Binaural Cues for Distance and Direction of Nearby Sound Sources

To a first-order approximation, binaural localization cues are ambiguous: a number of source locations give rise to nearly the same interaural differences. For sources more than a meter from the listener, binaural localization cues are approximately equal for any source on a cone centered on the interaural axis (i.e., the well-known "cones of confusion"). The current paper analyzes simple geometric approximations of a listener's head to gain insight into localization performance for sources near the listener. In particular, if the head is treated as a rigid, perfect sphere, interaural intensity differences (IIDs) can be broken down into two main components. One component is constant along the cone of confusion (and thus co varies with the interaural time difference, or ITD). The other component is roughly constant for a sphere centered on the interaural axis and depends only on the relative pathlengths from the source to the two ears. This second factor is only large enough to be perceptible when sources are within one or two meters of the listener. These results are not dramatically different if one assumes that the ears are separated by 160 degrees along the surface of the sphere (rather than diametrically opposite one another). Thus, for sources within a meter of the listener, binaural information should allow listeners to locate sources within a volume around a circle centered on the interaural axis, on a "doughnut of confusion." The volume of the doughnut of confusion increases dramatically with angle between source and the interaural axis, degenerating to the entire median plane in the limit.Air Force Office of Scientific Research (F49620-98-1-0108

A Geometric Approach to Sound Source Localization from Time-Delay Estimates

This paper addresses the problem of sound-source localization from time-delay estimates using arbitrarily-shaped non-coplanar microphone arrays. A novel geometric formulation is proposed, together with a thorough algebraic analysis and a global optimization solver. The proposed model is thoroughly described and evaluated. The geometric analysis, stemming from the direct acoustic propagation model, leads to necessary and sufficient conditions for a set of time delays to correspond to a unique position in the source space. Such sets of time delays are referred to as feasible sets. We formally prove that every feasible set corresponds to exactly one position in the source space, whose value can be recovered using a closed-form localization mapping. Therefore we seek for the optimal feasible set of time delays given, as input, the received microphone signals. This time delay estimation problem is naturally cast into a programming task, constrained by the feasibility conditions derived from the geometric analysis. A global branch-and-bound optimization technique is proposed to solve the problem at hand, hence estimating the best set of feasible time delays and, subsequently, localizing the sound source. Extensive experiments with both simulated and real data are reported; we compare our methodology to four state-of-the-art techniques. This comparison clearly shows that the proposed method combined with the branch-and-bound algorithm outperforms existing methods. These in-depth geometric understanding, practical algorithms, and encouraging results, open several opportunities for future work.Comment: 13 pages, 2 figures, 3 table, journa

Improving elevation perception with a tool for image-guided head-related transfer function selection

This paper proposes an image-guided HRTF selection procedure that exploits the relation between features of the pinna shape and HRTF notches. Using a 2D image of a subject's pinna, the procedure selects from a database the HRTF set that best fits the anthropometry of that subject. The proposed procedure is designed to be quickly applied and easy to use for a user without previous knowledge on binaural audio technologies. The entire process is evaluated by means of an auditory model for sound localization in the mid-sagittal plane available from previous literature. Using virtual subjects from a HRTF database, a virtual experiment is implemented to assess the vertical localization performance of the database subjects when they are provided with HRTF sets selected by the proposed procedure. Results report a statistically significant improvement in predictions of localization performance for selected HRTFs compared to KEMAR HRTF which is a commercial standard in many binaural audio solutions; moreover, the proposed analysis provides useful indications to refine the perceptually-motivated metrics that guides the selection

Surround by Sound: A Review of Spatial Audio Recording and Reproduction

In this article, a systematic overview of various recording and reproduction techniques for spatial audio is presented. While binaural recording and rendering is designed to resemble the human two-ear auditory system and reproduce sounds specifically for a listener’s two ears, soundfield recording and reproduction using a large number of microphones and loudspeakers replicate an acoustic scene within a region. These two fundamentally different types of techniques are discussed in the paper. A recent popular area, multi-zone reproduction, is also briefly reviewed in the paper. The paper is concluded with a discussion of the current state of the field and open problemsThe authors acknowledge National Natural Science Foundation of China (NSFC) No. 61671380 and Australian Research Council Discovery Scheme DE 150100363

Proceedings of the EAA Spatial Audio Signal Processing symposium: SASP 2019

International audienc

Evidence for cue-independent spatial representation in the human auditory cortex during active listening

Few auditory functions are as important or as universal as the capacity for auditory spatial awareness (e.g., sound localization). That ability relies on sensitivity to acoustical cues-particularly interaural time and level differences (ITD and ILD)-that correlate with sound-source locations. Under nonspatial listening conditions, cortical sensitivity to ITD and ILD takes the form of broad contralaterally dominated response functions. It is unknown, however, whether that sensitivity reflects representations of the specific physical cues or a higher-order representation of auditory space (i.e., integrated cue processing), nor is it known whether responses to spatial cues are modulated by active spatial listening. To investigate, sensitivity to parametrically varied ITD or ILD cues was measured using fMRI during spatial and nonspatial listening tasks. Task type varied across blocks where targets were presented in one of three dimensions: auditory location, pitch, or visual brightness. Task effects were localized primarily to lateral posterior superior temporal gyrus (pSTG) and modulated binaural-cue response functions differently in the two hemispheres. Active spatial listening (location tasks) enhanced both contralateral and ipsilateral responses in the right hemisphere but maintained or enhanced contralateral dominance in the left hemisphere. Two observations suggest integrated processing of ITD and ILD. First, overlapping regions in medial pSTG exhibited significant sensitivity to both cues. Second, successful classification of multi-voxel patterns was observed for both cue types and-critically-for cross-cue classification. Together, these results suggest a higher-order representation of auditory space in the human auditory cortex that at least partly integrates the specific underlying cues.Peer reviewe

Sound Event Localization, Detection, and Tracking by Deep Neural Networks

In this thesis, we present novel sound representations and classification methods for the task of sound event localization, detection, and tracking (SELDT). The human auditory system has evolved to localize multiple sound events, recognize and further track their motion individually in an acoustic environment. This ability of humans makes them context-aware and enables them to interact with their surroundings naturally. Developing similar methods for machines will provide an automatic description of social and human activities around them and enable machines to be context-aware similar to humans. Such methods can be employed to assist the hearing impaired to visualize sounds, for robot navigation, and to monitor biodiversity, the home, and cities. A real-life acoustic scene is complex in nature, with multiple sound events that are temporally and spatially overlapping, including stationary and moving events with varying angular velocities. Additionally, each individual sound event class, for example, a car horn can have a lot of variabilities, i.e., different cars have different horns, and within the same model of the car, the duration and the temporal structure of the horn sound is driver dependent. Performing SELDT in such overlapping and dynamic sound scenes while being robust is challenging for machines. Hence we propose to investigate the SELDT task in this thesis and use a data-driven approach using deep neural networks (DNNs). The sound event detection (SED) task requires the detection of onset and offset time for individual sound events and their corresponding labels. In this regard, we propose to use spatial and perceptual features extracted from multichannel audio for SED using two different DNNs, recurrent neural networks (RNNs) and convolutional recurrent neural networks (CRNNs). We show that using multichannel audio features improves the SED performance for overlapping sound events in comparison to traditional single-channel audio features. The proposed novel features and methods produced state-of-the-art performance for the real-life SED task and won the IEEE AASP DCASE challenge consecutively in 2016 and 2017. Sound event localization is the task of spatially locating the position of individual sound events. Traditionally, this has been approached using parametric methods. In this thesis, we propose a CRNN for detecting the azimuth and elevation angles of multiple temporally overlapping sound events. This is the first DNN-based method performing localization in complete azimuth and elevation space. In comparison to parametric methods which require the information of the number of active sources, the proposed method learns this information directly from the input data and estimates their respective spatial locations. Further, the proposed CRNN is shown to be more robust than parametric methods in reverberant scenarios. Finally, the detection and localization tasks are performed jointly using a CRNN. This method additionally tracks the spatial location with time, thus producing the SELDT results. This is the first DNN-based SELDT method and is shown to perform equally with stand-alone baselines for SED, localization, and tracking. The proposed SELDT method is evaluated on nine datasets that represent anechoic and reverberant sound scenes, stationary and moving sources with varying velocities, a different number of overlapping sound events and different microphone array formats. The results show that the SELDT method can track multiple overlapping sound events that are both spatially stationary and moving