Binaural processing model based on contralateral inhibition. I. Model structure.

This article presents a quantitative binaural signal detection model which extends the monaural model described by Dau et al. [J. Acoust. Soc. Am. 99, 3615-3622 (1996)]. The model is divided into three stages. The first stage comprises peripheral preprocessing in the right and left monaural channels. The second stage is a binaural processor which produces a time-dependent internal representation of the binaurally presented stimuli. This stage is based on the Jeffress delay line extended with tapped attenuator lines. Through this extension, the internal representation codes both interaural time and intensity differences. In contrast to most present-day models, which are based on excitatory-excitatory interaction, the binaural interaction in the present model is based on contralateral inhibition of ipsilateral signals. The last stage, a central processor, extracts a decision variable that can be used to detect the presence of a signal in a detection task, but could also derive information about the position and the compactness of a sound source. In two accompanying articles, the model predictions are compared with data obtained with human observers in a great variety of experimental conditions.

Binaural processing model based on contralateral inhibition. II. Dependence on spectral parameters.

This and two accompanying articles [Breebaart et al., J. Acoust. Soc. Am. 110, 1074-1088 (2001); 110, 1105-1117 (2001)] describe a computational model for the signal processing in the binaural auditory system. The model consists of several stages of monaural and binaural preprocessing combined with an optimal detector. In the present article the model is tested and validated by comparing its predictions with experimental data for binaural discrimination and masking conditions as a function of the spectral parameters of both masker and signal. For this purpose, the model is used as an artificial observer in a three-interval, forced-choice adaptive procedure. All model parameters were kept constant for all simulations described in this and the subsequent article. The effects of the following experimental parameters were investigated: center frequency of both masker and target, bandwidth of masker and target, the interaural phase relations of masker and target, and the level of the masker. Several phenomena that occur in binaural listening conditions can be accounted for. These include the wider effective binaural critical bandwidth observed in band-widening NoS(pi) conditions, the different masker-level dependence of binaural detection thresholds for narrow- and for wide-band maskers, the unification of IID and ITD sensitivity with binaural detection data, and the dependence of binaural thresholds on frequency.

Binaural processing model based on contralateral inhibition. III. Dependence on temporal parameters.

This paper and two accompanying papers [Breebaart et al., J. Acoust. Soc. Am. 110, 1074-1088 (2001); 110, 1089-1104 (2001)] describe a computational model for the signal processing of the binaural auditory system. The model consists of several stages of monaural and binaural preprocessing combined with an optimal detector. Simulations of binaural masking experiments were performed as a function of temporal stimulus parameters and compared to psychophysical data adapted from literature. For this purpose, the model was used as an artificial observer in a three-interval, forced-choice procedure. All model parameters were kept constant for all simulations. Model predictions were obtained as a function of the interaural correlation of a masking noise and as a function of both masker and signal duration. Furthermore, maskers with a time-varying interaural correlation were used. Predictions were also obtained for stimuli with time-varying interaural time or intensity differences. Finally, binaural forward-masking conditions were simulated. The results show that the combination of a temporal integrator followed by an optimal detector in the time domain can account for all conditions that were tested, except for those using periodically varying interaural time differences (ITDs) and those measuring interaural correlation just-noticeable differences (jnd's) as a function of bandwidth.

The influence of interaural stimulus uncertainty on binaural signal detection.

This paper investigated the influence of stimulus uncertainty in binaural detection experiments and the predictions of several binaural models for such conditions. Masked thresholds of a 500-Hz sinusoid were measured in an NrhoSpi condition for both running and frozen-noise maskers using a three interval, forced-choice (3IFC) procedure. The nominal masker correlation varied between 0.64 and 1, and the bandwidth of the masker was either 10, 100, or 1,000 Hz. The running-noise thresholds were expected to be higher than the frozen-noise thresholds because of stimulus uncertainty in the running-noise conditions. For an interaural correlation close to +1, no difference between frozen-noise and running-noise thresholds was expected for all values of the masker bandwidth. These expectations were supported by the experimental data: for interaural correlations less than 1.0, substantial differences between frozen and running-noise conditions were observed for bandwidths of 10 and 100 Hz. Two additional conditions were tested to further investigate the influence of stimulus uncertainty. In the first condition a different masker sample was chosen on each trial, but the correlation of the masker was forced to a fixed value. In the second condition one of two independent frozen-noise maskers was randomly chosen on each trial. Results from these experiments emphasized the influence of stimulus uncertainty in binaural detection tasks: if the degree of uncertainty in binaural cues was reduced, thresholds decreased towards thresholds in the conditions without any stimulus uncertainty. In the analysis of the data, stimulus uncertainty was expressed in terms of three theories of binaural processing: the interaural correlation, the EC theory, and a model based on the processing of interaural intensity differences (IIDs) and interaural time differences (ITDs). This analysis revealed that none of the theories tested could quantitatively account for the observed thresholds. In addition, it was found that, in conditions with stimulus uncertainty, predictions based on correlation differ from those based on the EC theory.

The contribution of static and dynamically varying ITDs and IIDs to binaural detection.

This paper investigates the relative contribution of various interaural cues to binaural unmasking in conditions with an interaurally in-phase masker and an out-of-phase signal (MoS pi). By using a modified version of multiplied noise as the masker and a sinusoid as the signal, conditions with only interaural intensity differences (IIDs), only interaural time differences (ITDs), or combinations of the two were realized. In addition, the experimental procedure allowed the presentation of specific combinations of static and dynamically varying interaural differences. In these conditions with multiplied noise as masker, the interaural differences have a bimodal distribution with a minimum at zero IID or ITD. Additionally, by using the sinusoid as masker and the multiplied noise as signal, a unimodal distribution of the interaural differences was realized. Through this variation in the shape of the distributions, the close correspondence between the change in the interaural cross correlation and the size of the interaural differences is no longer found, in contrast to the situation for a Gaussian-noise masker [Domnitz and Colburn, J. Acoust. Soc. Am. 59, 598-601 (1976)]. When analyzing the mean thresholds across subjects, the experimental results could not be predicted from parameters of the distributions of the interaural differences (the mean, the standard deviation, or the root-mean-square value). A better description of the subjects' performance was given by the change in the interaural correlation, but this measure failed in conditions which produced a static interaural intensity difference. The data could best be described by using the energy of the difference signal as the decision variable, an approach similar to that of the equalization and cancellation model.