9 citations · 29 across the 12 of their papers we have counts for
12 papers
Improving acoustic drone detection generalization through pretraining and data augmentation
Paul M. Reuter, Mattes Ohlenbusch, Christian Rollwage
Detecting unauthorized UAV flights is critical for surveillance, security, and airspace management. Acoustic drone detection, which relies on the distinctive propeller and motor so…
Subjective quality evaluation of personalized own voice reconstruction systems
Mattes Ohlenbusch, Christian Rollwage, Simon Doclo +1
Own voice pickup technology for hearable devices facilitates communication in noisy environments. Own voice reconstruction (OVR) systems enhance the quality and intelligibility of…
Comparison of Knowledge Distillation Methods for Low-complexity Multi-microphone Speech Enhancement using the FT-JNF Architecture
Robert Metzger, Mattes Ohlenbusch, Christian Rollwage +1
Multi-microphone speech enhancement using deep neural networks (DNNs) has significantly progressed in recent years. However, many proposed DNN-based speech enhancement algorithms c…
Low-Complexity Own Voice Reconstruction for Hearables with an In-Ear Microphone
Mattes Ohlenbusch, Christian Rollwage, Simon Doclo
Hearable devices, equipped with one or more microphones, are commonly used for speech communication. Here, we consider the scenario where a hearable is used to capture the user's o…
Speech-dependent Data Augmentation for Own Voice Reconstruction with Hearable Microphones in Noisy Environments
Mattes Ohlenbusch, Christian Rollwage, Simon Doclo
Own voice pickup for hearables in noisy environments benefits from using both an outer and an in-ear microphone outside and inside the occluded ear. Due to environmental noise reco…
Multi-Microphone Noise Data Augmentation for DNN-based Own Voice Reconstruction for Hearables in Noisy Environments
Mattes Ohlenbusch, Christian Rollwage, Simon Doclo
Hearables with integrated microphones may offer communication benefits in noisy working environments, e.g. by transmitting the recorded own voice of the user. Systems aiming at rec…
Modeling of Speech-dependent Own Voice Transfer Characteristics for Hearables with In-ear Microphones
Mattes Ohlenbusch, Christian Rollwage, Simon Doclo
Many hearables contain an in-ear microphone, which may be used to capture the own voice of its user. However, due to the hearable occluding the ear canal, the in-ear microphone mos…
Speech-dependent Modeling of Own Voice Transfer Characteristics for In-ear Microphones in Hearables
Mattes Ohlenbusch, Christian Rollwage, Simon Doclo
Many hearables contain an in-ear microphone, which may be used to capture the own voice of its user in noisy environments. Since the in-ear microphone mostly records body-conducted…
Multilingual Query-by-Example Keyword Spotting with Metric Learning and Phoneme-to-Embedding Mapping
Paul M. Reuter, Christian Rollwage, Bernd T. Meyer
In this paper, we propose a multilingual query-by-example keyword spotting (KWS) system based on a residual neural network. The model is trained as a classifier on a multilingual k…
Speaker-conditioning Single-channel Target Speaker Extraction using Conformer-based Architectures
Ragini Sinha, Marvin Tammen, Christian Rollwage +1
Target speaker extraction aims at extracting the target speaker from a mixture of multiple speakers exploiting auxiliary information about the target speaker. In this paper, we con…
Training Strategies for Own Voice Reconstruction in Hearing Protection Devices using an In-ear Microphone
Mattes Ohlenbusch, Christian Rollwage, Simon Doclo
In-ear microphones in hearing protection devices can be utilized to capture the own voice speech of the person wearing the devices in noisy environments. Since in-ear recordings of…
Speaker-conditioned Target Speaker Extraction based on Customized LSTM Cells
Ragini Sinha, Marvin Tammen, Christian Rollwage +1
Speaker-conditioned target speaker extraction systems rely on auxiliary information about the target speaker to extract the target speaker signal from a mixture of multiple speaker…