3 citations · 9 across the 7 of their papers we have counts for
5 papers · 1 filter
Prevailing Research Areas for Music AI in the Era of Foundation Models
Megan Wei, Mateusz Modrzejewski, Aswin Sivaraman +1
Parallel to rapid advancements in foundation model research, the past few years have witnessed a surge in music AI applications. As AI-generated and AI-augmented music become incre…
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-…
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…
On Psychoacoustically Weighted Cost Functions Towards Resource-Efficient Deep Neural Networks for Speech Denoising
Kai Zhen, Aswin Sivaraman, Jongmo Sung +1
We present a psychoacoustically enhanced cost function to balance network complexity and perceptual performance of deep neural networks for speech denoising. While training the net…