Gaussian process models for HRTF based 3D sound localization

Luo, Yuhua; Zotkin, Dmitry N.; Duraiswami, Ramani

doi:10.1109/icassp.2014.6854122

Cited by 10 publications

(6 citation statements)

References 27 publications

(39 reference statements)

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“…To overcome the need of a complex explicit sound propagation model, a number of supervised approaches to SSL have been recently proposed. These methods use either artificial neural networks [2], manifold learning [8], [9] or regression [5], [9], [19], [22], [10], first to learn a mapping from binaural features to the (1D or 2D) direction of a single source, and second to infer an unknown source direction from binaural observations. These methods have the advantage that an explicit HRTF model is replaced by an implicit one, embedded in the parameters learned during the training stage.…”

Section: A Related Work and Contributionsmentioning

confidence: 99%

“…For a single spatially-narrow emitter, the ILD and IPD depend on the emitter's position relative to the head, namely the 2D directional vector formed by azimuth and elevation. Binaural features have hence been used for single sound source localization (single-SSL), e.g., [2], [3], [4], [5], [6], [7], [8], [9], [10], [11]. Matters are more complex when multiple sound sources, emitting from different directions, are simultaneously active.…”

Section: Introductionmentioning

confidence: 99%

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Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression

Deleforge

Horaud

Schechner

et al. 2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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International audienceThis paper addresses the problem of localizing audio sources using binaural measurements. We propose a supervised formulation that simultaneously localizes multiple sources at different locations. The approach is intrinsically efficient because, contrary to prior work, it relies neither on source separation, nor on monaural segregation. The method starts with a training stage that establishes a locally-linear Gaussian regression model between the directional coordinates of all the sources and the auditory features extracted from binaural measurements. While fixed-length wide-spectrum sounds (white noise) are used for training to reliably estimate the model parameters, we show that the testing (localization) can be extended to variable-length sparse-spectrum sounds (such as speech), thus enabling a wide range of realistic applications. Indeed, we demonstrate that the method can be used for audio-visual fusion, namely to map speech signals onto images and hence to spatially align the audio and visual modalities, thus enabling to discriminate between speaking and non-speaking faces. We release a novel corpus of real-room recordings that allow quantitative evaluation of the co-localization method in the presence of one or two sound sources. Experiments demonstrate increased accuracy and speed relative to several state-of-the-art methods

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Section: A Related Work and Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression

Deleforge

Horaud

Schechner

et al. 2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…The magnitude ratio (MR) is a bounded form of the ILD [8]. As the ILD can result in extreme values, the MR is used in its place.…”

Section: Proposed Methodsmentioning

confidence: 99%

Robust Full-sphere Binaural Sound Source Localization Using Interaural and Spectral Cues

Hammond

Jackson

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A binaural sound source localization method is proposed that uses interaural and spectral cues for localization of sound sources with any direction of arrival on the full-sphere. The method is designed to be robust to the presence of reverberation, additive noise and different types of sounds. The method uses the interaural phase difference (IPD) for lateral angle localization, then interaural and spectral cues for polar angle localization. The method applies different weighting to the interaural and spectral cues depending on the estimated lateral angle. In particular, only the spectral cues are used for sound sources near or on the median plane.

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“…is taken to eliminate background noise. One of the current state-of-the-art binaural sound localisation framework is called Head-Related Transfer Function (HRTF) [105], [106]. The idea is to recover how human ears receive and perceive sound, and treat the human way of localising sound as a transfer function.…”

Section: Maritime Object Localisation By Soundmentioning

confidence: 99%

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Thombre

Zhao

Ramm-Schmidt³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

114

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Autonomous ships are expected to improve the level of safety and efficiency in future maritime navigation. Such vessels need perception for two purposes: to perform autonomous situational awareness and to monitor the integrity of the sensor system itself. In order to meet these needs, the perception system must fuse data from novel and traditional perception sensors using Artificial Intelligence (AI) techniques. This article overviews the recognized operational requirements that are imposed on regular and autonomous seafaring vessels, and then proceeds to consider suitable sensors and relevant AI techniques for an operational sensor system. The integration of four sensors families is considered: sensors for precise absolute positioning (Global Navigation Satellite System (GNSS) receivers and Inertial Measurement Unit (IMU)), visual sensors (monocular and stereo cameras), audio sensors (microphones), and sensors for remotesensing (RADAR and LiDAR). Additionally, sources of auxiliary data, such as Automatic Identification System (AIS) and external data archives are discussed. The perception tasks are related to well-defined problems, such as situational abnormality detection, vessel classification, and localization, that are solvable using AI techniques. Machine learning methods, such as deep learning and Gaussian processes, are identified to be especially relevant for these problems. The different sensors and AI techniques are characterized keeping in view the operational requirements, and some example state-of-the-art options are compared based on accuracy, complexity, required resources, compatibility and adaptability to maritime environment, and especially towards practical realization of autonomous systems.

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Gaussian process models for HRTF based 3D sound localization

Cited by 10 publications

References 27 publications

Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression

Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression

Robust Full-sphere Binaural Sound Source Localization Using Interaural and Spectral Cues

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

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