From 241bf564edf24c64db5d8046fc8cc857db484fde Mon Sep 17 00:00:00 2001 From: Yifan Peng Date: Fri, 10 Nov 2023 14:37:32 -0500 Subject: [PATCH] update sphinx lunch --- _pages/sphinx-lunch.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/_pages/sphinx-lunch.md b/_pages/sphinx-lunch.md index 566dfbd4..45291aa2 100644 --- a/_pages/sphinx-lunch.md +++ b/_pages/sphinx-lunch.md @@ -25,11 +25,6 @@ A tentative schedule can be found [here](https://docs.google.com/spreadsheets/d/ ## Future Talks (tentative schedule) -- November 9, 2023 - - Title: Universal Speech Enhancement: What Can We Do With Real Data? - - Speaker: Wangyou Zhang - - Abstract: Speech enhancement (SE) methods based on deep learning have shown impressive performance on many simulation conditions (TIMIT/WSJ/Librispeech/...+Noise), whereas the generalization to a wider range of real conditions has not been addressed. In fact, many high-performing SE methods tend to overfit the simulation condition in training, whose inductive bias may be easily violated in real conditions. In the era of large-scale pre-training, it is natural to ask whether we can make use of the large-scale real recording data to train a truly universal SE model that can be used for all speech-as-input tasks in real-world conditoins. In this talk, I try to answer the following two questions by summarizing exisiting works on these directions: 1) what can we do to utilize real data for SE training? 2) what models can be used to achieve universal SE? Finally, I will finish the talk by proposing new problems in the related topics. - - Novemver 16, 2023 - Title: TBD - Speaker: Zhong-Qiu Wang @@ -37,6 +32,11 @@ A tentative schedule can be found [here](https://docs.google.com/spreadsheets/d/ ## Previous Talks +- November 9, 2023 + - Title: Universal Speech Enhancement: What Can We Do With Real Data? + - Speaker: Wangyou Zhang + - Abstract: Speech enhancement (SE) methods based on deep learning have shown impressive performance on many simulation conditions (TIMIT/WSJ/Librispeech/...+Noise), whereas the generalization to a wider range of real conditions has not been addressed. In fact, many high-performing SE methods tend to overfit the simulation condition in training, whose inductive bias may be easily violated in real conditions. In the era of large-scale pre-training, it is natural to ask whether we can make use of the large-scale real recording data to train a truly universal SE model that can be used for all speech-as-input tasks in real-world conditoins. In this talk, I try to answer the following two questions by summarizing exisiting works on these directions: 1) what can we do to utilize real data for SE training? 2) what models can be used to achieve universal SE? Finally, I will finish the talk by proposing new problems in the related topics. + - November 2, 2023 - Title: Music generation with precise control - Speakers: Chris Donahue and Shih-Lun Wu