701 research outputs found
Sound source localization through shape reconfiguration in a snake robot
This paper describes a snake robot system that uses sound source localization. We show in this paper as to how we can localize a sound source in 3D and solve the classic forward backward problem in sound source localization using minimum number of audio sensors by using the multiple degrees of freedom of the snake robot. We describe the hardware and software architecture of the robot and show the results of several sound tracking experiments we did with our snake robot. We also present biologically inspired sound tracking behavior in different postures of a biological snake robot as "Digital Snake Charming"
Acoustic Space Learning for Sound Source Separation and Localization on Binaural Manifolds
In this paper we address the problems of modeling the acoustic space
generated by a full-spectrum sound source and of using the learned model for
the localization and separation of multiple sources that simultaneously emit
sparse-spectrum sounds. We lay theoretical and methodological grounds in order
to introduce the binaural manifold paradigm. We perform an in-depth study of
the latent low-dimensional structure of the high-dimensional interaural
spectral data, based on a corpus recorded with a human-like audiomotor robot
head. A non-linear dimensionality reduction technique is used to show that
these data lie on a two-dimensional (2D) smooth manifold parameterized by the
motor states of the listener, or equivalently, the sound source directions. We
propose a probabilistic piecewise affine mapping model (PPAM) specifically
designed to deal with high-dimensional data exhibiting an intrinsic piecewise
linear structure. We derive a closed-form expectation-maximization (EM)
procedure for estimating the model parameters, followed by Bayes inversion for
obtaining the full posterior density function of a sound source direction. We
extend this solution to deal with missing data and redundancy in real world
spectrograms, and hence for 2D localization of natural sound sources such as
speech. We further generalize the model to the challenging case of multiple
sound sources and we propose a variational EM framework. The associated
algorithm, referred to as variational EM for source separation and localization
(VESSL) yields a Bayesian estimation of the 2D locations and time-frequency
masks of all the sources. Comparisons of the proposed approach with several
existing methods reveal that the combination of acoustic-space learning with
Bayesian inference enables our method to outperform state-of-the-art methods.Comment: 19 pages, 9 figures, 3 table
Sound Based Positioning
With a growing interest in non-GPS positioning, navigation, and timing (PNT), sound based positioning provides a precise way to locate both sound sources and microphones through audible signals of opportunity (SoOPs). Exploiting SoOPs allows for passive location estimation. But, attributing each signal to a specific source location when signals are simultaneously emitting proves problematic. Using an array of microphones, unique SoOPs are identified and located through steered response beamforming. Sound source signals are then isolated through time-frequency masking to provide clear reference stations by which to estimate the location of a separate microphone through time difference of arrival measurements. Results are shown for real data
ベイズ法によるマイクロフォンアレイ処理
京都大学0048新制・課程博士博士(情報学)甲第18412号情博第527号新制||情||93(附属図書館)31270京都大学大学院情報学研究科知能情報学専攻(主査)教授 奥乃 博, 教授 河原 達也, 准教授 CUTURI CAMETO Marco, 講師 吉井 和佳学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA
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