SAIGE

Please check out Darius Petermann's cool presentation on SpaIn-Net, a music source separation model that is mindful of the instruments' spatial locations. SpaIn-Net is robust even if the spatial information is not precise. ;) https://iu.mediaspace.kaltura.com/media/t/1_mboimmw7

SpaIn-Net is a spatially-informed network for music source separation. It takes the user's rough guess about the stereophonic location of the musical instrument as input and does better separation. More details, demo, source codes, and our paper about the SpaIn-Net project are here: https://saige.sice.indiana.edu/research-projects/spain-net/

For we named one of our new deep learning models after our beloved hometown, Bloomington, IN! In this paper, we present "BLOOM-Net" that flexibly scales its architecture to fit from small to large devices, while it always retains optimal speech enhancement performances. More details on this open-sourced project can be found here:

BLOOM-Net: Scalability Matters – SAIGE@IU BLOOM-Net: Scalability Matters Scalability is a big deal when it comes to video coding. When you watch a movie via a streaming service on Friday night, the video quality fluctuates—it’s the video codec’s effort in providing the maximum video quality even though your internet connection suffers...

SAIGE members authored SEVEN papers accepted for publication at ICASSP 2022. Great team work among the SAIGE members as well as exciting external collaboration! We are all sincerely hoping to be there in Singapore in person this year!

Sunwoo Kim, Minje Kim, "BLOOM-Net: Blockwise Optimization for Masking Networks Toward Scalable And Efficient Speech Enhancement"

Darius Petermann, Minje Kim, "SpaIn-Net: Spatially-Informed Stereophonic Music Source Separation"

Haici Yang, Shivani Firodiya, Nicholas Bryan, Minje Kim, "Don't Separate, Learn to Remix: End-to-End Neural Remixing with Joint Optimization”

Haici Yang, Sanna Wager, Spencer Russell, Mike Luo, Minje Kim, Wontak Kim, "Upmixing via Style Transfer: a Variational Autoencoder for Disentangling Spatial Images and Musical Content"

Hao Zhang, Srivatsan Kandadai, Harsha Rao, Minje Kim, Tarun Pruthi, Trausti Kristjansson, "Deep Adaptive AEC: Hybrid of Deep Learning and Adaptive Acoustic Echo Cancellation"

Aswin Sivaraman, Scott Wisdom, Hakan Erdogan, John R. Hershey, "Adapting Speech Separation to Real-World Meetings Using Mixture Invariant Training"

Darius Petermann, Gordon Wichern, Jonathan Le Roux, Zhong-Qiu Wang, "The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks"