1 code implementation • Interspeech 2021 • Lukas Pfeifenberger, Matthias Zoehrer, Franz Pernkopf
This paper proposes the Cross-Domain Echo-Controller(CDEC), submitted to the Interspeech 2021 AEC-Challenge. The algorithm consists of three building blocks: (i) a Time-Delay Compensation (TDC) module, (ii) a frequency-domainblock-based Acoustic Echo Canceler (AEC), and (iii) a Time-Domain Neural-Network (TD-NN) used as a post-processor. Our system achieves an overall MOS score of 3. 80, while onlyusing 2. 1 million parameters at a system latency of 32ms.
1 code implementation • 24 Mar 2021 • Lukas Pfeifenberger, Franz Pernkopf
In this paper, we present the Blind Speech Separation and Dereverberation (BSSD) network, which performs simultaneous speaker separation, dereverberation and speaker identification in a single neural network.
1 code implementation • Interspeech 2020 • Lukas Pfeifenberger, Franz Pernkopf
The acoustic front-end of hands-free communication de-vices introduces a variety of distortions to the linear echo pathbetween the loudspeaker and the microphone.
no code implementations • 22 Jul 2020 • Lukas Pfeifenberger, Matthias Zöhrer, Günther Schindler, Wolfgang Roth, Holger Fröning, Franz Pernkopf
While machine learning techniques are traditionally resource intensive, we are currently witnessing an increased interest in hardware and energy efficient approaches.
1 code implementation • ICASSP 2019 • Lukas Pfeifenberger, Franz Pernkopg
We propose a complex-valued deep neural network (cDNN) for speech enhancement and source separation.
no code implementations • 5 Dec 2018 • Franz Pernkopf, Wolfgang Roth, Matthias Zoehrer, Lukas Pfeifenberger, Guenther Schindler, Holger Froening, Sebastian Tschiatschek, Robert Peharz, Matthew Mattina, Zoubin Ghahramani
In that way, we provide an extensive overview of the current state-of-the-art of robust and efficient machine learning for real-world systems.