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Foto: Universität Paderborn, Jörg Ullmann Bildinformationen anzeigen
Foto: Universität Paderborn, Jörg Ullmann Bildinformationen anzeigen
Foto: Universität Paderborn, Jörg Ullmann Bildinformationen anzeigen

Foto: Universität Paderborn, Jörg Ullmann

Foto: Universität Paderborn, Jörg Ullmann

Foto: Universität Paderborn, Jörg Ullmann

Source Separation

“Source Separation” is a wide research area ranging from decomposition of EEG signals to telecommunication channels. We will here focus on speech signals and mention the obligatory cocktail party problem:

Imagine a cocktail party where different speakers speak simultaneously. A keen listener may try to unterstand a single speaker but struggles with interfering speakers and noise.

Our past research projects aimed at solving this by leveraging phase and level differences in a model based clustering approach to estimate spectral masks for each target speaker. This approach has shown promissing results on the fifth CHiME challenge. However, it became apparent that this approach is limited due to the lack of knowledge from an extensive database and not using speech characteristics.

A more recent outcome is a discriminatively trained neural network to provide spectral masks to estimate beamforming coefficients in a multi-channel setting. Motivated by its efficacy demonstrated during the third and fourth CHiME challenge, our current research focusses on:

  • Discriminatively trained spectral masking,
  • Synergy of model based source separation with neural networks,
  • Source separation as a front-end for automatic speech recognition.

Demo

Ansprechpartner

Jens Heitkämper

Nachrichtentechnik (NT)

Forschung & Lehre

Jens Heitkämper
Telefon:
+49 5251 60-5288
Fax:
+49 5251 60-3627
Büro:
P7.2.06

Tobias Cord-Landwehr

Nachrichtentechnik (NT)

Forschung & Lehre

Tobias Cord-Landwehr
Telefon:
+49 5251 60-5288
Fax:
+49 5251 60-3627
Büro:
P7.2.06

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