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.

0 Followers
 · 
57 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: We present a method for calibrating a distributed microphone array using time-of-arrival (TOA) measurements. The calibration encompasses localization and gain equalization of the microphones, which are both important in applications such as beamforming. The availability of accurate TOA measurements between the microphones and a set of spatially distributed acoustic events is pivotal to the calibration task. We propose to use a moving acoustic source emitting a calibration signal at known intervals. We then show that the TOAs and the observed signals can be used to estimate the gain differences between microphones in addition to the more established microphone localization. Finally, we provide experimental results with simulated and real measured data to demonstrate that our approach facilitates accurate TOA measurements and hence, accurate localization and gain equalization, even in reverberant and noisy conditions.
    ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: A time delay estimation method based on wavelet transform and speech envelope is proposed for distributed microphone arrays. This method first extracts the speech envelopes of the signals processed with multi-level discrete wavelet transform, and then makes use of the speech envelopes to estimate a coarse time delay. Finally it searches for the accurate time delay near the coarse time delay by the cross-correlation function calculated in time domain. The simulation results illustrate that the proposed method can accurately estimate the time delay between two distributed microphone array signals.
    Advances in Electrical and Computer Engineering 01/2013; 13(3):39-44. DOI:10.4316/aece.2013.03007 · 0.64 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we propose a novel method for the blind compensation of drift for the asynchronous recording of an ad hoc microphone array. Digital signals simultaneously observed by different recording devices have drift of the time differences between the observation channels because of the sampling frequency mismatch among the devices. On the basis of a model in which the time difference is constant within each short time frame but varies in proportion to the central time of the frame, the effect of the sampling frequency mismatch can be compensated in the short-time Fourier transform (STFT) domain by a linear phase shift. By assuming that the sources are motionless and have stationary amplitudes, the observation is regarded as being stationary when drift does not occur. Thus, we formulate a likelihood to evaluate the stationarity in the STFT domain to evaluate the compensation of drift. The maximum likelihood estimation is obtained effectively by a golden section search. Using the estimated parameters, we compensate the drift by STFT analysis with a noninteger frame shift. The effectiveness of the proposed blind drift compensation method is evaluated in an experiment in which artificial drift is generated.
    Signal Processing 01/2014; 107. DOI:10.1016/j.sigpro.2014.09.015 · 2.24 Impact Factor

Full-text (2 Sources)

Download
25 Downloads
Available from
May 22, 2014