30 citations · 98 across the 28 of their papers we have counts for
8 papers · 1 filter
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…
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…
OtoWorld: Towards Learning to Separate by Learning to Move
Omkar Ranadive, Grant Gasser, David Terpay +1
We present OtoWorld, an interactive environment in which agents must learn to listen in order to solve navigational tasks. The purpose of OtoWorld is to facilitate reinforcement le…
Improving Sound Event Detection In Domestic Environments Using Sound Separation
Nicolas Turpault, Scott Wisdom, Hakan Erdogan +5
Performing sound event detection on real-world recordings often implies dealing with overlapping target sound events and non-target sounds, also referred to as interference or nois…