Comparison of time-frequency methods for analyzing stimulus frequency otoacoustic emissions

Optimal Scale-Invariant Wavelet Representation and Filtering of Human Otoacoustic Emissions

Otoacoustic emissions (OAEs) are generated in the cochlea and recorded in the ear canal either as a time domain waveform or as a collection of complex responses to tones in the frequency domain (Probst et al. J Account Soc Am 89:2027-2067, 1991). They are typically represented either in their original acquisition domain or in its Fourier-conjugated domain. Round-trip excursions to the conjugated domain are often used to perform filtering operations in the computationally simplest way, exploiting the convolution theorem. OAE signals consist of the superposition of backward waves generated in different cochlear regions by different generation mechanisms, over a wide frequency range. The cochlear scaling symmetry (cochlear physics is the same at all frequency scales), which approximately holds in the human cochlea, leaves its fingerprints in the mathematical properties of OAE signals. According to a generally accepted taxonomy (Sher and Guinan Jr, J Acoust Soc Am 105:782-798, 1999), OAEs are generated either by wave-fixed sources, moving with frequency according with the cochlear scaling (as in nonlinear distortion) or by place-fixed sources (as in coherent reflection by roughness). If scaling symmetry holds, the two generation mechanisms yield OAEs with different phase gradient delay: almost null for wave-fixed sources, and long (and scaling as 1/f) for place-fixed sources. Thus, the most effective representation of OAE signals is often that respecting the cochlear scale-invariance, such as the time-frequency domain representation provided by the wavelet transform. In the time-frequency domain, the elaborate spectra or waveforms yielded by the superposition of OAE components from different generation mechanisms assume a much clearer 2-D pattern, with each component localized in a specific and predictable region. The wavelet representation of OAE signals is optimal both for visualization purposes and for designing filters that effectively separate different OAE components, improving both the specificity and the sensitivity of OAE-based applications. Indeed, different OAE components have different physiological meanings, and filtering dramatically improves the signal-to-noise ratio.

Denoising click-evoked otoacoustic emission signals by optimal shrinkage

Click-evoked otoacoustic emissions (CEOAEs) are clinically used as an objective way to infer whether cochlear functions are normal. However, because the sound pressure level of CEOAEs is typically much lower than the background noise, it usually takes hundreds, if not thousands, of repetitions to estimate the signal with sufficient accuracy. In this paper, we propose to improve the signal-to-noise ratio (SNR) of CEOAE signals within limited measurement time by optimal shrinkage (OS) in two different settings: covariance-based optimal shrinkage (cOS) and singular value decomposition-based optimal shrinkage (sOS). By simulation, the cOS consistently enhanced the SNR by 1-2 dB from a baseline method that is based on calculating the median. In real data, however, the cOS cannot enhance the SNR over 1 dB. The sOS achieved a SNR enhancement of 2-3 dB in simulation and demonstrated capability to enhance the SNR in real recordings. In addition, the level of enhancement increases as the baseline SNR decreases. An appealing property of OS is that it produces an estimate of all single trials. This property makes it possible to investigate CEOAE dynamics across a longer period of time when the cochlear conditions are not strictly stationary.

Maximising the ability of stimulus-frequency otoacoustic emissions to predict hearing status and thresholds using machine-learning models

OBJECTIVE

This study aimed to maximise the ability of stimulus-frequency otoacoustic emissions (SFOAEs) to predict hearing status and thresholds based on machine-learning models.

DESIGN

SFOAE data and audiometric thresholds were collected at octave frequencies from 0.5 to 8 kHz. Support vector machine, k-nearest neighbour, back propagation neural network, decision tree, and random forest algorithms were used to build classification models for status identification and to develop regression models for threshold prediction.

STUDY SAMPLE

About 230 ears with normal hearing and 737 ears with sensorineural hearing loss.

RESULTS

All classification models yielded areas under the receiver operating characteristic curve of 0.926-0.994 at 0.5-8 kHz, superior to the previous SFOAE study. The regression models produced lower standard errors (8.1-12.2 dB, mean absolute errors: 5.53-8.97 dB) as compared to those for distortion-product and transient-evoked otoacoustic emissions previously reported (8.6-19.2 dB).

CONCLUSIONS

SFOAEs using machine-learning approaches offer promising tools for the prediction of hearing capabilities, at least at 0.5-4 kHz. Future research may focus on further improvements in accuracy and reductions in test time to improve clinical utility.

Experimental study of airfoil-rotor interaction noise by wavelet beamforming

A wavelet-based beamforming method is performed in this investigation to analyze moving acoustic sources in the time-frequency domain, which is of scientific significance and practical importance. The particular problem considered here is the interaction noise from an airfoil and the downstream rotor in the presence of a moving flow, which could find realistic applications in next-generation underwater and aviation systems. A realistic experimental setup is prepared with a rotating blade and the airfoil in an anechoic wind tunnel. The results show that the wavelet-based beamforming method is very suitable for unsteady sound source imaging, which would be able to strengthen the time-frequency analysis capability of acoustic imaging tests and, consequently, possibly leads to deepened physical insights of various transient and moving systems in underwater and aerospace systems.

Effects of Forward- and Emitted-Pressure Calibrations on the Variability of Otoacoustic Emission Measurements Across Repeated Probe Fits

OBJECTIVE

The stimuli used to evoke otoacoustic emissions (OAEs) are typically calibrated based on the total SPL measured at the probe microphone. However, due to the acoustics of the ear-canal space (i.e., standing-wave interference), this method can underestimate the stimulus pressure reaching the tympanic membrane at certain frequencies. To mitigate this effect, stimulus calibrations based on forward pressure level (FPL) can be applied. Furthermore, the influence of ear-canal acoustics on measured OAE levels can be compensated by expressing them in emitted pressure level (EPL). To date, studies have used artificial shallow versus deep probe fits to assess the effects of calibration method on changes in probe insertion. In an attempt to better simulate a clinical setting, the combined effects of FPL calibration of stimulus level and EPL compensation of OAE level on response variability during routine (noncontrived) probe fittings were examined.

DESIGN

The distortion component of the distortion-product OAE (DPOAE) and the stimulus-frequency OAE (SFOAE) were recorded at low and moderate stimulus levels in 20 normal-hearing young-adult subjects across a five-octave range. In each subject, three different calibration approaches were compared: (1) the conventional SPL-based stimulus calibration with OAE levels expressed in SPL; (2) FPL stimulus calibration with OAEs expressed in SPL; and (3) FPL stimulus calibration with OAEs expressed in EPL. Test and retest measurements were obtained during the same session and, in a subset of subjects, several months after the initial test. The effects of these different procedures on the inter- and intra-subject variability of OAE levels were assessed across frequency and level.

RESULTS

There were no significant differences in the inter-subject variability of OAE levels across the three calibration approaches. However, there was a significant effect on OAE intra-subject variability. The FPL/EPL approach resulted in the overall lowest test-rest differences in DPOAE level for frequencies above 4 kHz, where standing-wave interference is strongest. The benefit was modest, ranging on average from 0.5 to 2 dB and was strongest at the lower stimulus level. SFOAE level variability did not show significant differences among the three procedures, perhaps due to insufficient signal-to-noise ratio and nonoptimized stimulus levels. Correlations were found between the short-term replicability of DPOAEs and the benefit derived from the FPL/EPL procedure: the more variable the DPOAE, the stronger the benefit conferred by the advanced calibration methods.

CONCLUSIONS

Stimulus and response calibration procedures designed to mitigate the effects of standing-wave interference on both the stimulus and the OAE enhance the repeatability of OAE measurements and reduce their dependence on probe position, even when probe shifts are small. Modest but significant improvements in short-term test-retest repeatability were observed in the mid- to high-frequency region when using combined FPL/EPL procedures. The authors posit that the benefit will be greater in a more heterogeneous group of subjects and when different testers participate in the fitting and refitting of subjects, which is a common practice in the audiology clinic. The impact of calibration approach on OAE inter-subject variability was not significant, possibly due to a homogeneous subject population and because factors other than probe position are at play.