3 citations · 8 across the 6 of their papers we have counts for
6 papers
Upmixing via style transfer: a variational autoencoder for disentangling spatial images and musical content
Haici Yang, Sanna Wager, Spencer Russell +3
In the stereo-to-multichannel upmixing problem for music, one of the main tasks is to set the directionality of the instrument sources in the multichannel rendering results. In thi…
Dereverberation using joint estimation of dry speech signal and acoustic system
Sanna Wager, Keunwoo Choi, Simon Durand
The purpose of speech dereverberation is to remove quality-degrading effects of a time-invariant impulse response filter from the signal. In this report, we describe an approach to…
Deep Autotuner: a Pitch Correcting Network for Singing Performances
Sanna Wager, George Tzanetakis, Cheng-i Wang +1
We introduce a data-driven approach to automatic pitch correction of solo singing performances. The proposed approach predicts note-wise pitch shifts from the relationship between…
Fully Learnable Front-End for Multi-Channel Acoustic Modeling using Semi-Supervised Learning
Sanna Wager, Aparna Khare, Minhua Wu +2
In this work, we investigated the teacher-student training paradigm to train a fully learnable multi-channel acoustic model for far-field automatic speech recognition (ASR). Using…
Deep Autotuner: A Data-Driven Approach to Natural-Sounding Pitch Correction for Singing Voice in Karaoke Performances
Sanna Wager, George Tzanetakis, Cheng-i Wang +3
We describe a machine-learning approach to pitch correcting a solo singing performance in a karaoke setting, where the solo voice and accompaniment are on separate tracks. The prop…
A Data-Driven Approach to Smooth Pitch Correction for Singing Voice in Pop Music
Sanna Wager, Lijiang Guo, Aswin Sivaraman +1
In this paper, we present a machine-learning approach to pitch correction for voice in a karaoke setting, where the vocals and accompaniment are on separate tracks and time-aligned…