30 citations · 51 across the 9 of their papers we have counts for
16 papers
How to Listen? Rethinking Visual Sound Localization
Ho-Hsiang Wu, Magdalena Fuentes, Prem Seetharaman +1
Localizing visual sounds consists on locating the position of objects that emit sound within an image. It is a growing research area with potential applications in monitoring natur…
Unsupervised Source Separation By Steering Pretrained Music Models
Ethan Manilow, Patrick O'Reilly, Prem Seetharaman +1
We showcase an unsupervised method that repurposes deep models trained for music generation and music tagging for audio source separation, without any retraining. An audio generati…
What's All the FUSS About Free Universal Sound Separation Data?
Scott Wisdom, Hakan Erdogan, Daniel Ellis +6
We introduce the Free Universal Sound Separation (FUSS) dataset, a new corpus for experiments in separating mixtures of an unknown number of sounds from an open domain of sound typ…
Sound Event Detection and Separation: a Benchmark on Desed Synthetic Soundscapes
Nicolas Turpault, Romain Serizel, Scott Wisdom +5
We propose a benchmark of state-of-the-art sound event detection systems (SED). We designed synthetic evaluation sets to focus on specific sound event detection challenges. We anal…
A Study of Transfer Learning in Music Source Separation
Andreas Bugler, Bryan Pardo, Prem Seetharaman
Supervised deep learning methods for performing audio source separation can be very effective in domains where there is a large amount of training data. While some music domains ha…
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…