6 citations · 18 across the 6 of their papers we have counts for
9 papers
Bespoke Neural Networks for Score-Informed Source Separation
Ethan Manilow, Bryan Pardo
In this paper, we introduce a simple method that can separate arbitrary musical instruments from an audio mixture. Given an unaligned MIDI transcription for a target instrument fro…
AutoClip: Adaptive Gradient Clipping for Source Separation Networks
Prem Seetharaman, Gordon Wichern, Bryan Pardo +1
Clipping the gradient is a known approach to improving gradient descent, but requires hand selection of a clipping threshold hyperparameter. We present AutoClip, a simple method fo…
Incorporating Music Knowledge in Continual Dataset Augmentation for Music Generation
Alisa Liu, Alexander Fang, Gaëtan Hadjeres +2
Deep learning has rapidly become the state-of-the-art approach for music generation. However, training a deep model typically requires a large training set, which is often not avai…
Bach or Mock? A Grading Function for Chorales in the Style of J.S. Bach
Alexander Fang, Alisa Liu, Prem Seetharaman +1
Deep generative systems that learn probabilistic models from a corpus of existing music do not explicitly encode knowledge of a musical style, compared to traditional rule-based sy…
OtoMechanic: Auditory Automobile Diagnostics via Query-by-Example
Max Morrison, Bryan Pardo
Early detection and repair of failing components in automobiles reduces the risk of vehicle failure in life-threatening situations. Many automobile components in need of repair pro…
Bootstrapping deep music separation from primitive auditory grouping principles
Prem Seetharaman, Gordon Wichern, Jonathan Le Roux +1
Separating an audio scene such as a cocktail party into constituent, meaningful components is a core task in computer audition. Deep networks are the state-of-the-art approach. The…