28 citations · 47 across the 12 of their papers we have counts for
5 papers · 1 filter
Adapting Speech Separation to Real-World Meetings Using Mixture Invariant Training
Aswin Sivaraman, Scott Wisdom, Hakan Erdogan +1
The recently-proposed mixture invariant training (MixIT) is an unsupervised method for training single-channel sound separation models in the sense that it does not require ground-…
Zero-Shot Personalized Speech Enhancement through Speaker-Informed Model Selection
Aswin Sivaraman, Minje Kim
This paper presents a novel zero-shot learning approach towards personalized speech enhancement through the use of a sparsely active ensemble model. Optimizing speech denoising sys…
Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification
Aswin Sivaraman, Sunwoo Kim, Minje Kim
Training personalized speech enhancement models is innately a no-shot learning problem due to privacy constraints and limited access to noise-free speech from the target user. If t…
Efficient Personalized Speech Enhancement through Self-Supervised Learning
Aswin Sivaraman, Minje Kim
This work presents self-supervised learning methods for developing monaural speaker-specific (i.e., personalized) speech enhancement models. While generalist models must broadly ad…
Detecting Extraneous Content in Podcasts
Sravana Reddy, Yongze Yu, Aasish Pappu +3
Podcast episodes often contain material extraneous to the main content, such as advertisements, interleaved within the audio and the written descriptions. We present classifiers th…