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This paper first reviews well known analytical techniques for predicting the Rossiter modes of a cavity in compressible flow. We combine existing methods to improve the performance in compressible flow. Second, we introduce a method based on an effective length to depth ratio of the cavity from experimental results for predicting frequencies across Mach numbers. Finally, the fractional vortex speed used in the Rossiter equation and its derivatives is calculated from high subsonic (M 0.55) to supersonic (M 2.3) for use in future cavity mode prediction.

This paper addresses trade-offs in adaptive feedback cancellation (AFC) for hearing aids. Aggressive AFC for improved added stable gain (ASG) reduces speech quality. In this paper, the hearing-aid speech quality index (HASQI) is used to investigate AFC performance before the system becomes unstable. It is demonstrated that for a desired speech quality, multiple AFC algorithms can be evaluated for their ASG and computational efficiency. An example is presented with HASQI = 0.8, baseline AFC, and two advanced approaches. For the advanced AFCs, ASG gains of 4 and 7 dB were obtained at additional computational complexity of 8% and 11%, respectively.

No agreed-upon method currently exists for objective measurement of perceived voice quality. This paper describes validation of a psychoacoustic model designed to fill this gap. This model includes parameters to characterize the harmonic and inharmonic voice sources, vocal tract transfer function, fundamental frequency, and amplitude of the voice, which together serve to completely quantify the integral sound of a target voice sample. In experiment 1, 200 voices with and without diagnosed vocal pathology were fit with the model using analysis-by-synthesis. The resulting synthetic voice samples were not distinguishable from the original voice tokens, suggesting that the model has all the parameters it needs to fully quantify voice quality. In experiment 2 parameters that model the harmonic voice source were removed one by one, and the voice tokens were re-synthesized with the reduced model. In every case the lower-dimensional models provided worse perceptual matches to the quality of the natural tokens than did the original set, indicating that the psychoacoustic model cannot be reduced in dimensionality without loss of fit to the data. Results confirm that this model can be validly applied to quantify voice quality in clinical and research applications.

Little is known about the nature or extent of everyday variability in voice quality. This paper describes a series of principal component analyses to explore within- and between-talker acoustic variation and the extent to which they conform to expectations derived from current models of voice perception. Based on studies of faces and cognitive models of speaker recognition, the authors hypothesized that a few measures would be important across speakers, but that much of within-speaker variability would be idiosyncratic. Analyses used multiple sentence productions from 50 female and 50 male speakers of English, recorded over three days. Twenty-six acoustic variables from a psychoacoustic model of voice quality were measured every 5 ms on vowels and approximants. Across speakers the balance between higher harmonic amplitudes and inharmonic energy in the voice accounted for the most variance (females = 20%, males = 22%). Formant frequencies and their variability accounted for an additional 12% of variance across speakers. Remaining variance appeared largely idiosyncratic, suggesting that the speaker-specific voice space is different for different people. Results further showed that voice spaces for individuals and for the population of talkers have very similar acoustic structures. Implications for prototype models of voice perception and recognition are discussed.

Little is known about the nature or extent of everyday variability in voice quality. This paper describes a series of principal component analyses to explore within- and between-talker acoustic variation and the extent to which they conform to expectations derived from current models of voice perception. Based on studies of faces and cognitive models of speaker recognition, the authors hypothesized that a few measures would be important across speakers, but that much of within-speaker variability would be idiosyncratic. Analyses used multiple sentence productions from 50 female and 50 male speakers of English, recorded over three days. Twenty-six acoustic variables from a psychoacoustic model of voice quality were measured every 5 ms on vowels and approximants. Across speakers the balance between higher harmonic amplitudes and inharmonic energy in the voice accounted for the most variance (females = 20%, males = 22%). Formant frequencies and their variability accounted for an additional 12% of variance across speakers. Remaining variance appeared largely idiosyncratic, suggesting that the speaker-specific voice space is different for different people. Results further showed that voice spaces for individuals and for the population of talkers have very similar acoustic structures. Implications for prototype models of voice perception and recognition are discussed.

Groups of coordinated underwater vehicles or sensors are powerful tools for monitoring the ocean. A requirement of many coordinated surveys is to determine a spatial reference between each node in a swarm. This work considers the self-localization of a swarm of independently moving vehicles using acoustic noise from a dominating incoherent source recorded by a single hydrophone onboard each vehicle. This method provides an inexpensive and infrastructure-free spatial reference between vehicles. Movement between the vehicles changes the swarm geometry and a self-localization estimate must be generated from data collected on short time scales. This challenges past self-localization approaches for acoustic arrays. To overcome this challenge, the proposed self-localization algorithm jointly estimates the vehicle geometry and the directionality of the ambient noise field, without prior knowledge of either estimate. To demonstrate this method, experimental results are provided when a boat is the main dominating source. The results demonstrate the ability to both estimate the direction of arrival of the boat and the relative positions of the vehicles in the swarm. The approach in this paper is not limited to moving vessels. Simulations are provided to examine three different factors that affect the proposed solution: inter-vehicle motion, vehicle geometry, and the azimuthal variance of the noise field.

Groups of coordinated underwater vehicles or sensors are powerful tools for monitoring the ocean. A requirement of many coordinated surveys is to determine a spatial reference between each node in a swarm. This work considers the self-localization of a swarm of independently moving vehicles using acoustic noise from a dominating incoherent source recorded by a single hydrophone onboard each vehicle. This method provides an inexpensive and infrastructure-free spatial reference between vehicles. Movement between the vehicles changes the swarm geometry and a self-localization estimate must be generated from data collected on short time scales. This challenges past self-localization approaches for acoustic arrays. To overcome this challenge, the proposed self-localization algorithm jointly estimates the vehicle geometry and the directionality of the ambient noise field, without prior knowledge of either estimate. To demonstrate this method, experimental results are provided when a boat is the main dominating source. The results demonstrate the ability to both estimate the direction of arrival of the boat and the relative positions of the vehicles in the swarm. The approach in this paper is not limited to moving vessels. Simulations are provided to examine three different factors that affect the proposed solution: inter-vehicle motion, vehicle geometry, and the azimuthal variance of the noise field.

The probability is one of the many factors which influence phonetic variation. Contextual probability, which describes how predictable a linguistic unit is in some local environment, has been consistently shown to modulate the phonetic salience of words and other linguistic units in speech production (the probabilistic reduction effect). In this paper the question of whether the probabilistic reduction effect, as previously observed for majority languages like English, is also found in a language (Kaqchikel Mayan) which has relatively rich morphology is explored. Specifically, whether the contextual predictability of words and morphemes influences their phonetic duration in Kaqchikel is examined. It is found that the contextual predictability of a word has a significant effect on its duration. The effect is manifested differently for lexical words and function words. It is also found that the contextual predictability of certain prefixes in Kaqchikel affects their duration, showing that contextual predictability may drive reduction effects at multiple levels of structure. While the findings are broadly consistent with many previous studies (primarily on English), some of the details of the results are different. These differences highlight the importance of examining the probabilistic reduction effect in languages beyond the majority, Indo-European languages most commonly investigated in experimental and corpus linguistics.

While it has been recognized that the multipath structure of the underwater acoustic (UWA) channel offers the potential for compressed sensing (CS) sparsity exploitation, the rapidly time varying arrivals induced by highly dynamic surfaces unfortunately pose significant difficulties to channel estimation. From the viewpoint of underwater acoustic propagation, with the exception of the highly time varying arrivals caused by dynamic surface, generally there exist relatively stationary or slowly changing arrivals caused by direct path or bottom reflection, which imply the adoption of a discriminate estimation method to handle sparse components with different time variation scale. By modeling the time varying UWA channels as a sparse set consisting of constant and time-varying supports, in this paper, estimation of time varying UWA channel is transformed into a problem of dynamic compressed sensing sparse recovery. The combination of a Kalman filter and compressed sensing is adopted to pursue the solution of it. Numerical simulations demonstrate the superiority of the proposed algorithm. In the form of a channel-estimation-based decision-feedback equalizer, the experimental results with the field data obtained in a shallow water acoustic communication experiment indicate that the proposed dynamic compressed sensing algorithm outperforms classic algorithms as well as CS algorithms.

Bayesian modeling and Hamiltonian Monte Carlo (HMC) are utilized to formulate a robust algorithm capable of simultaneously estimating anisotropic elastic properties and crystallographic orientation of a specimen from a list of measured resonance frequencies collected via Resonance Ultrasound Spectroscopy (RUS). Unlike typical optimization procedures which yield point estimates of the unknown parameters, computing a Bayesian posterior yields probability distributions for the unknown parameters, and HMC is an efficient way to compute this posterior. The algorithms described are demonstrated on RUS data collected from two parallelepiped specimens of structural metal alloys. First, the elastic constants for a specimen of fine-grain polycrystalline Ti-6Al-4 V with random crystallographic texture and isotropic elastic symmetry are estimated. Second, the elastic constants and crystallographic orientation for a single crystal Ni-based superalloy CMSX-4 specimen are accurately determined, using only measurements of the specimen geometry, mass, and resonance frequencies. The unique contributions of this paper are as follows: the application of HMC for sampling the Bayesian posterior of a probabilistic RUS model, and the procedure for simultaneous estimation of elastic constants and lattice-specimen misorientation. Compared to previous approaches these algorithms demonstrate superior convergence behavior, particularly when the initial parameterization is unknown, and enable substantially simplified experimental procedures.

This paper presents a demonstration of ambient acoustic noise processing on a set of free floating oceanic receivers whose relative positions vary with time. It is shown that it is possible to retrieve information that is relevant to the travel time between the receivers. With thousands of short time cross-correlations (10 s) of varying distance, it is shown that on average, the decrease in amplitude of the noise correlation function with increased separation follows a power law. This suggests that there may be amplitude information that is embedded in the noise correlation function. An incoherent beamformer is developed, which shows that it is possible to determine a source direction using an array with moving elements and large element separation. This incoherent beamformer is used to verify cases when the distribution of noise sources in the ocean allows one to recover travel time information between pairs of mobile receivers.

This paper presents a demonstration of ambient acoustic noise processing on a set of free floating oceanic receivers whose relative positions vary with time. It is shown that it is possible to retrieve information that is relevant to the travel time between the receivers. With thousands of short time cross-correlations (10 s) of varying distance, it is shown that on average, the decrease in amplitude of the noise correlation function with increased separation follows a power law. This suggests that there may be amplitude information that is embedded in the noise correlation function. An incoherent beamformer is developed, which shows that it is possible to determine a source direction using an array with moving elements and large element separation. This incoherent beamformer is used to verify cases when the distribution of noise sources in the ocean allows one to recover travel time information between pairs of mobile receivers.

As the primary means of communication, voice plays an important role in daily life. Voice also conveys personal information such as social status, personal traits, and the emotional state of the speaker. Mechanically, voice production involves complex fluid-structure interaction within the glottis and its control by laryngeal muscle activation. An important goal of voice research is to establish a causal theory linking voice physiology and biomechanics to how speakers use and control voice to communicate meaning and personal information. Establishing such a causal theory has important implications for clinical voice management, voice training, and many speech technology applications. This paper provides a review of voice physiology and biomechanics, the physics of vocal fold vibration and sound production, and laryngeal muscular control of the fundamental frequency of voice, vocal intensity, and voice quality. Current efforts to develop mechanical and computational models of voice production are also critically reviewed. Finally, issues and future challenges in developing a causal theory of voice production and perception are discussed.

Sound propagation of wind farms is typically simulated by the use of engineering tools that are neglecting some atmospheric conditions and terrain effects. Wind and temperature profiles, however, can affect the propagation of sound and thus the perceived sound in the far field. A better understanding and application of those effects would allow a more optimized farm operation towards meeting noise regulations and optimizing energy yield. This paper presents the parabolic equation (PE) model development for accurate wind turbine noise propagation. The model is validated against analytic solutions for a uniform sound speed profile, benchmark problems for nonuniform sound speed profiles, and field sound test data for real environmental acoustics. It is shown that PE provides good agreement with the measured data, except upwind propagation cases in which turbulence scattering is important. Finally, the PE model uses computational fluid dynamics results as input to accurately predict sound propagation for complex flows such as wake flows. It is demonstrated that wake flows significantly modify the sound propagation characteristics.

This paper introduces a parametric three-dimensional body-cover vocal fold model based on magnetic resonance imaging (MRI) of the human larynx. Major geometric features that are observed in the MRI images but missing in current vocal fold models are discussed, and their influence on vocal fold vibration is evaluated using eigenmode analysis. Proper boundary conditions for the model are also discussed. Based on control parameters corresponding to anatomic landmarks that can be easily measured, this model can be adapted toward a subject-specific vocal fold model for voice production research and clinical applications.

Sound propagation of wind farms is typically simulated by the use of engineering tools that are neglecting some atmospheric conditions and terrain effects. Wind and temperature profiles, however, can affect the propagation of sound and thus the perceived sound in the far field. A better understanding and application of those effects would allow a more optimized farm operation towards meeting noise regulations and optimizing energy yield. This paper presents the parabolic equation (PE) model development for accurate wind turbine noise propagation. The model is validated against analytic solutions for a uniform sound speed profile, benchmark problems for nonuniform sound speed profiles, and field sound test data for real environmental acoustics. It is shown that PE provides good agreement with the measured data, except upwind propagation cases in which turbulence scattering is important. Finally, the PE model uses computational fluid dynamics results as input to accurately predict sound propagation for complex flows such as wake flows. It is demonstrated that wake flows significantly modify the sound propagation characteristics.

This paper demonstrates the importance of accounting for environmental effects on passive underwater acoustic monitoring results. The situation considered is the reduction in shipping off the California coast between 2008-2010 due to the recession and environmental legislation. The resulting variations in ocean noise change the probability of detecting marine mammal vocalizations. An acoustic model was used to calculate the time-varying probability of detecting humpback whale vocalizations under best-guess environmental conditions and varying noise. The uncorrected call counts suggest a diel pattern and an increase in calling over a two-year period; the corrected call counts show minimal evidence of these features.

Passive acoustic monitoring of marine mammal calls is an increasingly important method for assessing population numbers, distribution, and behavior. A common mistake in the analysis of marine mammal acoustic data is formulating conclusions about these animals without first understanding how environmental properties such as bathymetry, sediment properties, water column sound speed, and ocean acoustic noise influence the detection and character of vocalizations in the acoustic data. The approach in this paper is to use Monte Carlo simulations with a full wave field acoustic propagation model to characterize the site specific probability of detection of six types of humpback whale calls at three passive acoustic monitoring locations off the California coast. Results show that the probability of detection can vary by factors greater than ten when comparing detections across locations, or comparing detections at the same location over time, due to environmental effects. Effects of uncertainties in the inputs to the propagation model are also quantified, and the model accuracy is assessed by comparing calling statistics amassed from 24,690 humpback units recorded in the month of October 2008. Under certain conditions, the probability of detection can be estimated with uncertainties sufficiently small to allow for accurate density estimates.

The validity of perceptual measures of vocal quality has been neglected in studies of voice, which focus more commonly on rater reliability. Validity depends in part on reliability, because an unreliable test does not measure what it is intended to measure. However, traditional measures of rating reliability only partially represent interrater agreement, because they cannot reflect variations or patterns of agreement for specific voice samples. In this paper the likelihood that two raters would agree in their ratings of a single voice is examined, for each voice in five previously gathered data sets. Results do not support the continued assumption that traditional rating procedures produce useful indices of listeners' perceptions. Listeners agreed very poorly in the midrange of scales for breathiness and roughness, and mean ratings in the midrange of such scales did not represent the extent to which a voice possesses a quality, but served only to indicate that listeners disagreed. Techniques like analysis by synthesis or judgment of similarity avoid decomposing quality into constituent dimensions, and do not require a listener to compare an external stimulus to an unstable internal representation, thus decreasing the error in measures of quality. Modeling individual differences in perception can increase the variance accounted for in models of quality, further reducing the error in perceptual measures. Thus such techniques may provide valid alternatives to current approaches.