A unified beamforming and source separation model for static and dynamic human-robot interaction.

This paper presents a unified model for combining beamforming and blind source separation (BSS). The validity of the model's assumptions is confirmed by recovering target speech information in noise accurately using Oracle information. Using real static human-robot interaction (HRI) data, the proposed combination of BSS with the minimum-variance distortionless response beamformer provides a greater signal-to-noise ratio (SNR) than previous parallel and cascade systems that combine BSS and beamforming. In the difficult-to-model HRI dynamic environment, the system provides a SNR gain that was 2.8 dB greater than the results obtained with the cascade combination, where the parallel combination is infeasible.

Automatic Detection of Dyspnea in Real Human-Robot Interaction Scenarios.

A respiratory distress estimation technique for telephony previously proposed by the authors is adapted and evaluated in real static and dynamic HRI scenarios. The system is evaluated with a telephone dataset re-recorded using the robotic platform designed and implemented for this study. In addition, the original telephone training data are modified using an environmental model that incorporates natural robot-generated and external noise sources and reverberant effects using room impulse responses (RIRs). The results indicate that the average accuracy and AUC are just 0.4% less than those obtained with matched training/testing conditions with simulated data. Quite surprisingly, there is not much difference in accuracy and AUC between static and dynamic HRI conditions. Moreover, the beamforming methods delay-and-sum and MVDR lead to average improvement in accuracy and AUC equal to 8% and 2%, respectively, when applied to training and testing data. Regarding the complementarity of time-dependent and time-independent features, the combination of both types of classifiers provides the best joint accuracy and AUC score.

Effect of Titrated Exposure to Non-Traumatic Noise on Unvoiced Speech Recognition in Human Listeners with Normal Audiological Profiles.

Non-traumatic noise exposure has been shown in animal models to impact the processing of envelope cues. However, evidence in human studies has been conflicting, possibly because the measures have not been specifically parameterized based on listeners' exposure profiles. The current study examined young dental-school students, whose exposure to high-frequency non-traumatic dental-drill noise during their course of study is systematic and precisely quantifiable. Twenty-five dental students and twenty-seven non-dental participants were recruited. The listeners were asked to recognize unvoiced sentences that were processed to contain only envelope cues useful for recognition and have been filtered to frequency regions inside or outside the dental noise spectrum. The sentences were presented either in quiet or in one of the noise maskers, including a steady-state noise, a 16-Hz or 32-Hz temporally modulated noise, or a spectrally modulated noise. The dental students showed no difference from the control group in demographic information, audiological screening outcomes, extended high-frequency thresholds, or unvoiced speech in quiet, but consistently performed more poorly for unvoiced speech recognition in modulated noise. The group difference in noise depended on the filtering conditions. The dental group's degraded performances were observed in temporally modulated noise for high-pass filtered condition only and in spectrally modulated noise for low-pass filtered condition only. The current findings provide the most direct evidence to date of a link between non-traumatic noise exposure and supra-threshold envelope processing issues in human listeners despite the normal audiological profiles.

The fMRI Data of Thompson et al. (2006) Do Not Constrain How the Human Midbrain Represents Interaural Time Delay.

This commentary provides an alternate interpretation of the fMRI data that were presented in a communication to the journal Nature Neuroscience (Thompson et al., Nat. Neurosci. 9: 1096-1098, 2006 ). The authors argued that their observations demonstrated that traditional models of binaural hearing which incorporate "internal delays," such as the coincidence-counting mechanism proposed by Jeffress and quantified by Colburn, are invalid, and that a new model for human interaural time delay processing must be developed. We argue that the fMRI data presented do not strongly favor either the refutation or the retention of the traditional models, although they may be useful in constraining the physiological sites of various processing stages. The conclusions of Thompson et al. are based on the locations of maximal activity in the midbrain in response to selected binaural signals. These locations are inconsistent with well-known perceptual attributes of the stimuli under consideration, as is noted by the authors, which suggests that further processing is involved in forming the percept of subjective lateral position.

A framework for testing and comparing binaural models.

Auditory research has a rich history of combining experimental evidence with computational simulations of auditory processing in order to deepen our theoretical understanding of how sound is processed in the ears and in the brain. Despite significant progress in the amount of detail and breadth covered by auditory models, for many components of the auditory pathway there are still different model approaches that are often not equivalent but rather in conflict with each other. Similarly, some experimental studies yield conflicting results which has led to controversies. This can be best resolved by a systematic comparison of multiple experimental data sets and model approaches. Binaural processing is a prominent example of how the development of quantitative theories can advance our understanding of the phenomena, but there remain several unresolved questions for which competing model approaches exist. This article discusses a number of current unresolved or disputed issues in binaural modelling, as well as some of the significant challenges in comparing binaural models with each other and with the experimental data. We introduce an auditory model framework, which we believe can become a useful infrastructure for resolving some of the current controversies. It operates models over the same paradigms that are used experimentally. The core of the proposed framework is an interface that connects three components irrespective of their underlying programming language: The experiment software, an auditory pathway model, and task-dependent decision stages called artificial observers that provide the same output format as the test subject.