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Holger Fröning

2 accepted papers

2020

Towards Real-Time Single-Channel Singing-Voice Separation with Pruned Multi-Scaled Densenets

ICASSP 2020accepted

Modern musical source separation systems based on deep neural networks reach unprecedented levels of separation quality. However, harnessing the power of these large-scale models in typical audio production environments, which frequently offer only limited computing resources while demanding real-ti…

Cited by 0SourceScholar
2018

Resource Efficient Deep Eigenvector Beamforming

ICASSP 2018accepted

We propose binary neural networks (BNN s) for acoustic beamforming. This makes the speech enhancement approach resource efficient and applicable for embedded applications. Using CHiME4 data, we use BNN s to estimate the speech presence probability mask for GEV-PAN beamformers. By doing so, we achiev…

Cited by 0SourceScholar