In virtual reality (VR) studies, where object distance plays a role of an independent variable, unknown egocentric distance perception values can affect the interpretation of the collected data. It is known that the perceived egocentric distance in VR is often underestimated, which may affect other judgments that implicitly depend on it. In order to prepare later experiments on the effect of distance on audiovisual (a)synchrony perception, this study quantifies the egocentric distance perception in a virtual indoor environment using two methods: verbal judgment (VJ) and position adjustment (PA). For the VJ method, participants verbally estimated the distance between their own position and a cardboard box position at a distance between a nominal 5 m and 30 m, with increments of 5 m. For the PA method, participants were asked to position a cardboard box to an instructed distance of a nominal 5 m to 13 m, with increments of 1 m. Both methods (VJ and PA) showed significant and substantial levels of underestimation, where simulated distance was underestimated on average by 38.5%. Our study suggests taking these findings into account when treating distance as an independent parameter in experiments conducted in purely simulated virtual spaces which do not exist physically in the real world.

In this article we present an approach that uses sound to communicate geometrical data related to a virtual object. This has been developed in the framework of a multimodal interface for product design. The interface allows a designer to evaluate the quality of a 3-D shape using touch, vision, and sound. Two important considerations addressed in this article are the nature of the data that is sonified and the haptic interaction between the user and the interface, which in fact triggers the sound and influences its characteristics. Based on these considerations, we present a number of sonification strategies that are designed to map the geometrical data of interest into sound. The fundamental frequency of various sounds was used to convey the curve shape or the curvature to the listeners. Two evaluation experiments are described, one involves partipants with a varied background, the other involved the intended users, i.e. participants with a background in industrial design. The results show that independent of the sonification method used and independent of whether the curve shape or the curvature were sonified, the sonification was quite successful. In the first experiment participants had a success rate of about 80% in a multiple choice task, in the second experiment it took the participants on average less than 20 seconds to find the maximum, minimum or inflection points of the curvature of a test curve.

Although the field of automatic speaker recognition (ASR) has been the subject of extensive research over the past decades, the lack of robustness against background noise has remained a major challenge. This paper describes a noise-robust speaker recognition system that combines missing data (MD) recognition with the adaptation of speaker models using a universal background model (UBM). For MD recognition, the identification of reliable and unreliable feature components is required. For this purpose, the signal-to-noise ratio (SNR) based mask estimation performance of various state-of-the art noise estimation techniques and noise reduction schemes is compared. Speaker recognition experiments show that the usage of a UBM in combination with missing data recognition yields substantial improvements in recognition performance, especially in the presence of highly non-stationary background noise at low SNRs.

In this study, we present a binaural scene analyzer that is able to simultaneously localize, detect and identify a known number of target speakers in the presence of spatially positioned noise sources and reverberation. In contrast to many other binaural cocktail party processors, the proposed system does not require a priori knowledge about the azimuth position of the target speakers. The proposed system consists of three main building blocks: binaural localization, speech source detection, and automatic speaker identification. First, a binaural front-end is used to robustly localize relevant sound source activity. Second, a speech detection module based on missing data classification is employed to determine whether detected sound source activity corresponds to a speaker or to an interfering noise source using a binary mask that is based on spatial evidence supplied by the binaural front-end. Third, a second missing data classifier is used to recognize the speaker identities of all detected speech sources. The proposed system is systematically evaluated in simulated adverse acoustic scenarios. Compared to state-of-the art MFCC recognizers, the proposed model achieves significant speaker recognition accuracy improvements.

Although extensive research has been done in the field of machine-based localization, the degrading effect of reverberation and the presence of multiple sources on localization performance has remained a major problem. Motivated by the ability of the human auditory system to robustly analyze complex acoustic scenes, the associated peripheral stage is used in this paper as a front-end to estimate the azimuth of sound sources based on binaural signals. One classical approach to localize an acoustic source in the horizontal plane is to estimate the interaural time difference (ITD) between both ears by searching for the maximum in the cross-correlation function. Apart from ITDs, the interaural level difference (ILD) can contribute to localization, especially at higher frequencies where the wavelength becomes smaller than the diameter of the head, leading to ambiguous ITD information. The interdependency of ITD and ILD on azimuth is a complex pattern that depends also on the room acoustics, and is therefore learned by azimuth-dependent Gaussian mixture models (GMMs). Multiconditional training is performed to take into account the variability of the binaural features which results from multiple sources and the effect of reverberation. The proposed localization model outperforms state-of-the-art localization techniques in simulated adverse acoustic conditions.

The use of psychoacoustical masking models for audio coding applications has been wide spread over the past decades. In such applications, it is typically assumed that the original input signal serves as a masker for the distortions that are introduced by the lossy coding method that is used. Such masking models are based on the peripheral bandpass filtering properties of the auditory system and basically evaluate the distortion-to-masker ratio within each auditory filter. Up to now these models have been based on the assumption that the masking of distortions is governed by the auditory filter for which the ratio between distortion and masker is largest. This assumption, however, is not in line with some new findings within the field of psychoacoustics. A more accurate assumption would be that the human auditory system is able to integrate distortions that are present within a range of auditory filters. In this contribution a new model is presented which is in line with new psychoacoustical studies and which is suitable for application within an audio codec. Although this model can be used to derive a masking curve, the model also gives a measure for the detectability of distortions provided that distortions are not too large.

The perceptual relevance of changes to glottal-pulse parameters is studied. First, it is demonstrated that a distance measure based on excitation patterns can predict audibility discrimination thresholds for small changes to the R parameters of the Liljencrants-Fant (LF) model. Next, by using this measure the perceptual relevance of the LF parameters is quantified. Results are presented for a number of sets of glottal-pulse parameters that were taken from literature, representing distinct voice qualities.

This paper presents the result of psychophysicalexperiments dealing with pitch-marker positioning withinthe Pitch Synchronous OverLap and Add (PSOLA)framework. Sustained natural vowels were PSOLAmodifiedin fundamental frequency. The experimentswere aimed at determining the auditory sensitivity to (1)deterministic shifts of either all or single pitch markerswithin a sequence, and (2) random shifts of all pitchmarkers niner"). As for deterministic shifts of all pitchmarkers, the results were in reasonable agreement withresults obtained previously for synthetic formant signals.For deterministic shifts of single pitch markers,thresholds depended on position in the sequence.Detection thresholds for jittered shifts were comparableto thresholds for detecting jitter in pulse trains. Theranking of the thresholds for these three conditionsindicated that the auditory system is more sensitive todynamic (modulation) cues rather than to static (timbral)cues arising from shifts in pitch-marker positioning.

The detection of narrowband noise targets in maskers of equal-amplitude harmonics has been reported in an earlier contribution [4]. The results revealed that the masking patterns are strongly dependent on the fundamental frequency of the masker. In the present paper we report masking patterns of broadband harmonic complexes as a function of their spectral tilt and level, and the masking pattern of a vowel sound. We will focus on how the spectral masking patterns are influenced by temporal processes in the auditory system