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Generalized Mutual Interdependence Analysis of Noisy Channels

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Excursions in Harmonic Analysis, Volume 1

Part of the book series: Applied and Numerical Harmonic Analysis ((ANHA))

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Abstract

The main motivation for our present work is to reliably perform voice (or signal) detection for a source of interest from a single microphone recording. We rely on the assumption that the input signal contains invariant information about the channel, or transfer function from each source to the microphone, which could be reliably exploited for signal detection and classification. In this chapter we employ a nonconventional method called generalized mutual interdependence analysis (GMIA) that proposes a model for the computation of this hidden invariant information present across multiple measurements. Such information turns out to be a good characteristic feature of a signal source, transformation, or composition that fits the model. This chapter introduces a unitary and succinct description of the underlying model of GMIA, and the formulation and solution of the corresponding optimization problem. We apply GMIA for feature extraction in the problem of own-voice activity detection, which aims at classification of a near-field channel based on access to prior information about GMIA features of the channel. It is extremely challenging to recognize the presence of voice in noisy scenarios with interference from music, car noise, or street noise. We compare GMIA with MFCC and cepstral-mean features. For example, GMIA performs with equal error rates below 10 % for music interference of SNRs down to − 20 dB.

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Notes

  1. 1.

    The instance i implicitly represents the timescale of interest, e.g., a timescale of the order of the pitch period (10–20 ms) or of the order of the average word period (500 ms).

  2. 2.

    The spectrum of the excitation changes slowly for voiced sounds and appears unchanged although radically different over the duration of a consonant, at the phonetic timescale.

  3. 3.

    A detailed analysis of these components of the speech production model is beyond present scope.

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Authors and Affiliations

  1. Siemens Corporation, Corporate Research, 755 College Road East, Princeton, NJ, 08540, USA

    Heiko Claussen & Justinian Rosca

  2. Siemens Corporate Research and Technologies-India, Bangalore, India

    Viswanathan Ramasubramanian & Subramani Thiyagarajan

Authors
  1. Heiko Claussen
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  2. Justinian Rosca
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  3. Viswanathan Ramasubramanian
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  4. Subramani Thiyagarajan
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Corresponding author

Correspondence to Justinian Rosca .

Editor information

Editors and Affiliations

  1. , Department of Mathematics, University of Maryland, Norbert Wiener Center, College Park, 20742-0001, Maryland, USA

    Travis D. Andrews

  2. Norbert Wiener Center, Department of Mathematics, University of Maryland, College Park, 20742, Maryland, USA

    Radu Balan

  3. Department of Mathematics, University of Maryland, College Park, 20742-4015, Maryland, USA

    John J. Benedetto

  4. Department of Mathematics, University of Maryland, College Park, 20742-4015, Maryland, USA

    Wojciech Czaja

  5. , Department of Mathematics, University of Maryland, College Park, 20742, Maryland, USA

    Kasso A. Okoudjou

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Claussen, H., Rosca, J., Ramasubramanian, V., Thiyagarajan, S. (2013). Generalized Mutual Interdependence Analysis of Noisy Channels. In: Andrews, T., Balan, R., Benedetto, J., Czaja, W., Okoudjou, K. (eds) Excursions in Harmonic Analysis, Volume 1. Applied and Numerical Harmonic Analysis. Birkhäuser, Boston. https://doi.org/10.1007/978-0-8176-8376-4_18

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