The ability to localize sound sources in three-dimensional space was tested in humans. In Experiment 1, naive subjects listened to noises filtered with sub

Monaural spectral features are important for human sound-source localization in sagittal planes, including front-back discrimination and elevation perception. These directional features result from the acoustic filtering of incoming sounds by the listener's morphology and are described by listener-specific head-related transfer functions (HRTFs). This article proposes a probabilistic, functional model of sagittal-plane localization that is based on human listeners' HRTFs. The model approximates spectral auditory processing, accounts for acoustic and non-acoustic listener specificity, allows for predictions beyond the median plane, and directly predicts psychoacoustic measures of localization performance. The predictive power of the listener-specific modeling approach was verified under various experimental conditions: The model predicted effects on localization performance of band limitation, spectral warping, non-individualized HRTFs, spectral resolution, spectral ripples, and high-frequency attenuation in speech. The functionalities of vital model components were evaluated and discussed in detail. Positive spectral gradient extraction, sensorimotor mapping, and binaural weighting of monaural spatial information were addressed in particular. Potential applications of the model include predictions of psychophysical effects, for instance, in the context of virtual acoustics or hearing assistive devices.

The Auditory Modeling Toolbox (AMT) is a MATLAB/Octave toolbox for the development and application of computational auditory models with a particular focus on binaural hearing. The AMT aims for a consistent implementation of auditory models, well-structured in-code documentation, and inclusion of auditory data required to run the models. The motivation is to provide a toolbox able to reproduce the model predictions and allowing students and researchers to work with and to advance existing models. In the AMT, model implementations can be evaluated in two stages: by running so-called demonstrations, which are quick presentations of a model, and by starting so-called experiments aimed at reproducing results from the corresponding publications. Here, we describe the tools and mechanisms available within the framework of all AMT 1.x versions. The recently released AMT 1.1 includes over 60 models and is freely available as an open-source package fromhttps://www.amtoolbox.org.

Sound externalization, or the perception that a sound source is outside of the head, is an intriguing phenomenon that has long interested psychoacousticians. While previous reviews are available, the past few decades have produced a substantial amount of new data.In this review, we aim to synthesize those data and to summarize advances in our understanding of the phenomenon. We also discuss issues related to the definition and measurement of sound externalization and describe quantitative approaches that have been taken to predict the outcomes of externalization experiments. Last, sound externalization is of practical importance for many kinds of hearing technologies. Here, we touch on two examples, discussing the role of sound externalization in augmented/virtual reality systems and bringing attention to the somewhat overlooked issue of sound externalization in wearers of hearing aids.

We study the ability of deep neural networks (DNNs) to restore missing audio content based on its context, i.e., inpaint audio gaps. We focus on a condition which has not received much attention yet: gaps in the range of tens of milliseconds. We propose a DNN structure that is provided with the signal surrounding the gap in the form of time-frequency (TF) coefficients. Two DNNs with either complex-valued TF coefficient output or magnitude TF coefficient output were studied by separately training them on inpainting two types of audio signals (music and musical instruments) having 64-ms long gaps. The magnitude DNN outperformed the complex-valued DNN in terms of signal-to-noise ratios and objective difference grades. Although, for instruments, a reference inpainting obtained through linear predictive coding performed better in both metrics, it performed worse than the magnitude DNN for music. This demonstrates the potential of the magnitude DNN, in particular for inpainting signals that are more complex than single instrument sounds.