Swept-tone transient-evoked otoacoustic emissions

Transient-evoked otoacoustic emissions (TEOAE) are responses generated within the inner ear in response to acoustic stimuli and are indicative of normal cochlear function. They are commonly acquired by averaging post-stimulus acoustic responses recorded near the eardrum in response to brief stimuli such as clicks or tone pips. In this study a new long duration stimulus consisting of a frequency swept tone is introduced for the acquisition of TEOAEs. Like stimulus frequency generated OAEs, swept-tone responses contain embedded OAEs. With swept-tone analysis, OAEs can be recovered by convolving it with a time reversed swept-tone signal resulting in time-compression. In addition, higher order nonlinear OAE responses were removed from the linear TEOAE. The results show comparable phase and time-frequency properties between the click and swept-tone evoked OAEs. Swept-tone acquisition of TEOAEs has beneficial noise properties, improving the signal to noise ratio by 6 dB compared to click evoked responses thus offering testing time savings. Additionally, swept-tone analysis removed synchronized spontaneous OAE activity from the recordings of subjects exhibiting such responses in conventional click TEOAEs. Since swept-tone stimulus consists of a single frequency component at any instantaneous moment, its analysis also provides for direct comparison with stimulus-frequency OAEs and click evoked OAEs.     

Near equivalence of human click-evoked and stimulus-frequency otoacoustic emissions

Otoacoustic emissions (OAEs) evoked by broadband clicks and by single tones are widely regarded as originating via different mechanisms within the cochlea. Whereas the properties of stimulus-frequency OAEs (SFOAEs) evoked by tones are consistent with an origin via linear mechanisms involving coherent wave scattering by preexisting perturbations in the mechanics, OAEs evoked by broadband clicks (CEOAEs) have been suggested to originate via nonlinear interactions among the different frequency components of the stimulus (e.g., intermodulation distortion). The experiments reported here test for bandwidth-dependent differences in mechanisms of OAE generation. Click-evoked and stimulus-frequency OAE input/output transfer functions were obtained and compared as a function of stimulus frequency and intensity. At low and moderate intensities human CEOAE and SFOAE transfer functions are nearly identical. When stimulus intensity is measured in "bandwidth-compensated" sound-pressure level (cSPL), CEOAE and SFOAE transfer functions have equivalent growth functions at fixed frequency and equivalent spectral characteristics at fixed intensity. This equivalence suggests that CEOAEs and SFOAEs are generated by the same mechanism. Although CEOAEs and SFOAEs are known by different names because of the different stimuli used to evoke them, the two OAE "types" are evidently best understood as members of the same emission family.     

Perceptuomotor bias in the imitation of steady-state vowels

Previous studies suggest that speakers are systematically inaccurate, or biased, when imitating self-produced vowels. The direction of these biases in formant space and their variation may offer clues about the organization of the vowel perceptual space. To examine these patterns, three male speakers were asked to imitate 45 self-produced vowels that were systematically distributed in F1/F2 space. All three speakers showed imitation bias, and the bias magnitudes were significantly larger than those predicted by a model of articulatory noise. Each speaker showed a different pattern of bias directions, but the pattern was unrelated to the locations of prototypical vowels produced by that speaker. However, there were substantial quantitative regularities: (1) The distribution of imitation variability and bias magnitudes were similar for all speakers, (2) the imitation variability was independent of the bias magnitudes, and (3) the imitation variability (a production measure) was commensurate with the formant discrimination limen (a perceptual measure). These results indicate that there is additive Gaussian noise in the imitation process that independently affects each formant and that there are speaker-dependent and potentially nonlinguistic biases in vowel perception and production.     

Spontaneous otoacoustic emissions and relaxation dynamics of long decay time OAEs in audiometrically normal and impaired subjects

The relationship between hearing loss, detected by measuring the audiometric threshold shift, and the presence of long-lasting otoacoustic emissions, has been studied in a population of 66 adult males, by analyzing the cochlear response in the 80 ms following the subministration of a click stimulus. Most long-lasting OAEs are also recognizable as Synchronized Spontaneous OAEs (SSOAEs). The OAE characteristic decay times were evaluated according to the model by Sisto and Moleti [J. Acoust. Soc. Am. 106, 1893 (1999)]. The starting hypothesis, confirmed by the results, is that long decay time and large equilibrium amplitude are both manifestations of the effectiveness of the active feedback mechanism. The prevalence and frequency distribution of long-lasting OAEs, and of their SSOAE subset, have been separately analyzed for normal and impaired ears. No long-lasting OAE was found within the hearing loss frequency range, but several were found in impaired ears outside the hearing loss range, both at lower and higher frequencies. This result suggests that the correlation between the presence of long-lasting OAEs and good cochlear functionality be local in the frequency domain. The monitor of the OAE decay time is proposed as a new possible method for early detecting hearing loss in populations exposed to noise.     

Comparison of absolute magnitude estimation and relative magnitude estimation for judging the subjective intensity of noise and vibration

The method of magnitude estimation is used in psychophysical studies to obtain numerical values for the intensity of perception of environmental stresses (e.g., noise and vibration). The exponent in a power function relating the subjective magnitude of a stimulus (e.g., the degree of discomfort) to the physical magnitude of the stimulus shows the rate of growth of sensations with increasing stimulus magnitude. When judging noise and vibration, there is no basis for deciding whether magnitude estimation should be performed with a reference stimulus (i.e., relative magnitude estimation, RME) or without a reference stimulus (i.e., absolute magnitude estimation, AME). Twenty subjects rated the discomfort caused by thirteen magnitudes of whole-body vertical vibration and 13 levels of noise, by both RME and AME on three occasions. There were high correlations between magnitude estimates of discomfort and the magnitudes of vibration and noise. Both RME and AME provided rates of growth of discomfort with high consistency over the three repetitions. When judging noise, RME was more consistent than AME, with less inter-subject variability in the exponent, n_s. When judging vibration, RME was also more consistent than AME, but with greater inter-subject variability in the exponent, n_v. When judging vibration, AME may be beneficial because sensations caused by the RME reference stimulus may differ (e.g., occur in a different part of the body) from the sensations caused by the stimuli being judged.     

Normalized split-spectrum: a detection approach

We consider in this paper the problem of automatic detection of ultrasonic echo pulses in a grain noise background. We start by assuming a reference model for grain noise: multivariate correlated Gaussian model having, in general, different variances under every hypothesis. We show that, even for this simple model, there is not practical optimum solution, except if the variances are equal under every hypothesis and the echo pulse satisfies a spectral constraint. Then we consider split-spectrum (SS) suboptimum solutions. Firstly, SS algorithms are formulated following an algebraic approach which is appropriate in an automatic detection framework. Popular minimization and polarity thresholding algorithms are considered under this framework. Then a new detector called normalized SS (NSS) is proposed. The underlying idea is to actually exploit the tuning frequency sensitivity (i.e., variability of the output magnitudes from one SS channel to another), making this measurement independent of the absolute magnitudes. Different experiments with simulated and real data show evidences of the interest of the new method in an automatic detection framework. Derivations of the formulas for fitting the probability of false alarm in every detector are included in the paper.     

Importance sampling for noise-induced amplitude and timing jitter in soliton transmission systems

We discuss the application of importance-sampling techniques to the numerical simulation of transmission impairments induced by amplified spontaneous emission noise in soliton-based optical transmission systems. The method allows one to concentrate numerical simulations on the noise realizations that are most likely to result in transmission errors, thus leading to increases in speed of several orders of magnitude over standard Monte Carlo methods. We demonstrate the technique by calculating the probability distribution function of amplitude and timing fluctuations.     

Time-domain observation of otoacoustic emissions during constant tone stimulation.

Observation of the otoacoustic emissions (OAEs) evoked during a continuous single stimulus tone have been made on humans using a nonlinear residual time domain technique. The technique, described in this paper, involved the digital summation of responses to contiguous stimulation intervals, some of which included short bursts of a suppressor, or probe, tone. Stimulus intervals are constructed so that both the stimulus and probe tones summed to zero cyclically, leaving a residual response. This residual is attributable to the nonlinearity of the whole acoustic response, as measured in the ear canal, to the stimulus and probe tone complex. A theoretical treatment of this paradigm is presented examining the relation of this residual to the OAE evoked by the stimulus tone. It is shown experimentally that the residual, found at the stimulus tone frequency, has a latency and saturating input-output growth functions indicative of an OAE. The detailed OAE amplitude-versus-frequency variations, and the general latencies of the OAEs in two human ears were measured using both the constant tone evoked residual method described and the click evoked delayed emission method. The results from both methods are in agreement. The frequency-dependent properties of the suppression of the OAE were investigated using various stimuli to probe frequency ratios. The continuous tone time domain residual method has advantages for the observation of stimulus frequency OAEs and for relating these to any distortion product simultaneously generated.     

Obtaining reliable phase-gradient delays from otoacoustic emission data

Reflection-source otoacoustic emission phase-gradient delays are widely used to obtain noninvasive estimates of cochlear function and properties, such as the sharpness of mechanical tuning and its variation along the length of the cochlear partition. Although different data-processing strategies are known to yield different delay estimates and trends, their relative reliability has not been established. This paper uses in silico experiments to evaluate six methods for extracting delay trends from reflection-source otoacoustic emissions (OAEs). The six methods include both previously published procedures (e.g., phase smoothing, energy-weighting, data exclusion based on signal-to-noise ratio) and novel strategies (e.g., peak-picking, all-pass factorization). Although some of the methods perform well (e.g., peak-picking), others introduce substantial bias (e.g., phase smoothing) and are not recommended. In addition, since standing waves caused by multiple internal reflection can complicate the interpretation and compromise the application of OAE delays, this paper develops and evaluates two promising signal-processing strategies, the first based on time-frequency filtering using the continuous wavelet transform and the second on cepstral analysis, for separating the direct emission from its subsequent reflections. Altogether, the results help to resolve previous disagreements about the frequency dependence of human OAE delays and the sharpness of cochlear tuning while providing useful analysis methods for future studies.     

General second-order covariance of Gaussian maximum likelihood estimates applied to passive source localization in fluctuating waveguides

A method is provided for determining necessary conditions on sample size or signal to noise ratio (SNR) to obtain accurate parameter estimates from remote sensing measurements in fluctuating environments. These conditions are derived by expanding the bias and covariance of maximum likelihood estimates (MLEs) in inverse orders of sample size or SNR, where the first-order covariance term is the Cramer-Rao lower bound (CRLB). Necessary sample sizes or SNRs are determined by requiring that (i) the first-order bias and the second-order covariance are much smaller than the true parameter value and the CRLB, respectively, and (ii) the CRLB falls within desired error thresholds. An analytical expression is provided for the second-order covariance of MLEs obtained from general complex Gaussian data vectors, which can be used in many practical problems since (i) data distributions can often be assumed to be Gaussian by virtue of the central limit theorem, and (ii) it allows for both the mean and variance of the measurement to be functions of the estimation parameters. Here, conditions are derived to obtain accurate source localization estimates in a fluctuating ocean waveguide containing random internal waves, and the consequences of the loss of coherence on their accuracy are quantified.     

噪声方差未知时的分布式量化估计融合方法

为了解决观观测噪声和信道噪声概率分布不完全已知时的多传感器分布式量化估计融合问题,提出了一种期望极大化算法(EM算法)的分布式量化估计融合方法。该方法将未知的噪声参数以及局部量化器量化概率建模为EM算法中二元高斯混合模型参数,利用极大似然估计方法的估计不变性得到目标参数的估计融合结果。仿真实验结果表明:该方法在局部传感器观测样本数目大于6000和信噪比大于6 dB时与已有理想信道条件下的估计方法性能相当。本文方法对水下分布式协同探测问题提供了一种简化的估计融合实现途径。      

Wavelet domain de-noising of time-courses in MR image sequences

Magnetic resonance images acquired with high temporal resolution often exhibit large noise artifacts, which arise from physiological sources as well as from the acquisition hardware. These artifacts can be detrimental to the quality and interpretation of the time-course data in functional MRI studies. A class of wavelet-domain de-noising algorithms estimates the underlying, noise-free signal by thresholding (or 'shrinking') the wavelet coefficients, assuming the underlying temporal noise of each pixel is uncorrelated and Gaussian. A Wiener-type shrinkage algorithm is developed in this paper, for de-noising either complex- or magnitude-valued image data sequences. Using the de-correlation properties of the wavelet transform, as elucidated by Johnstone and Silverman, the assumption of i.i.d. Gaussian noise can be abandoned, opening up the possibility of removing colored noise. Both wavelet- and wavelet-packet based algorithms are developed, and the Wiener method is compared to the traditional Hard and Soft wavelet thresholding methods of Donoho and Johnstone. The methods are applied to two types of data sets. In the first, an artificial set of complex-valued images was constructed, in which each pixel has a simulated bimodal time-course. Gaussian noise was added to each of the real and imaginary channels, and the noise removed from the complex image sequence as well as the magnitude image sequence (where the noise is Rician). The bias and variance between the original and restored paradigms was estimated for each method. It was found that the Wiener method gives better balance in bias and variance than either Hard or Soft methods. Furthermore, de-noising magnitude data provides comparable accuracy of the restored images to that obtained from de-noising complex data. In the second data set, an actual in vivo complex image sequence containing unknown physiological and instrumental noise was used. The same bimodal paradigm as in the first data set was added to pixels in a small localized region of interest. For the paradigm investigated here, the smooth Daubechies wavelets provide better de-noising characteristics than the discontinuous Haar wavelets. Also, it was found that wavelet packet de-noising offers no significant improvement over the computationally more efficient wavelet de-noising methods. For the in vivo data, it is desirable that the groups of "activated" time-courses are homogeneous. It was found that the internal homogeneity of the group of time-courses increases when de-noising is applied. This suggests using de-noising as a pre-processing tool for both exploratory and inferential data analysis methods in fMRI.     

Perceptually motivated wavelet packet transform for bioacoustic signal enhancement

A significant and often unavoidable problem in bioacoustic signal processing is the presence of background noise due to an adverse recording environment. This paper proposes a new bioacoustic signal enhancement technique which can be used on a wide range of species. The technique is based on a perceptually scaled wavelet packet decomposition using a species-specific Greenwood scale function. Spectral estimation techniques, similar to those used for human speech enhancement, are used for estimation of clean signal wavelet coefficients under an additive noise model. The new approach is compared to several other techniques, including basic bandpass filtering as well as classical speech enhancement methods such as spectral subtraction, Wiener filtering, and Ephraim-Malah filtering. Vocalizations recorded from several species are used for evaluation, including the ortolan bunting (Emberiza hortulana), rhesus monkey (Macaca mulatta), and humpback whale (Megaptera novaeanglia), with both additive white Gaussian noise and environment recording noise added across a range of signal-to-noise ratios (SNRs). Results, measured by both SNR and segmental SNR of the enhanced wave forms, indicate that the proposed method outperforms other approaches for a wide range of noise conditions.     

Using the short-time correlation coefficient to compare transient- and derived, noise-evoked otoacoustic emission temporal waveforms

Transient-evoked otoacoustic emissions (TEOAEs) and derived, noise-evoked otoacoustic emissions (derived-NEOAEs) were measured in seven normally hearing subjects. The evoked OAEs were all recorded at three excitation levels chosen to ensure that the OAE level curve compressive region was reached. The short-time correlation coefficient (STCC) was used to compare the OAE waveforms at different excitation levels, and thus estimate the time over which the response exceeds the noise level. The short-time correlation for TEOAEs is significant for longer than it is for NEOAEs, particularly in some individuals, and the STCC allows this to be quantified. This suggests that derived NEOAEs do not display the highly synchronized dominant frequencies often seen in TEOAEs. This has been confirmed by examining the derived frequency responses for the two types of excitation. Conventional TEOAEs thus appear to measure a combination of two conceptually different processes, while NEOAEs measure just one.     

Algorithm for Optimal Estimation of Appearance Times of Pulsed Signals in Discrete Time

Using the methods of optimal nonlinear Markov filtering, we obtain an algorithm for optimal mean-square estimation of appearance times of random pulsed variations in signal parameters against the background of white Gaussian noise in discrete time. Linear difference equations are used to describe signals, noise, and the observed processes. Equations of the algorithm permitting real-time calculations of the a posteriori variances and optimal estimations of pulse-appearance times are obtained in the approximation of Gaussian conditional probability densities. We present simulation results for algorithm operation in the particular problem of estimating the appearance times of two pulsed signals having the known shapes and observed against noise background.     

Ear asymmetries in middle-ear, cochlear, and brainstem responses in human infants

In 2004, Sininger and Cone-Wesson examined asymmetries in the signal-to-noise ratio (SNR) of otoacoustic emissions (OAE) in infants, reporting that distortion-product (DP)OAE SNR was larger in the left ear, whereas transient-evoked (TE)OAE SNR was larger in the right. They proposed that cochlear and brainstem asymmetries facilitate development of brain-hemispheric specialization for sound processing. Similarly, in 2006 Sininger and Cone-Wesson described ear asymmetries mainly favoring the right ear in infant auditory brainstem responses (ABRs). The present study analyzed 2640 infant responses to further explore these effects. Ear differences in OAE SNR, signal, and noise were evaluated separately and across frequencies (1.5, 2, 3, and 4 kHz), and ABR asymmetries were compared with cochlear asymmetries. Analyses of ear-canal reflectance and admittance showed that asymmetries in middle-ear functioning did not explain cochlear and brainstem asymmetries. Current results are consistent with earlier studies showing right-ear dominance for TEOAE and ABR. Noise levels were higher in the right ear for OAEs and ABRs, causing ear asymmetries in SNR to differ from those in signal level. No left-ear dominance for DPOAE signal was observed. These results do not support a theory that ear asymmetries in cochlear processing mimic hemispheric brain specialization for auditory processing.     

A wavelet-based method for improving signal-to-noise ratio and contrast in MR images

Magnetic resonance (MR) images acquired with fast measurement often display poor signal-to-noise ratio (SNR) and contrast. With the advent of high temporal resolution imaging, there is a growing need to remove these noise artifacts. The noise in magnitude MR images is signal-dependent (Rician), whereas most de-noising algorithms assume additive Gaussian (white) noise. However, the Rician distribution only looks Gaussian at high SNR. Some recent work by Nowak employs a wavelet-based method for de-noising the square magnitude images, and explicitly takes into account the Rician nature of the noise distribution. In this article, we apply a wavelet de-noising algorithm directly to the complex image obtained as the Fourier transform of the raw k-space two-channel (real and imaginary) data. By retaining the complex image, we are able to de-noise not only magnitude images but also phase images. A multiscale (complex) wavelet-domain Wiener-type filter is derived. The algorithm preserves edges better when the Haar wavelet rather than smoother wavelets, such as those of Daubechies, are used. The algorithm was tested on a simulated image to which various levels of noise were added, on several EPI image sequences, each of different SNR, and on a pair of low SNR MR micro-images acquired using gradient echo and spin echo sequences. For the simulated data, the original image could be well recovered even for high values of noise (SNR approximately 0 dB), suggesting that the present algorithm may provide better recovery of the contrast than Nowak's method. The mean-square error, bias, and variance are computed for the simulated images. Over a range of amounts of added noise, the present method is shown to give smaller bias than when using a soft threshold, and smaller variance than a hard threshold; in general, it provides a better bias-variance balance than either hard or soft threshold methods. For the EPI (MR) images, contrast improvements of up to 8% (for SNR = 33 dB) were found. In general, the improvement in contrast was greater the lower the original SNR, for example, up to 50% contrast improvement for SNR of about 20 dB in micro-imaging. Applications of the algorithm to the segmentation of medical images, to micro-imaging and angiography (where the correct preservation of phase is important for flow encoding to be possible), as well as to de-noising time series of functional MR images, are discussed.     

Additivity of nonsimultaneous masking for short Gaussian-shaped sinusoids

The additivity of nonsimultaneous masking was studied using Gaussian-shaped tone pulses (referred to as Gaussians) as masker and target stimuli. Combinations of up to four temporally separated Gaussian maskers with an equivalent rectangular bandwidth of 600 Hz and an equivalent rectangular duration of 1.7 ms were tested. Each masker was level-adjusted to produce approximately 8 dB of masking. Excess masking (exceeding linear additivity) was generally stronger than reported in the literature for longer maskers and comparable target levels. A model incorporating a compressive input/output function, followed by a linear summation stage, underestimated excess masking when using an input/output function derived from literature data for longer maskers and comparable target levels. The data could be predicted with a more compressive input/output function. Stronger compression may be explained by assuming that the Gaussian stimuli were too short to evoke the medial olivocochlear reflex (MOCR), whereas for longer maskers tested previously the MOCR caused reduced compression. Overall, the interpretation of the data suggests strong basilar membrane compression for very short stimuli.     

Otoacoustic emission sensitivity to low levels of noise-induced hearing loss

With the aim of investigating the capability of otoacoustic emission (OAE) in the detection of low levels of noise-induced hearing loss, audiometric and otoacoustic data of young workers (age: 18-35) exposed to different levels of industrial noise have been recorded. These subjects are participating in a long-term longitudinal study, in which audiometric, exposure (both professional and extra-professional), and OAE data (transient evoked and distortion product) will be collected for a period of several years. All measurements have been performed, during routine occupational health surveillance, with a standard clinical apparatus and acquisition procedure, which can be easily used in the occupational safety practice. The first study was focused on the correlation between transient evoked OAE signal-to-noise ratio and distortion product (DPOAE) OAE level and the audiometric threshold, investigating the causes of the rather large intersubject variability of the OAE levels. The data analysis has shown that, if both OAE data and audiometric data are averaged over a sufficiently large bandwidth, the correlation between DPOAE levels and audiometric hearing threshold is sufficient to design OAE-based diagnostic tests with good sensitivity and specificity also in a very mild hearing loss range, between 10 and 20 dB.     

Estimation of damping from response spectra

In this paper procedures for estimating damping ratio from response spectra are examined. The study is restricted to an evaluation of bias and random errors introduced by signal processing requirements. A second order system is used in the study, and a Gaussian white noise input is assumed. It is shown that, due to bias errors in estimating the response spectra, calculations of damping ratio by the peak response and half-power bandwidth methods give overestimates. The bias errors of the damping ratio estimates are a function of the true damping of the system and the ratio of analysis bandwidth to resonant frequency. The bias error for the half-power bandwidth method is three times that for the peak response method. It is also shown for large ranges of damping ratio and bandwidth ratio that zero bias response occurs at a point where the response is approximately 80% of the peak response. Numerical results obtained by simulation studies are used to verify the expressions for normalized bias error. Expressions for random error associated with damping ratio estimates are also developed. Random error can be minimized by maintaining a high coherence between the system input and response.