Novel GCC-PHAT model in diffuse sound field for microphone array pairwise distance based calibration

Velasco, Jose; Taghizadeh, Mohammad Javad; Asaei, Afsaneh; Bourlard, Hervé; Martín-Arguedas, Carlos J.; Macías-Guarasa, Javier; Pizarro, Daniel

doi:10.1109/icassp.2015.7178455

Cited by 19 publications

(8 citation statements)

References 22 publications

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“…TDOA estimate is obtained as the peak position in the IDFT of the normalized cross spectrum. TDOA estimation by GCC-PHAT works with low computational cost, archives good estimation accuracy under low noise environments, even with high reverberation [13], [14]. However, estimation accuracy degrades when SNR becomes lower.…”

Section: Conventional Methodsmentioning

confidence: 99%

TDOA estimation based on phase-voting cross correlation and circular standard deviation

Kato

Senda

Kondo

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-This paper proposes a new TDOA estimation based on phase-voting cross correlation and circular standard deviation. Based on phase delay and kernel function, the proposed method generates a probability density function (PDF) of TDOA for each frequency bin. TDOA estimate is determined by voting the PDFs generated for all frequency bins. Peak positions of the bin-wise PDFs for the target signal are concentrated only at the target time difference because peak positions for the noise totally differ among bins and periodicity of peaks depends on frequency. Therefore, by voting the PDFs for all frequency bins, the peak position for the target can be easily identified. The kernel width of PDF is determined by circular standard deviation of cross spectral phase for each frequency bin. This width control enhances peaks of PDFs for high SNR frequency bins since phases for high SNR bins are more stable than those for low ones. Evaluation with ship and drone sounds shows that the RMSE of TDOA estimation by the proposed method reaches 0.37 times that by GCC-PHAT.

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Section: Conventional Methodsmentioning

confidence: 99%

TDOA estimation based on phase-voting cross correlation and circular standard deviation

Kato

Senda

Kondo

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…The first one (sec. IV A) relies on a low-rank and sparse decomposition of noisy TDOA observations (Velasco et al, 2016). Then the low-rank part is the denoised data, which can be used with any TDOA calibration method from the state of the art.…”

Section: B Our Contributionmentioning

confidence: 99%

“…For the -th measured subset { } the noise model from equation 16is rewritten in the matrix form: 17leads to a structured decomposition (Velasco et al, 2016). Indeed, is a sparse matrix and is such that rank( ) = 2.…”

Section: Outlier-aware Tdoa Geometric Calibrationmentioning

confidence: 99%

Geometric calibration of very large microphone arrays in mismatched free field

Vanwynsberghe

Challande

Ollivier

et al. 2019

The Journal of the Acoustical Society of America

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Large microphone arrays are an efficient means for source localization thanks to a wide aperture and a great number of sensors. When such arrays are deployed in situ, accurate geometric calibration becomes essential to obtain the microphone positions. In free field, the classic procedures rely on measured Times Of Arrival (TOA) or Time Differences of Arrival (TDOA) between the microphones and several controlled sources. However free field model mismatches, such as reflectors, generate outliers which severely deteriorate the positioning accuracy. This paper introduces a unified framework for robust calibration using TOA or TDOA, by exploiting an outlier-aware noise model. Thanks to the largeness of the array, the existing outliers are sparse and can be identified by a Lasso regression. From this, three iterative robust solvers a are proposed: (i) for TOA by Robust Multi Dimensional Unfolding, a particular variation of Robust Multi Dimensional Scaling (ii) for TDOA by data predenoising based on sparse and low-rank matrix decomposition, and (iii) for TDOA by jointly identifying the outliers and the geometry. The relevance of outlier-aware approaches is asserted by numerical and experimental tests. Compared with the baseline least-square approaches, the proposed robust solvers significantly improve the positioning accuracy in a free field mismatched by reflectors.

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“…Delays between sound channels are calculated by GCC algorithms [25]. Two different types of GCC algorithms which are basic GCC and Generalized Cross Correlation with Phase Transform (GCC-PHAT) are used in this paper [26][27]. The GCC of the signals recorded by two microphones is given by…”

Section: Amplitude (V)mentioning

confidence: 99%

“…The moving average filter is shown in (26). The parameters including [ ], [ ], and refer to the input signal, the output signal, and the number of points used in the moving average filter, respectively [28].…”

Section: Sound Delay Estimationmentioning

confidence: 99%

Estimation of Trajectory and Location for Mobile Sound Source

Çatalbaş¹,

Yıldırım²,

Gülten³

et al. 2016

ijacsa

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Abstract-In this paper, we present an approach to estimate mobile sound source trajectory. An artificially created sound source signal is used in this work. The main aim of this paper is to estimate the mobile object trajectory via sound processing methods. The performance of generalized cross correlation techniques is compared with that of noise reduction filters for the success of trajectory estimation. The azimuth angle between the sound source and receiver is calculated during the whole movement. The parameter of Interaural Time Difference (ITD) is utilized for determining azimuth angle. The success of estimated delay is compared with different types of Generalized Cross Correlation (GCC) algorithms. In this study, an approach for sound localization and trajectory estimation on 2D space is proposed. Besides, different types of pre-filter method are tried for removing the noise and signal smoothing of recorded sound signals. Some basic parameters of sound localization process are also explained. Moreover, the calculation error of average azimuth angle is compared with different GCC and pre-filtered methods. To conclude, it is observed that estimation of location and trajectory information of a mobile object from a stereo sound recording is realized successfully.

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Novel GCC-PHAT model in diffuse sound field for microphone array pairwise distance based calibration

Cited by 19 publications

References 22 publications

TDOA estimation based on phase-voting cross correlation and circular standard deviation

TDOA estimation based on phase-voting cross correlation and circular standard deviation

Geometric calibration of very large microphone arrays in mismatched free field

Estimation of Trajectory and Location for Mobile Sound Source

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