3 citations · 8 across the 6 of their papers we have counts for
4 papers · 1 filter
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…