104 citations · 305 across the 50 of their papers we have counts for
5 papers · 1 filter
Finding Strength in Weakness: Learning to Separate Sounds with Weak Supervision
Fatemeh Pishdadian, Gordon Wichern, Jonathan Le Roux
While there has been much recent progress using deep learning techniques to separate speech and music audio signals, these systems typically require large collections of isolated s…
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…
WHAMR!: Noisy and Reverberant Single-Channel Speech Separation
Matthew Maciejewski, Gordon Wichern, Emmett McQuinn +1
While significant advances have been made with respect to the separation of overlapping speech signals, studies have been largely constrained to mixtures of clean, near anechoic sp…
Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity
Ethan Manilow, Gordon Wichern, Prem Seetharaman +1
Music source separation performance has greatly improved in recent years with the advent of approaches based on deep learning. Such methods typically require large amounts of label…
WHAM!: Extending Speech Separation to Noisy Environments
Gordon Wichern, Joe Antognini, Michael Flynn +5
Recent progress in separating the speech signals from multiple overlapping speakers using a single audio channel has brought us closer to solving the cocktail party problem. Howeve…