6 citations · 20 across the 6 of their papers we have counts for
13 papers · 1 filter
Transcription Is All You Need: Learning to Separate Musical Mixtures with Score as Supervision
Yun-Ning Hung, Gordon Wichern, Jonathan Le Roux
Most music source separation systems require large collections of isolated sources for training, which can be difficult to obtain. In this work, we use musical scores, which are co…
Streaming automatic speech recognition with the transformer model
Niko Moritz, Takaaki Hori, Jonathan Le Roux
Encoder-decoder based sequence-to-sequence models have demonstrated state-of-the-art results in end-to-end automatic speech recognition (ASR). Recently, the transformer architectur…
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…