activity
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

collaborators

20 papers

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.AS20216 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…

cs.SD20212 cited

A Database for Research on Detection and Enhancement of Speech Transmitted over HF links

Jens Heitkaemper, Joerg Schmalenstroeer, Joerg Ullmann +2

In this paper we present an open database for the development of detection and enhancement algorithms of speech transmitted over HF radio channels. It consists of audio samples rec…

eess.AS202120 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…

cs.SD20218 cited

A Comparison and Combination of Unsupervised Blind Source Separation Techniques

Christoph Boeddeker, Frederik Rautenberg, Reinhold Haeb-Umbach

Unsupervised blind source separation methods do not require a training phase and thus cannot suffer from a train-test mismatch, which is a common concern in neural network based so…

eess.AS20215 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…