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pro vyhledávání: '"Peters Nils"'
Autor:
Gaznepoglu, Ünal Ege, Peters, Nils
Speaker anonymization systems continue to improve their ability to obfuscate the original speaker characteristics in a speech signal, but often create processing artifacts and unnatural sounding voices as a tradeoff. Many of those systems stem from t
Externí odkaz:
http://arxiv.org/abs/2308.11337
Autor:
Gaznepoglu, Ünal Ege, Peters, Nils
Voice conversion for speaker anonymization is an emerging concept for privacy protection. In a deep learning setting, this is achieved by extracting multiple features from speech, altering the speaker identity, and waveform synthesis. However, many e
Externí odkaz:
http://arxiv.org/abs/2306.16860
We introduce a novel method to improve the performance of the VoicePrivacy Challenge 2022 baseline B1 variants. Among the known deficiencies of x-vector-based anonymization systems is the insufficient disentangling of the input features. In particula
Externí odkaz:
http://arxiv.org/abs/2210.17338
Autor:
Rao, Nagashree K. S., Peters, Nils
Previous DCASE challenges contributed to an increase in the performance of acoustic scene classification systems. State-of-the-art classifiers demand significant processing capabilities and memory which is challenging for resource-constrained mobile
Externí odkaz:
http://arxiv.org/abs/2112.04841
Autor:
Chenna, Srivatsav, Peters, Nils
Detection of fabricated or manipulated audio content to prevent, e.g., distribution of forgeries in digital media, is crucial, especially in political and reputational contexts. Better tools for protecting the integrity of media creation are desired.
Externí odkaz:
http://arxiv.org/abs/2110.08821
Autor:
Gaznepoglu, Ünal Ege, Peters, Nils
Voice conversion for speaker anonymization is an emerging field in speech processing research. Many state-of-the-art approaches are based on the resynthesis of the phoneme posteriorgrams (PPG), the fundamental frequency (F0) of the input signal toget
Externí odkaz:
http://arxiv.org/abs/2110.06887
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