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20192021
most citedSMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition

56 citations · 145 across the 14 of their papers we have counts for

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Showing eess.ASShow all

16 papers · 1 filter

eess.AS2021

Monaural source separation: From anechoic to reverberant environments

Tobias Cord-Landwehr, Christoph Boeddeker, Thilo von Neumann +3

Impressive progress in neural network-based single-channel speech source separation has been made in recent years. But those improvements have been mostly reported on anechoic data…

eess.AS2021

On Synchronization of Wireless Acoustic Sensor Networks in the Presence of Time-varying Sampling Rate Offsets and Speaker Changes

Tobias Gburrek, Joerg Schmalenstroeer, Reinhold Haeb-Umbach

A wireless acoustic sensor network records audio signals with sampling time and sampling rate offsets between the audio streams, if the analog-digital converters (ADCs) of the netw…

eess.AS2021

SA-SDR: A novel loss function for separation of meeting style data

Thilo von Neumann, Keisuke Kinoshita, Christoph Boeddeker +2

Many state-of-the-art neural network-based source separation systems use the averaged Signal-to-Distortion Ratio (SDR) as a training objective function. The basic SDR is, however,…

eess.AS2021★ 6 cited

Speeding Up Permutation Invariant Training for Source Separation

Thilo von Neumann, Christoph Boeddeker, Keisuke Kinoshita +2

Permutation invariant training (PIT) is a widely used training criterion for neural network-based source separation, used for both utterance-level separation with utterance-level P…

eess.AS2021★ 20 cited

Graph-PIT: Generalized permutation invariant training for continuous separation of arbitrary numbers of speakers

Thilo von Neumann, Keisuke Kinoshita, Christoph Boeddeker +2

Automatic transcription of meetings requires handling of overlapped speech, which calls for continuous speech separation (CSS) systems. The uPIT criterion was proposed for utteranc…

eess.AS2021★ 5 cited

Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-supervised Sound Event Detection

Janek Ebbers, Reinhold Haeb-Umbach

In this paper we present our system for the detection and classification of acoustic scenes and events (DCASE) 2020 Challenge Task 4: Sound event detection and separation in domest…