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Informed Spatial Filtering Based on Constrained Independent Component Analysis

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Audio Source Separation

Part of the book series: Signals and Communication Technology ((SCT))

Abstract

In this work, we present a linearly constrained signal extraction algorithm which is based on a Minimum Mutual Information (MMI) criterion that allows to exploit the three fundamental properties of speech and audio signals: Nonstationarity, Nonwhiteness, and Nongaussianity. Hence, the proposed method is very well suited for signal processing of nonstationary nongaussian broadband signals like speech. Furthermore, from the linearly constrained MMI approach, we derive an efficient realization in a (GSC) structure. To estimate the relative transfer functions between the microphones, which are needed for the set of linear constraints, we use an informed time-domain independent component analysis algorithm, which exploits some coarse direction-of-arrival information of the target source. As a decisive advantage, this simplifies the otherwise challenging control mechanism for simultaneous adaptation of the GSC’s blocking matrix und interference and noise canceler coefficients. Finally, we establish relations between the proposed method and other well-known multichannel linear filter approaches for signal extraction based on second-order-statistics, and demonstrate the effectiveness of the proposed signal extraction method in a multispeaker scenario.

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Notes

  1. 1.

    The approach of minimizing the output power in the presence of linear constraints was originally presented by Frost in [14] for use with antenna arrays, assuming free-field propagation.

  2. 2.

    Note that we chose this reverberation time in order to demonstrate the advantage of the HOS-based realization over the SOS-based realization of the MMI-based GSC.

Abbreviations

ICA:

Independent Component Analysis

BSS:

Blind Source Separation

MWF:

Multichannel Wiener Filter

LCMV:

Linearly Constrained Minimum Variance

DOA:

Direction of Arrival

RTF:

Relative Transfer Functions

SOS:

Second Order Statistics

MVDR:

Minimum Variance Distortionless Response

FIR:

Finite Impulse Response

AIR:

Acoustic Impulse Response

GSC:

Generalized Sidelobe Canceler

MMI:

Minimum Mutual Information

STFT:

Short-Time Fourier Transform

VAD:

Voice Activity Detection

SPP:

Speech Presence Probability

TRINICON:

TRIple-N Independent component analysis for CONvolutive mixtures

SE:

Signal Extraction

NRE:

Normalized RTF Estimation Error

SIR:

Signal-to-Interference Ratio

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Authors and Affiliations

  1. Chair of Multimedia Communications and Signal Processing, Friedrich-Alexander University Erlangen-Nürnberg, Cauerstrasse 7, 91058, Erlangen, Germany

    Hendrik Barfuss, Klaus Reindl & Walter Kellermann

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  1. Hendrik Barfuss
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  2. Klaus Reindl
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  3. Walter Kellermann
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Correspondence to Hendrik Barfuss .

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  1. University of Tsukuba, Ibaraki, Japan

    Shoji Makino

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Barfuss, H., Reindl, K., Kellermann, W. (2018). Informed Spatial Filtering Based on Constrained Independent Component Analysis. In: Makino, S. (eds) Audio Source Separation. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-73031-8_10

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