6 papers
Room Impulse Response Embeddings for Speech Enhancement in Noisy and Reverberant Environments
Adrian Meise, Reinhold Haeb-Umbach
We propose a self-supervised approach for learning room impulse response (RIR) representations from single-channel noisy-reverberant speech. It consists of first training on reverb…
Investigating the Integration of Spatial Information in Foundation-Model-Based Speaker Diarization
Marc Deegen, Adrian Meise, Reinhold Haeb-Umbach
Spatial information gleaned from multi-channel input has been shown to lead to improvements in meeting processing tasks like diarization and source separation. At the same time, di…
Loose coupling of spectral and spatial models for multi-channel diarization and enhancement of meetings in dynamic environments
Adrian Meise, Tobias Cord-Landwehr, Christoph Boeddeker +3
Sound capture by microphone arrays opens the possibility to exploit spatial, in addition to spectral, information for diarization and signal enhancement, two important tasks in mee…
On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation
Adrian Meise, Tobias Cord-Landwehr, Reinhold Haeb-Umbach
Diffusion models have been shown to achieve natural-sounding enhancement of speech degraded by noise or reverberation. However, their simultaneous denoising and dereverberation cap…
Diminishing Domain Mismatch for DNN-Based Acoustic Distance Estimation via Stochastic Room Reverberation Models
Tobias Gburrek, Adrian Meise, Joerg Schmalenstroeer +1
The room impulse response (RIR) encodes, among others, information about the distance of an acoustic source from the sensors. Deep neural networks (DNNs) have been shown to be able…
Investigating Speaker Embedding Disentanglement on Natural Read Speech
Michael Kuhlmann, Adrian Meise, Fritz Seebauer +2
Disentanglement is the task of learning representations that identify and separate factors that explain the variation observed in data. Disentangled representations are useful to i…