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Philipp Götz

3 accepted papers

2026

MATCHING REVERBERANT SPEECH THROUGH LEARNED ACOUSTIC EMBEDDINGS AND FEEDBACK DELAY NETWORKS

ICASSP 2026oral

Reverberation conveys critical acoustic cues about the environment, supporting spatial awareness and immersion. For auditory augmented reality (AAR) systems, generating perceptually plausible reverberation in real time remains a key challenge, especially when explicit acoustic measurements are unava…

Cited by 0SourcePDFScholar
2023

Contrastive Representation Learning for Acoustic Parameter Estimation

ICASSP 2023accepted

A study is presented in which a contrastive learning approach is used to extract low-dimensional representations of the acoustic environment from single-channel, reverberant speech signals. Convolution of room impulse responses (RIRs) with anechoic source signals is leveraged as a data augmentation…

Cited by 0SourceScholar
2022

Blind Reverberation Time Estimation in Dynamic Acoustic Conditions

ICASSP 2022accepted

The estimation of reverberation time from real-world signals plays a central role in a wide range of applications. In many scenarios, acoustic conditions change over time which in turn requires the estimate to be updated continuously. Previously proposed methods involving deep neural networks were m…

Cited by 0SourceScholar