Tone-in-noise detection has been studied for decades; however, it is not completely understood what cue or cues are used by listeners for this task. Model predictions based on energy in the critical band are generally more successful than those based on temporal cues, except when the energy cue is not available. Nevertheless, neither energy nor temporal cues can explain the predictable variance for all listeners. In this study, it was hypothesized that better predictions of listeners' detection performance could be obtained using a nonlinear combination of energy and temporal cues, even when the energy cue was not available. The combination of different cues was achieved using the logarithmic likelihood-ratio test (LRT), an optimal detector in signal detection theory. A nonlinear LRT-based combination of cues was proposed, given that the cues have Gaussian distributions and the covariance matrices of cue values from noise-alone and tone-plus-noise conditions are different. Predictions of listeners' detection performance for three different sets of reproducible noises were computed with the proposed model. Results showed that predictions for hit rates approached the predictable variance for all three datasets, even when an energy cue was not available.
We present a forensic technique for analyzing a printed image in order to trace the originating printer. Our method, which is applicable for commonly used electrophotographic (EP) printers, operates by exploiting the geometric distortion that these devices inevitably introduce in the printing process. In the proposed method, first a geometric distortion signature is estimated for an EP printer. This estimate is obtained using only the images printed on the printer and without access to the internal printer controls. Once a database of printer signatures is available, the printer utilized to print a test image is identified by computing the geometric distortion signature from test image and correlating the computes signatures against the printer signatures in the database. Experiments conducted over a corpus of EP printers demonstrate that the geometric distortion signatures of test documents exhibit high correlation with the corresponding printer signatures and a low correlation with other printer signatures. The method is therefore quite promising for forensic printer identification applications. We highlight several of the capabilities and challenges for the method.
The addition of out-of-phase tones to in-phase noises results in dynamic interaural level difference (ILD) and interaural time difference (ITD) cues for the dichotic tone-in-noise detection task. Several models have been used to predict listeners' detection performance based on ILD, ITD, or different combinations of the two cues. The models can be tested using detection performance from an ensemble of reproducible-noise maskers. Previous models cannot predict listeners' detection performance for reproducible-noise maskers without fitting the data. Here, two models were tested for narrowband and wideband reproducible-noise experiments. One model was a linear combination of ILD and ITD that included the generally ignored correlation between the two cues. The other model was based on a newly proposed cue, the slope of the interaural envelope difference (SIED). Predictions from both models explained a significant portion of listeners' performance for detection of a 500-Hz tone in wideband noise. Predictions based on the SIED approached the predictable variance in the wideband condition. The SIED represented a nonlinear combination of ILD and ITD, with the latter cue dominating. Listeners did not use a common strategy (cue) to detect tones in the narrowband condition and may use different single frequencies or different combinations of frequency channels.
Tone-in-noise detection tasks with reproducible noise maskers have been used to identify cues that listeners use to detect signals in noisy environments. Previous studies have shown that energy, envelope, and finestructure cues are significantly correlated to listeners' performance for detection of a 500-Hz tone in noise. In this study, envelope cues were examined for both diotic and dichotic tone-in-noise detection using both stimulus-based signal processing and physiological models. For stimulus-based envelope cues, a modified envelope slope model was used for the diotic condition and the binaural slope of the interaural envelope difference model for the dichotic condition. Stimulusbased models do not include key nonlinear transformations in the auditory periphery such as compression, rate and dynamic range adaptation, and rate saturation, all of which affect the encoding of the stimulus envelope. For physiological envelope cues, stimuli were passed through models for the auditory nerve (AN), cochlear nucleus, and inferior colliculus (IC). The AN and cochlear nucleus models included appropriate modulation gain, another transformation of the stimulus envelope that is not typically included in stimulus-based models. A model IC cell was simulated with a linear band-pass modulation filter. The average discharge rate and response fluctuations of the model IC cell were compared to human performance. Previous studies have predicted a significant amount of the variance across reproducible noise maskers in listeners' detection using stimulusbased envelope cues. In this study, a physiological model that includes neural mechanisms that affect encoding of the stimulus envelope predicts a similar amount of the variance in listeners' performance across noise maskers.
By formulating the problem of ordering the outputs observed from a device over time, we pose a new problem in forensics and propose a framework for addressing this problem of device temporal forensics. Our proposed framework is based on a two-stage approach wherein time-dependent device parameters are first estimated from observed outputs and the resulting estimates are then temporally ordered by employing a Markov model for the temporal evolution of device parameters and exploiting the data processing inequality in information theory. We demonstrate and evaluate a simple realization of the framework for digital camera forensics based on photo-response non-uniformity. Results obtained over a database of online images indicate that the method provides accurate temporal ordering.
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