Conference Paper

Blind alignment of asynchronously recorded signals for distributed microphone array

Grad. Sch. of Inf. Sci. & Technol., Univ. of Tokyo, Tokyo, Japan
DOI: 10.1109/ASPAA.2009.5346505 Conference: Applications of Signal Processing to Audio and Acoustics, 2009. WASPAA '09. IEEE Workshop on
Source: IEEE Xplore

ABSTRACT In this paper, aiming to utilize independent recording devices as a distributed microphone array, we present a novel method for alignment of recorded signals with localizing microphones and sources. Unlike conventional microphone array, signals recorded by independent devices have different origins of time, and microphone positions are generally unknown. In order to estimate both of them from only recorded signals, time differences between channels for each source are detected, which still include the differences of time origins, and an objective function defined by their square errors is minimized. For that, simple iterative update rules are derived through auxiliary function approach. The validity of our approach is evaluated by simulative experiment.

Download full-text


Available from: Shigeki Sagayama, Sep 26, 2015
21 Reads
  • Source
    • "2.3. Determinedness As pointed out in [8] "
    Conference Paper: Distributed acoustic SLAM
    [Show abstract] [Hide abstract]
    ABSTRACT: Vision-based methods are very popular for simultaneous localization and environment mapping (SLAM). One can imagine that exploiting the natural acoustic landscape of the robot’s environment can prove to be a useful alternative to vision SLAM. Visual SLAM depends on matching local features between images, whereas distributed acoustic SLAM is based on matching acoustic events. Proposed DASLAM is based on distributed microphone arrays, where each microphone is connected to a separate, moving, controllable recording device, which requires compensation for their different clock shifts. We show that this controlled mobility is necessary to deal with underdetermined cases. Estimation is done using particle filtering. Results show that both tasks can be accomplished with good precision, even for the theoretically underdetermined cases. For example, we were able to achieve mapping error as low as 17.53 cm for sound sources with localization error of 18.61 cm and clock synchronization error of 42 μs for 2 robots and 2 sources.
    EUSIPCO 2015; 08/2015
  • Source
    • "Several solutions to localization based on the TDOA have been proposed. Some are iterative methods based on leastsquares criteria [4] [9] [10] [11] [12] or a maximum likelihood principle [3] [13] [14] [15], and some are non-iterative methods [16] [17]. Generally, since the cost functions used in the iterative methods are nonlinear and nonconvex, they can be easily trapped at local minima. "
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we present a new method to find solutions to the time difference of arrival (TDOA)-based source and sensor localization problem. This paper is a continuation of [1], in which sources and sensors are localized on the basis of time of arrival (TOA) measurements. Generally, the TOA is known if the TDOA and reference-distances with the sound velocity are given, where the reference-distances are defined as the distances from the first (reference) sensor to the sources. We show that when the numbers of sources and sensors are at least six and eight, respectively, the reference-distances can be computed directly from TDOA measurements. This means that in such cases, the positions of the sources and sensors can be directly estimated in closed-form solutions, except for one reference-distance, which is estimated by a grid search. The validity of our algorithm is evaluated by synthetic experiments in noise-free and noisy cases.
    40th International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia; 04/2015
  • Source
    • "Several solutions to solve the sound localization based on TOA or Time-Difference-of-Arrival (TDOA) have been proposed . Some are iterative methods based on a least square criteria [1] [2] [3] [4] [5] or a maximum likelihood principle [6] [7] [8] [9], and some are non-iterative methods [10] [11]. Generally, since cost functions used in the iterative methods are nonlinear and non-convex, they can be easily trapped into local minima . "
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents numerical formulae for the Time-of-Arrival (TOA)-based microphone and source localization problem, which determines the positions of microphones and sources based on distances between each microphone and each source respectively. This is a purely geometrical problem in mathematics. Concretely, we show when the number of microphones or the number of sources is at least nine, the formulae of the microphone positions and source positions are given simply from the distance-matrix. A similar statement is given in the cases of at least eight microphones or sources if we know an extra information about the distance between any two microphones or two sources. The accuracy of these formulae are proven shortly by ten thousand independent experiments of which coordinates of points have an independent Uniform distribution.
    14th International Workshop on Acoustic Signal Enhancement (IWAENC); 11/2014
Show more