields of w if beta leq 1 but no solutions exist if beta 1 in the former case setting alpha beta 1 2 the unique solution x is distributed as a skew brownian motion with parameter alpha this is a diffusion obtained from standard wiener process by independently altering the signs of the excursions away from zero each excursion being positive with probability alpha and negative with probability 1 alpha finally we show that skew brownian motion is the weak limit as n rightarrow infty of n 1 2 s lbrack nt rbrack where s n is a random walk with exceptional behavior at the origin

predicting binaural speech intelligibility using the signal to noise ratio in the envelope power spectrum domain this study proposes a binaural extension to the multi resolution speech based envelope power spectrum model mr sepsm jorgensen ewert and dau 2013 j acoust soc am 134 436 446 it consists of a combination of better ear be and binaural unmasking processes implemented as two monaural realizations of the mr sepsm combined with a short term equalization cancellation process and uses the signal to noise ratio in the envelope domain snrenv as the decision metric the model requires only two parameters to be fitted per speech material and does not require an explicit frequency weighting the model was validated against three data sets from the literature which covered the following effects the number of maskers the masker types speech shaped noise ssn speech modulated ssn babble and reversed speech the masker s azimuths reverberation on the target and masker and the interaural time difference of the target and masker the pearson correlation coefficient between the simulated spee

convergence analyses on on line weight noise injection based training algorithms for mlps injecting weight noise during training is a simple technique that has been proposed for almost two decades however little is known about its convergence behavior this paper studies the convergence of two weight noise injection based training algorithms multiplicative weight noise injection with weight decay and additive weight noise injection with weight decay we consider that they are applied to multilayer perceptrons either with linear or sigmoid output nodes let w t be the weight vector let v w be the corresponding objective function of the training algorithm let 0 be the weight decay constant and let t be the step size we show that if t 0 then with probability one e w t 22 is bound and limt w t 2 exists based on these two properties we show that if t 0 t t and t t 2 then with probability one these algorithms converge moreover w t converges with probability one to a point where wv w 0

a detailed study on the effects of noise on speech intelligibility a wavelet representation of speech was used to display the instantaneous amplitude and phase within 1 4 octave frequency bands representing the envelope and the carrier within each band adding stationary noise alters the wavelet pattern which can be understood as a combination of three simultaneously occurring subeffects two effects on the wavelet levels one systematic and one stochastic and one effect on the wavelet phases specific types of signal processing were applied to speech which allowed each effect to be either included or excluded the impact of each effect and of combinations on speech intelligibility was measured with cvc s it appeared that the systematic level effect i e the increase of each speech wavelet intensity with the mean noise intensity has the most degrading effect on speech intelligibility which is in accordance with measures such as the modulation transfer function and the speech transmission index however also the introduction of stochastic level fluctuations an

the importance for speech intelligibility of random fluctuations in steady background noise spectrally shaped steady noise is commonly used as a masker of speech the effects of inherent random fluctuations in amplitude of such a noise are typically ignored here the importance of these random fluctuations was assessed by comparing two cases for one speech was mixed with steady speech shaped noise and n channel tone vocoded a process referred to as signal domain mixing sdm this preserved the random fluctuations of the noise for the second the envelope of speech alone was extracted for each vocoder channel and a constant was added corresponding to the root mean square value of the noise envelope for that channel this is referred to as envelope domain mixing edm it removed the random fluctuations of the noise sinusoidally modulated noise and a single talker were also used as backgrounds with both sdm and edm speech intelligibility was measured for n 12 19 and 30 with the target to background ratio fixed at 7 db for sdm performance was best for the speech background and worst for the steady noise for edm this pattern was reversed intelligibility with steady noise was consistently very poor for sdm but near ceiling for edm demonstrating that the random fluctuations in steady noise have a large effect

the effects of adding noise during backpropagation training on a generalization performance we study the effects of adding noise to the inputs outputs weight connections and weight changes of multilayer feedforward neural networks during backpropagation training we rigorously derive and analyze the objective functions that are minimized by the noise affected training processes we show that input noise and weight noise encourage the neural network output to be a smooth function of the input or its weights respectively in the weak noise limit noise added to the output of the neural networks only changes the objective function by a constant hence it cannot improve generalization input noise introduces penalty terms in the objective function that are related to but distinct from those found in the regularization approaches simulations have been performed on a regression and a classification problem to further substantiate our analysis input noise is found to be effective in improving the generalization performance for both problems however weight noise is found to be effective in improving the generalization performance only for the classification problem other forms of noise have practically no effect on generalization

on the significance of phase in the short term fourier spectrum for speech intelligibility this paper investigates the significance of the magnitude or the phase in the short term fourier spectrum for speech intelligibility as a function of the time window length for a wide range of window lengths 1 16 2048 ms two hybrid signals were obtained by a cross wise combination of the magnitude and phase spectra of speech and white noise speech intelligibility data showed the significance of the phase spectrum for longer windows 256 ms and for very short windows 4 ms and that of the magnitude spectrum for medium range window lengths the hybrid signals used in the intelligibility test were analyzed in terms of the preservation of the original narrow band speech envelopes correlations between the narrow band envelopes of the original speech and the hybrid signals show a similar pattern as a function of window length this result illustrates the importance of the preservation of narrow band envelopes for speech intelligibility the observed significance of the phase spectrum in recovering the narrow band envelopes for the long term windows and for the very short term windows is discussed

similarities of error regularization sigmoid gain scaling target smoothing and training with jitter the generalization performance of feedforward layered perceptrons can in many cases be improved either by smoothing the target via convolution regularizing the training error with a smoothing constraint decreasing the gain i e slope of the sigmoid nonlinearities or adding noise i e jitter to the input training data in certain important cases the results of these procedures yield highly similar results although at different costs training with jitter for example requires significantly more computation than sigmoid scaling

relations between frequency selectivity temporal fine structure processing and speech reception in impaired hearing frequency selectivity temporal fine structure tfs processing and speech reception were assessed for six normal hearing nh listeners ten sensorineurally hearing impaired hi listeners with similar high frequency losses and two listeners with an obscure dysfunction od tfs processing was investigated at low frequencies in regions of normal hearing through measurements of binaural masked detection tone lateralization and monaural frequency modulation fm detection lateralization and fm detection thresholds were measured in quiet and in background noise speech reception thresholds were obtained for full spectrum and lowpass filtered sentences with different interferers both the hi listeners and the od listeners showed poorer performance than the nh listeners in terms of frequency selectivity tfs processing and speech reception while a correlation was observed between the monaural and binaural tfs processing deficits in the hi listeners no relation was found between tfs processing and frequency selectivity the effect of noise on tfs processing was not larger for the hi listeners than for the nh listeners finally tfs processing performance was correlated with speech reception in a two talker background and lateralized noise but not in amplitude modulated noise the results provide constraints for future models of impaired auditory signal processing

a cross linguistic study of speech modulation spectra languages show systematic variation in their sound patterns and grammars accordingly they have been classified into typological categories such as stress timed vs syllable timed or head complement hc vs complement head ch to date it has remained incompletely understood how these linguistic properties are reflected in the acoustic characteristics of speech in different languages in the present study the amplitude modulation am and frequency modulation fm spectra of 1797 utterances in ten languages were analyzed overall the spectra were found to be similar in shape across languages however significant effects of linguistic factors were observed on the am spectra these differences were magnified with a perceptually plausible representation based on the modulation index a measure of the signal to noise ratio at the output of a logarithmic modulation filterbank the maximum value distinguished between hc and ch languages with the exception of turkish while the exact frequency of this ma

the equivalence of weak strong and complete convergence in l_1 for kernel density estimates theorem 1 let k be a nonnegative borel measurable function on rd with f k x dx 1 then the following conditions are equivalent i j 0 in probability as n oo some f ii jn o 0 in probability as n s oo all f iii jn s 0 almost surely as n oo all f iv jn 0 exponentially as n oc i e for all e 0 there exist r no 0 such that p jne no all f v lim0h 0 and limnnhd oo also v implies iv when k is merely absolutely integrable and f k x dx 1 0

predicting speech intelligibility based on a correlation metric in the envelope power spectrum domain a speech intelligibility prediction model is proposed that combines the auditory processing front end of the multi resolution speech based envelope power spectrum model mr sepsm jorgensen ewert and dau 2013 j acoust soc am 134 1 436 446 with a correlation back end inspired by the short time objective intelligibility measure stoi taal hendriks heusdens and jensen 2011 ieee trans audio speech lang process 19 7 2125 2136 this hybrid model named sepsmcorr is shown to account for the effects of stationary and fluctuating additive interferers as well as for the effects of non linear distortions such as spectral subtraction phase jitter and ideal time frequency segregation itfs the model shows a broader predictive range than both the original mr sepsm which fails in the phase jitter and itfs conditions and stoi which fails to predict the influence of fluctuating interferers albeit with lower accuracy than the source models in some individual conditions similar to othe

a stochastic version of the delta rule abstract self organizing networks of neuron like elements naturally lead to high dimensional nonlinear parameter spaces which prove difficult to search back propagation is one of the simplest neural network connectionist models that uses a gradient descent delta rule in a high multi dimensional parameter space search in such a space is subject to many difficulties including minima that are locally stable but do not at the same time provide solutions search time can also be shown under relatively good conditions to scale poorly although gradient descent in error is attractive as a weak learning method it also seems to suffer from many search inefficiencies a principle is proposed about the relation between constrained local noise injections and global search the delta rule is modified to include synaptic noise in the transmission of information and modification of the connection strength this stochastic version of the delta rule seems to promote escape from poor locally stable minima and can improve convergence speed and likehood

improving the generalization properties of radial basis function neural networks an important feature of radial basis function neural networks is the existence of a fast linear learning algorithm in a network capable of representing complex nonlinear mappings satisfactory generalization in these networks requires that the network mapping be sufficiently smooth we show that a modification to the error functional allows smoothing to be introduced explicitly without significantly affecting the speed of training a simple example is used to demonstrate the resulting improvement in the generalization properties of the network

lenses in skew brownian flow we consider a stochastic flow in which individual particles follow skew brownian motions with each one of these processes driven by the same brownian motion one does not have uniqueness for the solutions of the corresponding stochastic differential equation simultaneously for all real initial conditions due to this lack of the simultaneous strong uniqueness for the whole system of stochastic differential equations the flow contains lenses that is pairs of skew brownian motions which start at the same point bifurcate and then coalesce in a finite time the paper contains qualitative and quantitative distributional results on the geometry of the flow and lenses

factors affecting masking release for speech in modulated noise for normal hearing and hearing impaired listeners the speech reception threshold for sentences in stationary noise and in several amplitude modulated noises was measured for 8 normal hearing listeners 29 sensorineural hearing impaired listeners and 16 normal hearing listeners with simulated hearing loss this approach makes it possible to determine whether the reduced benefit from masker modulations as often observed for hearing impaired listeners is due to a loss of signal audibility or due to suprathreshold deficits such as reduced spectral and temporal resolution which were measured in four separate psychophysical tasks results show that the reduced masking release can only partly be accounted for by reduced audibility and that when considering suprathreshold deficits the normal effects associated with a raised presentation level should be taken into account in this perspective reduced spectral resolution does not appear to qualify as an actual suprathreshold deficit while reduced temporal resolution does temporal resolution and age are shown to be the main factors governing masking release for speech in modulated noise accounting for more than half of the intersubject variance their influence appears to be related to the processing of mainly the higher stimulus frequencies results based on calculations of the speech intelligibility index in modulated noise confirm these conclusions

robustness and regularization of support vector machines we consider regularized support vector machines svms and show that they are precisely equivalent to a new robust optimization formulation we show that this equivalence of robust optimization and regularization has implications for both algorithms and analysis in terms of algorithms the equivalence suggests more general svm like algorithms for classification that explicitly build in protection to noise and at the same time control overfitting on the analysis front the equivalence of robustness and regularization provides a robust optimization interpretation for the success of regularized svms we use this new robustness interpretation of svms to give a new proof of consistency of kernelized svms thus establishing robustness as the reason regularized svms generalize well

functional approximation by feed forward networks a least squares approach to generalization this paper considers a least squares approach to function approximation and generalization the particular problem addressed is one in which the training data are noiseless and the requirement is to define a mapping that approximates the data and that generalizes to situations in which data samples are corrupted by noise in the input variables the least squares approach produces a generalizer that has the form of a radial basis function network for a finite number of training samples the finite sample approximation is valid provided that the perturbations due to noise on the expected operating conditions are large compared to the sample spacing in the data space in the other extreme of small noise perturbations a particular parametric form must be assumed for the generalizer it is shown that better generalization will occur if the error criterion used in training the generalizer is modified by the addition of a specific regularization term this is illustrated by an approximator that has a feedforward architecture and is applied to the problem of point source location using the outputs of an array of receivers in the focal plane of a lens

a practical bayesian framework for backpropagation networks a quantitative and practical bayesian framework is described for learning of mappings in feedforward networks the framework makes possible 1 objective comparisons between solutions using alternative network architectures 2 objective stopping rules for network pruning or growing procedures 3 objective choice of magnitude and type of weight decay terms or additive regularizers for penalizing large weights etc 4 a measure of the effective number of well determined parameters in a model 5 quantified estimates of the error bars on network parameters and on network output and 6 objective comparisons with alternative learning and interpolation models such as splines and radial basis functions the bayesian evidence automatically embodies occam s razor penalizing overflexible and overcomplex models the bayesian approach helps detect poor underlying assumptions in learning models for learning models well matched to a problem a good correlation between generalization ability and the bayesian evidence is obtained

speech enhancement using a minimum mean square error short time spectral amplitude estimator this paper focuses on the class of speech enhancement systems which capitalize on the major importance of the short time spectral amplitude stsa of the speech signal in its perception a system which utilizes a minimum mean square error mmse stsa estimator is proposed and then compared with other widely used systems which are based on wiener filtering and the spectral subtraction algorithm in this paper we derive the mmse stsa estimator based on modeling speech and noise spectral components as statistically independent gaussian random variables we analyze the performance of the proposed stsa estimator and compare it with a stsa estimator derived from the wiener estimator we also examine the mmse stsa estimator under uncertainty of signal presence in the noisy observations in constructing the enhanced signal the mmse stsa estimator is combined with the complex exponential of the noisy phase it is shown here that the latter is the mmse estimator of the complex exponential of the original phase which does not affect the stsa estimation the proposed approach results in a significant reduction of the noise and provides enhanced speech with colorless residual noise the complexity of the proposed algorithm is approximately that of other systems in the discussed class

label embedding a frugal baseline for text recognition the standard approach to recognizing text in images consists in first classifying local image regions into candidate characters and then combining them with high level word models such as conditional random fields this paper explores a new paradigm that departs from this bottom up view we propose to embed word labels and word images into a common euclidean space given a word image to be recognized the text recognition problem is cast as one of retrieval find the closest word label in this space this common space is learned using the structured svm framework by enforcing matching label image pairs to be closer than non matching pairs this method presents several advantages it does not require ad hoc or costly pre post processing operations it can build on top of any state of the art image descriptor fisher vectors in our case it allows for the recognition of never seen before words zero shot recognition and the recognition process is simple and efficient as it amounts to a nearest neighbor search experiments are performed on challenging datasets of license plates and scene text the main conclusion of the paper is that with such a frugal approach it is possible to obtain results which are competitive with standard bottom up approaches thus establishing label embedding as an interesting and simple to compute baseline for text recognition

on enhancement of spectral contrast in speech for hearing impaired listeners a digital processing method is described for altering spectral contrast the difference in amplitude between spectral peaks and valleys in natural utterances speech processed with programs implementing the contrast alteration procedure was presented to listeners with moderate to severe sensorineural hearing loss the task was a three alternative b d or g stop consonant identification task for consonants at a fixed location in short nonsense utterances overall tokens with enhanced contrast showed moderate gains in percentage correct stop consonant identification when compared to unaltered tokens conversely reducing spectral contrast generally reduced percent correct stop consonant identification contrast alteration effects were inconsistent for utterances containing d the observed contrast effects also interacted with token intelligibility

multiobjective optimization for model selection in kernel methods in regression regression plays a major role in many scientific and engineering problems the goal of regression is to learn the unknown underlying function from a set of sample vectors with known outcomes in recent years kernel methods in regression have facilitated the estimation of nonlinear functions however two major interconnected problems remain open the first problem is given by the bias versus variance tradeoff if the model used to estimate the underlying function is too flexible i e high model complexity the variance will be very large if the model is fixed i e low complexity the bias will be large the second problem is to define an approach for selecting the appropriate parameters of the kernel function to address these two problems this paper derives a new smoothing kernel criterion which measures the roughness of the estimated function as a measure of model complexity then we use multiobjective optimization to derive a criterion for selecting the parameters of that kernel the goal of this criterion is to find a tradeoff between the bias and the variance of the learned function that is the goal is to increase the model fit while keeping the model complexity in check we provide extensive experimental evaluations using a variety of problems in machine learning pattern recognition and computer vision the results demonstrate that the proposed approach yields smaller estimation errors as compared with methods in the state of the art

on overfitting generalization and randomly expanded training sets an algorithmic procedure is developed for the random expansion of a given training set to combat overfitting and improve the generalization ability of backpropagation trained multilayer perceptrons mlps the training set is k means clustered and locally most entropic colored gaussian joint input output probability density function estimates are formed per cluster the number of clusters is chosen such that the resulting overall colored gaussian mixture exhibits minimum differential entropy upon global cross validated shaping numerical studies on real data and synthetic data examples drawn from the literature illustrate and support these theoretical developments

local time flow related to skew brownian motion burdzy s research partially supported by nsf grant dms 9700721 chen s research partially supported by nsa grant mda904 99 1 0104

intensity discrimination of gaussian windowed tones indications for the shape of the auditory frequency time window the just noticeable difference in intensity jnd i was measured for 1 khz tones with a gaussian shaped envelope as a function of their spectro temporal shape the stimuli with constant energy and a constant product of bandwidth and duration ranged from a long duration narrow band tone to a short duration broadband click the jnd i was measured in three normal hearing listeners at sensation levels of 0 10 20 and 30 db in 35 db a spl pink noise at intermediate sensation levels jnd i depends on the spectro temporal shape at the extreme shapes tones and clicks intensity discrimination performance is best whereas at intermediate shapes the jnd i is larger similar results are observed at a higher overall sound level and at a higher carrier frequency the maximum jnd i is observed for stimuli with an effective bandwidth of about 1 3 octave and an effective duration of 4 ms at 1 khz 1 ms at 4 khz a generalized multiple window model is proposed that assumes that the spectro temporal domain is partitioned into internal auditory frequency time windows the model predicts that intensity discrimination thresholds depend upon the number of windows excited by a signal jnd i is largest for stimuli covering one window

nonparametric density estimation the l 1 view differentiation of integrals consistency lower bounds for rates of convergence rates of convergence in l1 the automatic kernel estimate l1 and pointwise convergence estimates related to the kernel estimate and the histogram estimate simulation inequalities and random variate generation the transformed kernel estimate applications in discrimination operations on density estimates estimators based on orthogonal series index

learning with noise in a linear perceptron the learning of a set of p random patterns in a linear perceptron is studied in the limit of a large n of input units with noise on the weights inputs and output the problem is formulated in continuous time as a langevin equation and the first task is to evaluate the response or green function for the system white noise on the output is shown to correspond to spatially correlated weight noise acting only in a subspace of the weight space it is shown that the input noise acts as a simple weight decay with a size proportional to the load parameter alpha p n with no weight decay the relaxation time diverges at alpha 1 with a weight decay it becomes shorter and finite for alpha 1 but at the cost of a larger asymptotic learning error that is found analytically it is shown that a small weight decay decreases the effect of noise on the weights or outputs

benefit of high rate envelope cues in vocoder processing effect of number of channels and spectral region in cochlear implants or vocoder simulations of cochlear implants the transmission of envelope cues at high rates related to voice fundamental frequency f0 may be limited by the widths of the filters used to form the channels and or by the cutoff frequency flp of the low pass filters used for envelope extraction the effect of varying flp in tone and noise vocoders was investigated for channel numbers n from 6 to 18 as n increased the widths of the channels decreased the value of flp was 45hz envelope or e filter or 180hz pitch or p filter the following combinations of cutoff frequencies were used for channels below and above 1500hz respectively ee pe ep and pp results from a competing talker task showed that the tone vocoder led to better intelligibility than the noise vocoder the pp condition led to the best intelligibility and the ee condition to the worst for n 6 intelligibility was better for condition pe than for condition ep for n 18 the reverse was true the results

training with noise is equivalent to tikhonov regularization it is well known that the addition of noise to the input data of a neural network during training can in some circumstances lead to significant improvements in generalization performance previous work has shown that such training with noise is equivalent to a form of regularization in which an extra term is added to the error function however the regularization term which involves second derivatives of the error function is not bounded below and so can lead to difficulties if used directly in a learning algorithm based on error minimization in this paper we show that for the purposes of network training the regularization term can be reduced to a positive semi definite form that involves only first derivatives of the network mapping for a sum of squares error function the regularization term belongs to the class of generalized tikhonov regularizers direct minimization of the regularized error function provides a practical alternative to training with noise

architecture independent approximation of functions we show that minimizing the expected error of a feedforward network over a distribution of weights results in an approximation that tends to be independent of network size as the number of hidden units grows this minimization can be easily performed and the complexity of the resulting function implemented by the network is regulated by the variance of the weight distribution for a fixed variance there is a number of hidden units above which either the implemented function does not change or the change is slight and tends to zero as the size of the network grows in sum the control of the complexity depends on only the variance not the architecture provided it is large enough

generalization ability of perceptrons with continuous outputs in this paper we examine the influence of different input output relations on the generalization ability of a single layer perceptron the input output relations can be linear binary or sigmoid with this choice we take into account most of the cases which are of present interest the generalization problem will be realizable or unrealizable if the input output relations for teacher and student are identical or not we show that sometimes it can have a positive effect on the generalization ability if one learns with errors

classification of temporal sequences via prediction using the simple recurrent neural network abstract an approach to classify temporal sequences using the simple recurrent neural network srnn is developed in this paper a classification problem is formulated as a component prediction problem and two training methods are described to train a single srnn to predict the components of temporal sequences belonging to multiple classes issues related to the selection of the dimension of the context vector and the influence of the context vector on classification are identified and investigated the use of a different initial context vector for each class is proposed as a means to improve classification and a classification rule which incorporates the different initial context vectors is formulated a systematic method in which the srnn is trained with noisy exemplars is developed to enhance the classification performance of the network a 4 class localized object classification problem is selected to demonstrate that a a single srnn can be trained to classify real multi class sequences via component prediction b the classification accuracy can be improved by using a distinguishing initial context vector for each class and c the classification accuracy of the srnn can be improved significantly by using the distinguishing initial context vector in conjunction with the systematic re training method it is concluded that through the approach developed in this paper the srnn can robustly classify temporal sequences which may have an unequal number of components

representation and generalization properties of class entropy networks using conditional class entropy cce as a cost function allows feedforward networks to fully exploit classification relevant information cce based networks arrange the data space into partitions which are assigned unambiguous symbols and are labeled by class information by this labeling mechanism the network can model the empirical data distribution at the local level region labeling evolves with the network training process which follows a plastic algorithm the paper proves several theoretical properties about the performance of cce based networks and considers both convergence during training and generalization ability at run time in addition analytical criteria and practical procedures are proposed to enhance the generalization performance of the trained networks experiments on artificial and real world domains confirm the accuracy of this class of networks and witness the validity of the described methods

effect of spectral change enhancement for the hearing impaired using parameter values selected with a genetic algorithm chen et al j acoust soc am 131 2987 2998 2012 evaluated the effectiveness of an algorithm for enhancing spectral changes over time in improving the intelligibility of speech in background sounds for hearing impaired subjects the processing improved intelligibility for speech in steady speech spectrum noise ssn but tended to impair intelligibility in a background of two talker speech tts large individual differences were found the present study assessed whether the effectiveness of the algorithm was improved when the parameters that controlled the degree and type of enhancement were chosen individually for each subject using a genetic algorithm based on subjective preferences for speech clarity the parameter values selected by the genetic algorithm varied markedly across subjects speech intelligibility was measured for unprocessed stimuli and stimuli processed using the selected parameters with ssn and tts maskers and two signal to masker ratios smrs for each subject the intelligibi

uncomodulated glimpsing in checkerboard noise the ability of listeners to glimpse acoustic cues during the quieter sections of an interrupted noise has primarily been studied using maskers with interruptions occurring simultaneously across the entire frequency range of the masker broadband comodulated interruptions here the possibility of uncomodulated glimpsing the glimpsing of acoustic cues separated both in time and frequency was investigated to achieve this speech reception thresholds for a set of intervocalic consonants were adaptively measured in 100 hz to 10 khz pink noise divided into a varying number of frequency bands of equal energy in uncomodulated noise conditions the odd and even numbered bands were switched on and off alternately at a rate of 10 hz the spectrograms of such noises on log frequency scales resemble portions of a checkerboard glimpsing in checkerboard noise was found with maskers divided into two and four bands but not into eight bands or more further investigations showed that in the two band case this release from masking was indeed due to uncomodulated glimpsing and not simply attributable to glimpsing in one of the modulated bands in the four band case the release from masking in checkerboard noise can be accounted for without recourse to uncomodulated glimpsing interestingly conditions which allowed glimpsing resulted in greater intersubject variability the implications of these results for quantitative analyses of masker fluctuations are discussed

contribution of temporal fine structure information and fundamental frequency separation to intelligibility in a competing speaker paradigm the speech reception threshold srt for identifying a target speaker in a background speaker was measured as a function of the difference f0sep in fundamental frequency f0 between the two speakers the amount of original temporal fine structure tfs information in the mixed signals was manipulated by tone vocoding channels above a certain cutoff channel co when the natural variations in f0 of both speakers were preserved the srt did not decrease with increasing f0sep indicating that short term differences in f0 can allow perceptual segregation of two speakers even when their f0s cross when f0 variations were removed from both speakers increasing f0sep led to decreased better srts the decrease was greater for unprocessed signals than for fully tone vocoded signals however the decrease was similar for unprocessed signals and for signals with original tfs below 1600 hz suggesting that most of the benefit from increasing f0 difference depends on the use of tfs information at lower frequenci

on different facets of regularization theory this review provides a comprehensive understanding of regularization theory from different perspectives emphasizing smoothness and simplicity principles using the tools of operator theory and fourier analysis it is shown that the solution of the classical tikhonov regularization problem can be derived from the regularized functional defined by a linear differential integral operator in the spatial fourier domain state of the art research relevant to the regularization theory is reviewed covering occam s razor minimum length description bayesian theory pruning algorithms informational entropy theory statistical learning theory and equivalent regularization the universal principle of regularization in terms of kolmogorov complexity is discussed finally some prospective studies on regularization theory and beyond are suggested

image classification with the fisher vector theory and practice a standard approach to describe an image for classification and retrieval purposes is to extract a set of local patch descriptors encode them into a high dimensional vector and pool them into an image level signature the most common patch encoding strategy consists in quantizing the local descriptors into a finite set of prototypical elements this leads to the popular bag of visual words representation in this work we propose to use the fisher kernel framework as an alternative patch encoding strategy we describe patches by their deviation from an universal generative gaussian mixture model this representation which we call fisher vector has many advantages it is efficient to compute it leads to excellent results even with efficient linear classifiers and it can be compressed with a minimal loss of accuracy using product quantization we report experimental results on five standard datasets pascal voc 2007 caltech 256 sun 397 ilsvrc 2010 and imagenet10k with up to 9m images and 10k classes showing that the fv framework is a state of the art patch encoding technique

using additive noise in back propagation training the possibility of improving the generalization capability of a neural network by introducing additive noise to the training samples is discussed the network considered is a feedforward layered neural network trained with the back propagation algorithm back propagation training is viewed as nonlinear least squares regression and the additive noise is interpreted as generating a kernel estimate of the probability density that describes the training vector distribution two specific application types are considered pattern classifier networks and estimation of a nonstochastic mapping from data corrupted by measurement errors it is not proved that the introduction of additive noise to the training vectors always improves network generalization however the analysis suggests mathematically justified rules for choosing the characteristics of noise if additive noise is used in training results of mathematical statistics are used to establish various asymptotic consistency results for the proposed method numerical simulations support the applicability of the training method

distance between two skew brownian motions as a s d e with jumps and law of the hitting time in this paper we consider two skew brownian motions driven by the same brownian motion with different starting points and different skewness coefficients we show that we can describe the evolution of the distance between the two processes with a stochastic differential equation this s d e possesses a jump component driven by the excursion process of one of the two skew brownian motions using this representation we show that the local time of two skew brownian motions at their first hitting time is distributed as a simple function of a beta random variable this extends a result by burdzy and chen ann probab 29 2001 1693 1715 where the law of coalescence of two skew brownian motions with the same skewness coefficient is computed

recursive bayesian recurrent neural networks for time series modeling this paper develops a probabilistic approach to recursive second order training of recurrent neural networks rnns for improved time series modeling a general recursive bayesian levenberg marquardt algorithm is derived to sequentially update the weights and the covariance hessian matrix the main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization and stable numerical performance the framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters which represent the noise in the data and the uncertainties in the model parameters experimental investigations using artificial and real world data sets show that rnns equipped with the proposed approach outperform standard real time recurrent learning and extended kalman training algorithms for recurrent networks as well as other contemporary nonlinear neural models on time series modeling

channel selection in the modulation domain for improved speech intelligibility in noise background noise reduces the depth of the low frequency envelope modulations known to be important for speech intelligibility the relative strength of the target and masker envelope modulations can be quantified using a modulation signal to noise ratio s n mod measure such a measure can be used in noise suppression algorithms to extract target relevant modulations from the corrupted target masker envelopes for potential improvement in speech intelligibility in the present study envelopes are decomposed in the modulation spectral domain into a number of channels spanning the range of 0 30 hz target dominant modulations are identified and retained in each channel based on the s n mod selection criterion while modulations which potentially interfere with perception of the target i e those dominated by the masker are discarded the impact of modulation selective processing on the speech reception threshold for sentences in noise is assessed with normal hearing listeners results indicate that the intelligibility of noise masked speech can be improved by as much as 13 db when preserving target dominant modulations present up to a modulation frequency of 18 hz while discarding masker dominant modulations from the mixture envelopes

noise injection theoretical prospects noise injection consists of adding noise to the inputs during neural network training experimental results suggest that it might improve the generalization ability of the resulting neural network a justification of this improvement remains elusive describing analytically the average perturbed cost function is difficult and controlling the fluctuations of the random perturbed cost function is hard hence recent papers suggest replacing the random perturbed cost by a deterministic taylor approximation of the average perturbed cost function this article takes a different stance when the injected noise is gaussian noise injection is naturally connected to the action of the heat kernel this provides indications on the relevance domain of traditional taylor expansions and shows the dependence of the quality of taylor approximations on global smoothness properties of neural networks under consideration the connection between noise injection and heat kernel also enables controlling the fluctuations of

anisotropic noise injection for input variables relevance determination there are two archetypal ways to control the complexity of a flexible regressor subset selection and ridge regression in neural networks jargon they are respectively known as pruning and weight decay these techniques may also be adapted to estimate which features of the input space are relevant for predicting the output variables relevance is given by a binary indicator for subset selection and by a continuous rating for ridge regression this paper shows how to achieve such a rating for a multilayer perceptron trained with noise or jitter noise injection ni is modified in order to penalize heavily irrelevant features the proposed algorithm is attractive as it requires the tuning of a single parameter this parameter controls the complexity of the model effective number of parameters together with the rating of feature relevances effective input space dimension bounds on the effective number of parameters support that the stability of this adaptive scheme is enforced by the constraints applied to the admissible set of relevance indices the good properties of the algorithm are confirmed by satisfactory experimental results on simulated data sets

the negative effect of amplitude compression in multichannel hearing aids in the light of the modulation transfer function the article deals with the question of why multichannel amplitude compression appears to have a negative rather than a positive effect on speech intelligibility by hearing impaired listeners it is argued that the small time constants of amplitude compression diminish the temporal as well as the spectral contrasts in the speech signal according to the modulation transfer function concept this results in reduced intelligibility scores experimental evidence is reviewed indicating that the following two arguments in favor of amplitude compression in case of sensorineural hearing loss are not valid 1 to compensate for the effects of loudness recruitment and 2 to get weak consonants above threshold the author concludes that in multichannel hearing aids automatic gain control with time constants of 0 25 0 5 s should be given preference to amplitude compression

stacked denoising autoencoders learning useful representations in a deep network with a local denoising criterion we explore an original strategy for building deep networks based on stacking layers of denoising autoencoders which are trained locally to denoise corrupted versions of their inputs the resulting algorithm is a straightforward variation on the stacking of ordinary autoencoders it is however shown on a benchmark of classification problems to yield significantly lower classification error thus bridging the performance gap with deep belief networks dbn and in several cases surpassing it higher level representations learnt in this purely unsupervised fashion also help boost the performance of subsequent svm classifiers qualitative experiments show that contrary to ordinary autoencoders denoising autoencoders are able to learn gabor like edge detectors from natural image patches and larger stroke detectors from digit images this work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations

induction of finite state languages using second order recurrent networks second order recurrent networks that recognize simple finite state languages over 0 1 are induced from positive and negative examples using the complete gradient of the recurrent network and sufficient training examples to constrain the definition of the language to be induced solutions are obtained that correctly recognize strings of arbitrary length

an asymptotically efficient solution to the bandwidth problem of kernel density estimation a data driven method of choosing the bandwidth h of a kernel density estimator is heuristically motivated by considering modifications of the kullback leibler or pseudo likelihood cross validation function it is seen that this means of choosing h is asymptotically equivalent to taking the h that minimizes some compelling error criteria such as the average squared error and the integrated squared error thus for a given kernel function the bandwidth can be chosen optimally without making precise smoothness assumptions on the underlying density

an analysis of noise in recurrent neural networks convergence and generalization concerns the effect of noise on the performance of feedforward neural nets we introduce and analyze various methods of injecting synaptic noise into dynamically driven recurrent nets during training theoretical results show that applying a controlled amount of noise during training may improve convergence and generalization performance we analyze the effects of various noise parameters and predict that best overall performance can be achieved by injecting additive noise at each time step noise contributes a second order gradient term to the error function which can be viewed as an anticipatory agent to aid convergence this term appears to find promising regions of weight space in the beginning stages of training when the training error is large and should improve convergence on error surfaces with local minima the first order term is a regularization term that can improve generalization specifically it can encourage internal representations where the state nodes operate in the saturated regions of the sigmoid discriminant function while this effect can improve performance on automata inference problems with binary inputs and target outputs it is unclear what effect it will have on other types of problems to substantiate these predictions we present simulations on learning the dual parity grammar from temporal strings for all noise models and present simulations on learning a randomly generated six state grammar using the predicted best noise model

a multi resolution envelope power based model for speech intelligibility the speech based envelope power spectrum model sepsm presented by jorgensen and dau 2011 j acoust soc am 130 1475 1487 estimates the envelope power signal to noise ratio snrenv after modulation frequency selective processing changes in this metric were shown to account well for changes of speech intelligibility for normal hearing listeners in conditions with additive stationary noise reverberation and nonlinear processing with spectral subtraction in the latter condition the standardized speech transmission index 2003 iec 60268 16 fails however the sepsm is limited to conditions with stationary interferers due to the long term integration of the envelope power and cannot account for increased intelligibility typically obtained with fluctuating maskers here a multi resolution version of the sepsm is presented where the snrenv is estimated in temporal segments with a modulation filter dependent duration the multi resolution sepsm is demonstrated to account for intelligibility

phoneme recognition in vocoded maskers by normal hearing and aided hearing impaired listeners this study compared modulation benefit for phoneme recognition obtained by normal hearing nh and aided hearing impaired hi listeners consonant and vowel recognition scores were measured using nonsense syllables in the presence of a steady state noise and four vocoded speech maskers vocoded maskers were generated by modulating the steady state noise in either one or six frequency channels with the speech envelope extracted from the speech of either a single talker or a four talker babble aided hi listeners obtained lower consonant recognition scores than nh listeners in all masker conditions vowel recognition scores for aided hi listeners were comparable to nh scores except in the six channel vocoded masker conditions where they were relatively lower analysis using the extended speech intelligibility index developed by rhebergen versfeld and dreschler 2006 j acoust soc am 120 6 3988 3997 suggested that the signal to noise ratio deficit observed in aided hi listeners was largely due to uncompensated audibility loss there was no significant difference between modulation masking release obtained by nh and aided hi listeners for both consonant and vowel recognition

factors influencing intelligibility of ideal binary masked speech implications for noise reduction the application of the ideal binary mask to an auditory mixture has been shown to yield substantial improvements in intelligibility this mask is commonly applied to the time frequency t f representation of a mixture signal and eliminates portions of a signal below a signal to noise ratio snr threshold while allowing others to pass through intact the factors influencing intelligibility of ideal binary masked speech are not well understood and are examined in the present study specifically the effects of the local snr threshold input snr level masker type and errors introduced in estimating the ideal mask are examined consistent with previous studies intelligibility of binary masked stimuli is quite high even at 10db snr for all maskers tested performance was affected the most when the masker dominated t f units were wrongly labeled as target dominated t f units performance plateaued near 100 correct for snr thresholds ranging from 20to5db the existence of the plateau region suggests that

generalization in a linear perceptron in the presence of noise the authors study the evolution of the generalization ability of a simple linear perceptron with n inputs which learns to imitate a teacher perceptron the system is trained on p alpha n example inputs drawn from some distribution and the generalization ability is measured by the average agreement with the teacher on test examples drawn from the same distribution the dynamics may be solved analytically and exhibits a phase transition from imperfect to perfect generalization at alpha 1 when there are no errors static noise in the training examples if the examples are produced by an erroneous teacher overfitting is observed i e the generalization error starts to increase after a finite time of training it is shown that a weight decay of the same size as the variance of the noise errors on the teacher improves on the generalization and suppresses the overfitting the generalization error as a function of time is calculated numerically for various values of the parameters finally dynamic noise in the training is considered white noise on the input corresponds on average to a weight decay and can thus improve generalization whereas white noise on the weights or the output degrades generalization generalization is particularly sensitive to noise on the weights for alpha 1 where it makes the error constantly increase with time but this effect is also shown to be damped by a weight decay weight noise and output noise acts similarly above the transition at alpha 1

a new approach to fuzzy modeling of nonlinear dynamic systems with noise relevance vector learning mechanism this paper presents a new fuzzy inference system for modeling of nonlinear dynamic systems based on input and output data with measurement noise the proposed fuzzy system has a number of fuzzy rules and parameter values of membership functions which are automatically generated using the extended relevance vector machine rvm the rvm has a probabilistic bayesian learning framework and has good generalization capability the rvm consists of the sum of product of weight and kernel function which projects input space into high dimensional feature space the structure of proposed fuzzy system is same as that of the takagi sugeno fuzzy model however in the proposed method the number of fuzzy rules can be reduced under the process of optimizing a marginal likelihood by adjusting parameter values of kernel functions using the gradient ascent method after a fuzzy system is determined coefficients in consequent part are found by the least square method examples illustrate effectiveness of the proposed new fuzzy inference system

effects of manipulating the signal to noise envelope power ratio on speech intelligibility jorgensen and dau 2011 j acoust soc am 130 1475 1487 suggested a metric for speech intelligibility prediction based on the signal to noise envelope power ratio snrenv calculated at the output of a modulation frequency selective process in the framework of the speech based envelope power spectrum model sepsm the snrenv was demonstrated to account for speech intelligibility data in various conditions with linearly and nonlinearly processed noisy speech as well as for conditions with stationary and fluctuating interferers here the relation between the snrenv and speech intelligibility was investigated further by systematically varying the modulation power of either the speech or the noise before mixing the two components while keeping the overall power ratio of the two components constant a good correspondence between the data and the corresponding sepsm predictions was obtained when the noise was manipulated and mixed with the unprocessed speech consistent with the hypothesis that sn

leading impulse response identification via the elastic net criterion this paper deals with the problem of finding a low complexity estimate of the impulse response of a linear time invariant discrete time dynamic system from noise corrupted inputoutput data to this purpose we introduce an identification criterion formed by the average over the input perturbations of a standard prediction error cost plus an 1 regularization term which promotes sparse solutions while it is well known that such criteria do provide solutions with many zeros a critical issue in our identification context is where these zeros are located since sensible low order models should be zero in the tail of the impulse response the flavor of the key results in this paper is that under quite standard assumptions such as i i d input and noise sequences and system stability the estimate of the impulse response resulting from the proposed criterion is indeed identically zero from a certain time index nl named the leading order onwards with arbitrarily high probability for a sufficiently large data cardinality n numerical experiments are reported that support the theoretical results and comparisons are made with some other state of the art methodologies

temporal envelope and fine structure cues for speech intelligibility this paper describes a number of listening experiments to investigate the relative contribution of temporal envelope modulations and fine structure to speech intelligibility the amplitude envelopes of 24 1 4 oct bands covering 100 6400 hz were processed in several ways e g fast compression in order to assess the importance of the modulation peaks and troughs results for 60 normal hearing subjects show that reduction of modulations by the addition of noise is more detrimental to sentence intelligibility than the same degree of reduction achieved by direct manipulation of the envelope in some cases the benefit in speech reception threshold srt is almost 7 db two crossover levels can be defined in dividing the temporal envelope into two equally important parts the first crossover level divides the envelope into two perceptually equal parts removing modulations either x db below or above that level yields the same intelligibility score the second crossover level divides the envelope into two acoustically equal peak and trough parts the perceptual level is 9 12 db higher than the acoustic level indicating that envelope peaks are perceptually more important than troughs further results showed that 24 intact temporal speech envelopes with noise fine structure retain perfect intelligibility in general for the present type of signal manipulations no one to one relation between the modulation transfer function and the intelligibility scores could be established

on langevin updating in multilayer perceptrons the langevin updating rule in which noise is added to the weights during learning is presented and shown to improve learning on problems with initially ill conditioned hessians this is particularly important for multilayer perceptrons with many hidden layers that often have ill conditioned hessians in addition manhattan updating is shown to have a similar effect

the concept of signal to noise ratio in the modulation domain and speech intelligibility a new concept is proposed that relates to intelligibility of speech in noise the concept combines traditional estimations of signal to noise ratios s n with elements from the modulation transfer function model which results in the definition of the signal to noise ratio in the modulation domain the sn mod it is argued that this sn mod quantifying the strength of speech modulations relative to a floor of spurious modulations arising from the speech noise interaction is the key factor in relation to speech intelligibility it is shown that by using a specific test signal the strength of these spurious modulations can be measured allowing an estimation of the sn mod for various conditions of additive noise noise suppression and amplitude compression by relating these results to intelligibility data for these same conditions the relevance of the sn mod as the key factor underlying speech intelligibility is clearly illustrated for instance it is shown that the commonly observed limited effect of noise suppression on speech intelligibility is correctly predicted by the sn mod whereas traditional measures such as the speech transmission index considering only the changes in the speech modulations fall short in this respect it is argued that sn mod may provide a relevant tool in the design of successful noise suppression systems

effect of enhancement of spectral changes on speech intelligibility and clarity preferences for the hearing impaired most information in speech is carried in spectral changes over time rather than in static spectral shape per se a form of signal processing aimed at enhancing spectral changes over time was developed and evaluated using hearing impaired listeners the signal processing was based on the overlap add method and the degree and type of enhancement could be manipulated via four parameters two experiments were conducted to assess speech intelligibility and clarity preferences three sets of parameter values one corresponding to a control condition two types of masker steady speech spectrum noise and two talker speech and two signal to masker ratios smrs were used for each masker type generally the effects of the processing were small although intelligibility was improved by about 8 percentage points relative to the control condition for one set of parameter values using the steady noise masker at 6 db smr the processed signals were not preferred over those for the control condition except for the steady noise masker at 6 db smr further work is needed to determine whether tailoring the processing to the characteristics of the individual hearing impaired listener is beneficial

informational masking of monaural target speech by a single contralateral formant recent research suggests that the ability of an extraneous formant to impair intelligibility depends on the variation of its frequency contour this idea was explored using a method that ensures interference cannot occur through energetic masking three formant f1 f2 f3 analogues of natural sentences were synthesized using a monotonous periodic source target formants were presented monaurally with the target ear assigned randomly on each trial a competitor for f2 f2c was presented contralaterally listeners must reject f2c to optimize recognition in experiment 1 f2cs with various frequency and amplitude contours were used f2cs with time varying frequency contours were effective competitors constant frequency f2cs had far less impact to a lesser extent amplitude contour also influenced competitor impact this effect was additive in experiment 2 f2cs were created by inverting the f2 frequency contour about its geometric mean and varying its depth of variation over a range from constant to twice the original 0 200 the impact on intelligibility was least for constant f2cs and increased up to 100 depth but little thereafter the effect of an extraneous formant depends primarily on its frequency contour interference increases as the depth of variation is increased until the range exceeds that typical for f2 in natural speech

the role of short time intensity and envelope power for speech intelligibility and psychoacoustic masking the generalized power spectrum model gpsm biberger and ewert 2016 j acoust soc am 140 1023 1038 combining the classical concept of the power spectrum model psm and the envelope power spectrum model epsm was demonstrated to account for several psychoacoustic and speech intelligibility si experiments the psm path of the model uses long time power signal to noise ratios snrs while the epsm path uses short time envelope power snrs a systematic comparison of existing si models for several spectro temporal manipulations of speech maskers and gender combinations of target and masker speakers schubotz et al 2016 j acoust soc am 140 524 540 showed the importance of short time power features conversely jorgensen et al 2013 j acoust soc am 134 436 446 demonstrated a higher predictive power of short time envelope power snrs than power snrs using reverberation and spectral subtraction here the gpsm was extended to utilize short time power snrs and was shown to account

moderate cochlear hearing loss leads to a reduced ability to use temporal fine structure information the ability of normally hearing and hearing impaired subjects to use temporal fine structure information in complex tones was measured subjects were required to discriminate a harmonic complex tone from a tone in which all components were shifted upwards by the same amount in hz in a three alternative forced choice task the tones either contained five equal amplitude components non shaped stimuli or contained many components but were passed through a fixed bandpass filter to reduce excitation pattern changes shaped stimuli components were centered at nominal harmonic numbers n 7 11 and 18 for the shaped stimuli hearing impaired subjects performed much more poorly than normally hearing subjects with most of the former scoring no better than chance when n 11 or 18 suggesting that they could not access the temporal fine structure information performance for the hearing impaired subjects was significantly improved for the non shaped stimuli presumably because they could benefit from spectral cues it is proposed that normal hearing subjects can use temporal fine structure information provided the spacing between fine structure peaks is not too small relative to the envelope period but subjects with moderate cochlear hearing loss make little use of temporal fine structure information for unresolved components

the dropout learning algorithm dropout is a recently introduced algorithm for training neural networks by randomly dropping units during training to prevent their co adaptation a mathematical analysis of some of the static and dynamic properties of dropout is provided using bernoulli gating variables general enough to accommodate dropout on units or connections and with variable rates the framework allows a complete analysis of the ensemble averaging properties of dropout in linear networks which is useful to understand the non linear case the ensemble averaging properties of dropout in non linear logistic networks result from three fundamental equations 1 the approximation of the expectations of logistic functions by normalized geometric means for which bounds and estimates are derived 2 the algebraic equality between normalized geometric means of logistic functions with the logistic of the means which mathematically characterizes logistic functions and 3 the linearity of the means with respect to sums as well as products of independent variables the results are also extended to other classes of transfer functions including rectified linear functions approximation errors tend to cancel each other and do not accumulate dropout can also be connected to stochastic neurons and used to predict firing rates and to backpropagation by viewing the backward propagation as ensemble averaging in a dropout linear network moreover the convergence properties of dropout can be understood in terms of stochastic gradient descent finally for the regularization properties of dropout the expectation of the dropout gradient is the gradient of the corresponding approximation ensemble regularized by an adaptive weight decay term with a propensity for self consistent variance minimization and sparse representations

coupled kernel embedding for low resolution face image recognition practical video scene and face recognition systems are sometimes confronted with low resolution lr images the faces may be very small even if the video is clear thus it is difficult to directly measure the similarity between the faces and the high resolution hr training samples face recognition based on traditional super resolution sr methods usually have limited performance because the target of sr may not be consistent with that of classification and time consuming sr algorithms are not suitable for real time applications in this paper a new feature extraction method called coupled kernel embedding cke is proposed for lr face recognition without any sr preprocessing in this method the final kernel matrix is constructed by concatenating two individual kernel matrices in the diagonal direction and the semi positively definite properties are preserved for optimization cke addresses the problem of comparing multimodal data that are difficult for conventional methods in practice due to the lack of an efficient similarity measure particularly different kernel types e g linear gaussian polynomial can be integrated into a uniform optimization objective which cannot be achieved by simple linear methods cke solves this problem by minimizing the dissimilarities captured by their kernel gram matrices in the lr and hr spaces in the implementation the nonlinear objective function is minimized by a generalized eigenvalue decomposition experiments on benchmark and real databases show that our cke method indeed improves the recognition performance

use of high rate envelope speech cues and their perceptually relevant dynamic range for the hearing impaired the ability of hearing impaired hi listeners to use high rate envelope information in a competing talker situation was assessed in experiment 1 signals were tone vocoded and the cutoff frequency fc of the envelope extraction filter was either 50 hz e filter or 200 hz p filter the channels for which the p or e filter was used were varied intelligibility was higher with the p filter regardless of whether it was used for low or high center frequencies performance was best when the p filter was used for all channels experiment 2 explored the dynamic range over which hi listeners made use of high rate cues in each channel of a vocoder the envelope extracted using fc 16 hz was replaced by the envelope extracted using fc 300 hz either at the peaks or valleys with a parametrically varied switching threshold for a target to background ratio of 5 db changes in speech intelligibility occurred mainly when the switching threshold was between 8 and 8 db relative to the channel root mean square level this range is similar in width to but about 3 db higher in absolute level than that found for normal hearing listeners despite the reduced dynamic range of the hi listeners

regularized principal manifolds many settings of unsupervised learning can be viewed as quantization problems the minimization of the expected quantization error subject to some restrictions this allows the use of tools such as regularization from the theory of supervised risk minimization for unsupervised learning this setting turns out to be closely related to principal curves the generative topographic map and robust coding we explore this connection in two ways 1 we propose an algorithm for finding principal manifolds that can be regularized in a variety of ways and 2 we derive uniform convergence bounds and hence bounds on the learning rates of the algorithm in particular we give bounds on the covering numbers which allows us to obtain nearly optimal learning rates for certain types of regularization operators experimental results demonstrate the feasibility of the approach