Understanding eyewitness lineup judgment processes is critical, both from a theoretical standpoint (to better understand human memory) and from a practical one (to prevent wrongful convictions and criminals walking free). Currently, two influential theories attempt to explain lineup decision making: the theory of eyewitness judgment strategies (Lindsay & Wells, 1985), and the signal detection theory-informed diagnostic-feature-detection hypothesis (Wixted & Mickes, 2014). The theory of eyewitness judgment strategies posits that eyewitnesses can adopt either an absolute judgment strategy (base identification decisions only on their memory for the culprit) or a relative judgment strategy (base identification decision on lineup member comparisons). This theory further predicts that relative judgment strategies lead to an increase in false identifications. Contrast this with the diagnostic-feature-detection hypothesis, which predicts that the lineup member comparisons inherent to relative strategies promote greater accuracy. These two theories have been tested indirectly (i.e., via lineup format manipulations tangentially related to the theory), but there is a lack of direct tests. Across two experiments (Ns = 192, 584), we presented participants with simulated crime videos and corresponding lineups, and manipulated judgment strategy using explicit absolute and relative strategy instructions and a novel rank-order manipulation meant to encourage lineup member comparisons. We found no substantial differences in identifications or overall accuracy as a function of instructed strategy. These results are inconsistent with the theory of eyewitness judgment strategies but provide some support for the diagnostic-feature-detection hypothesis. We discuss implications for both theories and future lineup research. / Graduate

Current Signal Detection Theory models of source memory necessitate assumptions about the underlying distributions of source strengths to describe source memory performance. The current experiments applied a modified version of the same-different task in order to plot individual memory stimuli along a controlled dimension of the average frequency of voices. This technique allowed us to determine that subjects were using an independent-observations strategy rather than a differencing strategy when deciding whether two test words were spoken by the same or different female speakers at study. By including two male and two female voices and changing the task distinction from same or different speakers to same or different genders, we predictably switched subjects’ decision strategies. With this new same-different memory design, we are one step closer to ending our reliance on measures that are inferred from data to describe subjects’ source memory performance.

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This thesis is concerned with the estimation of the acoustic source signal-bearing in the presence of a reflective surface. The method to estimate the bearing of the acoustic source is the cross spectral analysis of two microphones in which the characteristics of the acoustic source signal is preserved. The echo of the original time history is removed by an advanced signal processing technique, called Cepstrum analysis. This technique is successfully applied to remove the contaminating effect of echoes in the measured time history. The work described is divided into several steps. A computer program was developed to examine the effects of reflection and the behavior of the Cepstrum under different operating conditions using simulated signals. The algorithm was then adapted to process experimental signals acquired under laboratory test conditions. An improved liftering process based on the knowledge of the echo time was developed in order to make the method relatively automated. The results of the experimental work show that the Cepstral analysis can be both effective and efficient in removing reflected signals from convolved time histories and thus successful in correcting a bearing estimate contaminated by an echo. / M.S.

The question of whether or not an individual suffering from a hearing loss is capable of hearing an auditory alarm or warning is an extremely important industrial safety issue. International standard ISO 773 1—1986(E), Danger Signals for Work Places — Auditory Danger Signals, requires that any auditory alarm or warning be audible to all individuals in the workplace, including those suffering from a hearing loss and/or wearing hearing protection devices (HPDs). Very little research has been conducted to determine how an individual's hearing level affects his/her ability to detect an auditory alarm or warning in a high-noise environment while wearing an HPD. The research effort described herein was undertaken to determine how the ability to detect an alarm or warning signal changed for individuals with normal hearing and two levels of hearing loss as the levels of masking noise and alarm were manipulated. Pink noise was used as the masker since it is a generally-accepted, generic substitute for industrial noise. A heavy-equipment reverse alarm was used as the signal since it is a common alarm in industrial facilities and construction sites. The rating method paradigm of signal detection theory was used as the experimental procedure in order to separate the subjects’ absolute sensitivities to the alarm from their individual criteria for deciding to respond in an affirmative manner. Results indicated that even at a fairly low signal-to-noise ratio (0 dB), individuals with a substantial hearing loss [a pure-tone average (PTA) hearing level on the order of 45-50 dBHL in both ears] are capable of hearing the alarm while wearing a high-attenuation earmuff. Predictive models were developed using nonlinear regression techniques. These models may be used to predict whether or not individuals with known hearing levels will be capable of hearing the alarm under known conditions or to determine the level of alarm presentation in order to be heard reliably by individuals with a specified range of hearing for given noise levels / Ph. D.

Microphone arrays have seen wide usage in a variety of fields; especially in sonar, acoustic source monitoring and localization, telecommunications, and diagnostic medicine. The goal of most of these applications is to detect or extract a signal of interest. This task is complicated by the presence of interferers and noise, which corrupts the recorded array signals. This dissertation explores two new techniques that increase signal clarity: interferer suppression and emergent signal detection. Spatial processing is often used to suppress interferers that are spatially distinct from the signal of interest. If the signal of interest and the interferer are statistically independent, blind source separation can be used to statistically extract the signal of interest. The first new method to improve signal clarity presented in this work combines spatial processing with blind source separation to suppress interferers. This technique allows for the separation of independent sources that are not necessarily simultaneously mixed or spatially distinct. Simulations and experiments are used to show the capability of the new algorithm for a variety of conditions. The major contributions in this dissertation under this topic are to use independent component analysis to extract the signal of interest from a set of array signals, and to improve existing independent component analysis algorithms to allow for time delayed mixing. This dissertation presents a novel method of improving signal clarity through emergent signal detection. By determining which time frames contain the signal of interest, frames that contain only interferers and noise can be eliminated. When a new signal of interest emerges in a measurement of a mixed set of sources, the principal component subspace is altered. By examining the change in the subspace, the emergent signal can be robustly detected. This technique is highly effective for signals that have a near constant sample variance, but is successful at detecting a wide variety of signals, including voice signals. To improve performance, the algorithm uses a feed-forward processing technique. This is helpful for the VAD application because voice does not have a constant sample variance. Experiments and simulations are used to demonstrate the performance of the new technique. / Ph. D.

Frequency Hopping Spread Spectrum (FHSS) signaling is used across many devices operating in both regulated and unregulated bands. In either situation, if there is a malicious device operating within these bands, or more simply a user operating out of the required specifications, the identification this user important to insure communication link integrity and interference mitigation. The identification of a user involves the grouping of that users signal transmissions, and the separation of those transmission from transmissions of other users in a shared frequency band. Traditional signal separation methods often require difficult to obtain hardware fingerprinting characteristics or approximate geo-location estimates. This work will consider the characteristics of FHSS signals that can be extracted directly from signal detection. From estimates of these hopping characteristics, novel source separation with classic clustering algorithms can be performed. Background knowledge derived from the time domain representation of received waveforms can improve these clustering methods with the novel application of cannot-link pairwise constraints to signal separation. For equivalent clustering accuracy, constraint-based clustering tolerates higher parameter estimation error, caused by diminishing received signal-to-noise ratio (SNR), for example. Additionally, prior work does not fully cover the implications of detecting and estimating FHSS signals using image segmentation on a Time-Frequency (TF) waterfall. This work will compare several methods of FHSS signal detection, and discuss how each method may effect estimation accuracy and signal separation quality. The use of constraint-based clustering is shown to provide higher clustering accuracy, resulting in more reliable separation and identification of active users in comparison to traditional clustering methods. / Master of Science / The expansion of technology in areas such as smart homes and appliances, personal devices, smart vehicles, and many others, leads to more and more devices using common wireless communication techniques such as WiFi and Bluetooth. While the number of wirelessly connected users expands, the range of frequencies that support wireless communications does not. It is therefore essential that each of these devices unselfishly share the available wireless resources. If a device is using more resources than the required limits, or causing interference with other’s communications, this device will impact many others negatively and therefore preventative action must be taken to prevent further disruption in the wireless environment. Before action can be taken however, the device must first be identified in a mixture of other wireless activity. To identify a specific device, first, a wireless receiver must be in close enough proximity to detect the power that the malicious device is emitting through its wireless communication. This thesis provides a method that can be used to identify a problem user based only off of its wireless transmission behavior. The performance of this identification is shown with respect to the received signal power which represents the necessary range that a listening device must be to identify and separate a problem user from other cooperative users that are communicating wirelessly.