Search Results for author: Mattes Ohlenbusch

Found 4 papers, 0 papers with code

Multi-Microphone Noise Data Augmentation for DNN-based Own Voice Reconstruction for Hearables in Noisy Environments

no code implementations14 Dec 2023 Mattes Ohlenbusch, Christian Rollwage, Simon Doclo

Recording a sufficient amount of noise required for training such a system is costly since noise transmission between outer and inner microphones varies individually.

Data Augmentation

Modeling of Speech-dependent Own Voice Transfer Characteristics for Hearables with In-ear Microphones

no code implementations10 Oct 2023 Mattes Ohlenbusch, Christian Rollwage, Simon Doclo

In this paper, we propose a speech-dependent model of the own voice transfer characteristics based on phoneme recognition, assuming a linear time-invariant relative transfer function for each phoneme.

Speech-dependent Modeling of Own Voice Transfer Characteristics for In-ear Microphones in Hearables

no code implementations15 Sep 2023 Mattes Ohlenbusch, Christian Rollwage, Simon Doclo

To enhance the quality of the in-ear microphone signal using algorithms aiming at joint bandwidth extension, equalization, and noise reduction, it is desirable to have an accurate model of the own voice transfer characteristics between the entrance of the ear canal and the in-ear microphone.

Bandwidth Extension

Training Strategies for Own Voice Reconstruction in Hearing Protection Devices using an In-ear Microphone

no code implementations12 May 2022 Mattes Ohlenbusch, Christian Rollwage, Simon Doclo

In this paper, we apply a deep learning-based bandwidth-extension system to the own voice reconstruction task and investigate different training strategies in order to overcome the limited availability of training data.

Bandwidth Extension

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