Evaluating real-time audio localization algorithms for artificial audition in robotics

Badali, Anthony P.; Valin, Jean‐Marc; Michaud, François; Aarabi, Parham

doi:10.1109/iros.2009.5354308

Cited by 41 publications

(21 citation statements)

References 10 publications

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“…The PD-Tree method outperforms a simple linear model. Moreover, the overall averages are very similar to results reported by traditional coordinate based methods, meaning that coordinate-free methods need not sacrifice accuracy (Badali et al, 2009). …”

Section: Experiment: Grid Datasetsupporting

confidence: 73%

Camera Pointing with Coordinate-Free Localization and Tracking

Ettinger¹,

Freund²

2011

Advances in Sound Localization

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where c is the speed of sound in the medium. ∆ ij is often called the time delay of arrival (TDOA) between microphone i and j. It is worth noting that if f is the sampling rate being used, then the largest the TDOA can be in terms of audio samples is M = m i − m j 2 f /c. In other words, ∆ ij is always in the range [−M, M] and in practice can only be estimated to the nearest sample. This observation directly reveals the fact that close together microphones cannot have as wide a range of TDOAs as microphones that are spaced further apart. Placing microphones further 318 Advances in Sound Localization

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Section: Experiment: Grid Datasetsupporting

confidence: 73%

Camera Pointing with Coordinate-Free Localization and Tracking

Ettinger¹,

Freund²

2011

Advances in Sound Localization

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“…In the two-dimensional case (using multiple microphones), it is the steered response power (SRP) -it corresponds to 2D-NCC. For further details on these methods, we refer the reader to [21], [22], [23]. In all experiments, we use the same size of the Hamming window in short-time Fourier transform as the size of the temporal window in NCC correlation (6), i.e.…”

Section: Methodsmentioning

confidence: 99%

Active-speaker detection and localization with microphones and cameras embedded into a robotic head

Čech

Mittal

Deleforge

et al. 2013

2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

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Abstract-In this paper we present a method for detecting and localizing an active speaker, i.e., a speaker that emits a sound, through the fusion between visual reconstruction with a stereoscopic camera pair and sound-source localization with several microphones. Both the cameras and the microphones are embedded into the head of a humanoid robot. The proposed statistical fusion model associates 3D faces of potential speakers with 2D sound directions. The paper has two contributions: (i) a method that discretizes the two-dimensional space of all possible sound directions and that accumulates evidence for each direction by estimating the time difference of arrival (TDOA) over all the microphone pairs, such that all the microphones are used simultaneously and symmetrically and (ii) an audio-visual alignment method that maps 3D visual features onto 2D sound directions and onto TDOAs between microphone pairs. This allows to implicitly represent both sensing modalities into a common audiovisual coordinate frame. Using simulated as well as real data, we quantitatively assess the robustness of the method against noise and reverberations, and we compare it with several other methods. Finally, we describe a realtime implementation using the proposed technique and with a humanoid head embedding four microphones and two cameras: this enables natural human-robot interactive behavior.

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“…One can cite for instance [80], or [81] where the PhaT processor is exploited on a 24 evenly spaced microphones array fitted on the 3.2 m-long walls of a room which is visited by a tour-guide robot. More recent use of the PhaT approach can be cited: [82] where a triangular 3-microphone array is used to infer source location from short time observations so as to cope with the movement of the sound source or the robot; [83] presents an evaluation of various real-time sound localization approaches from a cubical 8-microphone array in which GCC-PhaT is compared with beamforming techniques; [84] proposes a robust approach to the acoustic perception of the presence of people from a pair of microphones. But GCC-PhaT only takes into account the phase of the perceived signals in the intercorrelation computation, giving the same importance to each frequency.…”

Section: Musicmentioning

confidence: 99%

A survey on sound source localization in robotics: From binaural to array processing methods

Argentieri

Danès

Souères

2015

Computer Speech & Language

136

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This paper attempts to provide a state-of-the-art of sound source localization in Robotics. Noticeably, this context raises original constraints-e.g. embeddability, real time, broadband environments, noise and reverberation-which are seldom simultaneously taken into account in Acoustics or Signal Processing. A comprehensive review is proposed of recent robotics achievements, be they binaural or rooted in Array Processing techniques. The connections are highlighted with the underlying theory as well as with elements of physiology and neurology of human hearing.

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Evaluating real-time audio localization algorithms for artificial audition in robotics

Cited by 41 publications

References 10 publications

Camera Pointing with Coordinate-Free Localization and Tracking

Camera Pointing with Coordinate-Free Localization and Tracking

Active-speaker detection and localization with microphones and cameras embedded into a robotic head

A survey on sound source localization in robotics: From binaural to array processing methods

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