56 citations · 248 across the 22 of their papers we have counts for
24 papers
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…
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…
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,…
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…
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…
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…
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…
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…
Open Range Pitch Tracking for Carrier Frequency Difference Estimation from HF Transmitted Speech
Joerg Schmalenstroeer, Jens Heitkaemper, Joerg Ullmann +1
In this paper we investigate the task of detecting carrier frequency differences from demodulated single sideband signals by examining the pitch contours of the received baseband s…
End-to-End Dereverberation, Beamforming, and Speech Recognition with Improved Numerical Stability and Advanced Frontend
Wangyou Zhang, Christoph Boeddeker, Shinji Watanabe +7
Recently, the end-to-end approach has been successfully applied to multi-speaker speech separation and recognition in both single-channel and multichannel conditions. However, seve…
Warping of Radar Data into Camera Image for Cross-Modal Supervision in Automotive Applications
Christopher Grimm, Tai Fei, Ernst Warsitz +3
We present an approach to automatically generate semantic labels for real recordings of automotive range-Doppler (RD) radar spectra. Such labels are required when training a neural…
Iterative Geometry Calibration from Distance Estimates for Wireless Acoustic Sensor Networks
Tobias Gburrek, Joerg Schmalenstroeer, Reinhold Haeb-Umbach
In this paper we present an approach to geometry calibration in wireless acoustic sensor networks, whose nodes are assumed to be equipped with a compact microphone array. The propo…
Convolutive Transfer Function Invariant SDR training criteria for Multi-Channel Reverberant Speech Separation
Christoph Boeddeker, Wangyou Zhang, Tomohiro Nakatani +6
Time-domain training criteria have proven to be very effective for the separation of single-channel non-reverberant speech mixtures. Likewise, mask-based beamforming has shown impr…
Far-Field Automatic Speech Recognition
Reinhold Haeb-Umbach, Jahn Heymann, Lukas Drude +3
The machine recognition of speech spoken at a distance from the microphones, known as far-field automatic speech recognition (ASR), has received a significant increase of attention…
Deep Neural Network based Distance Estimation for Geometry Calibration in Acoustic Sensor Networks
Tobias Gburrek, Joerg Schmalenstroeer, Andreas Brendel +2
We present an approach to deep neural network based (DNN-based) distance estimation in reverberant rooms for supporting geometry calibration tasks in wireless acoustic sensor netwo…
Multi-path RNN for hierarchical modeling of long sequential data and its application to speaker stream separation
Keisuke Kinoshita, Thilo von Neumann, Marc Delcroix +2
Recently, the source separation performance was greatly improved by time-domain audio source separation based on dual-path recurrent neural network (DPRNN). DPRNN is a simple but e…
Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR
Thilo von Neumann, Christoph Boeddeker, Lukas Drude +4
Most approaches to multi-talker overlapped speech separation and recognition assume that the number of simultaneously active speakers is given, but in realistic situations, it is t…
Contrastive Predictive Coding Supported Factorized Variational Autoencoder for Unsupervised Learning of Disentangled Speech Representations
Janek Ebbers, Michael Kuhlmann, Tobias Cord-Landwehr +1
In this work we address disentanglement of style and content in speech signals. We propose a fully convolutional variational autoencoder employing two encoders: a content encoder a…
Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments
Jens Heitkaemper, Joerg Schmalenstroeer, Reinhold Haeb-Umbach
Speech activity detection (SAD), which often rests on the fact that the noise is "more" stationary than speech, is particularly challenging in non-stationary environments, because…
Jointly optimal denoising, dereverberation, and source separation
Tomohiro Nakatani, Christoph Boeddeker, Keisuke Kinoshita +3
This paper proposes methods that can optimize a Convolutional BeamFormer (CBF) for jointly performing denoising, dereverberation, and source separation (DN+DR+SS) in a computationa…
End-to-end training of time domain audio separation and recognition
Thilo von Neumann, Keisuke Kinoshita, Lukas Drude +4
The rising interest in single-channel multi-speaker speech separation sparked development of End-to-End (E2E) approaches to multi-speaker speech recognition. However, up until now,…
Demystifying TasNet: A Dissecting Approach
Jens Heitkaemper, Darius Jakobeit, Christoph Boeddeker +2
In recent years time domain speech separation has excelled over frequency domain separation in single channel scenarios and noise-free environments. In this paper we dissect the ga…
SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition
Lukas Drude, Jens Heitkaemper, Christoph Boeddeker +1
We present a multi-channel database of overlapping speech for training, evaluation, and detailed analysis of source separation and extraction algorithms: SMS-WSJ -- Spatialized Mul…
Jointly optimal dereverberation and beamforming
Christoph Boeddeker, Tomohiro Nakatani, Keisuke Kinoshita +1
We previously proposed an optimal (in the maximum likelihood sense) convolutional beamformer that can perform simultaneous denoising and dereverberation, and showed its superiority…