6 citations · 20 across the 6 of their papers we have counts for
6 papers · 1 filter
Class-conditional embeddings for music source separation
Prem Seetharaman, Gordon Wichern, Shrikant Venkataramani +1
Isolating individual instruments in a musical mixture has a myriad of potential applications, and seems imminently achievable given the levels of performance reached by recent deep…
SDR - half-baked or well done?
Jonathan Le Roux, Scott Wisdom, Hakan Erdogan +1
In speech enhancement and source separation, signal-to-noise ratio is a ubiquitous objective measure of denoising/separation quality. A decade ago, the BSS_eval toolkit was develop…
Bootstrapping single-channel source separation via unsupervised spatial clustering on stereo mixtures
Prem Seetharaman, Gordon Wichern, Jonathan Le Roux +1
Separating an audio scene into isolated sources is a fundamental problem in computer audition, analogous to image segmentation in visual scene analysis. Source separation systems b…
Cycle-consistency training for end-to-end speech recognition
Takaaki Hori, Ramon Astudillo, Tomoki Hayashi +3
This paper presents a method to train end-to-end automatic speech recognition (ASR) models using unpaired data. Although the end-to-end approach can eliminate the need for expert k…
A Purely End-to-end System for Multi-speaker Speech Recognition
Hiroshi Seki, Takaaki Hori, Shinji Watanabe +2
Recently, there has been growing interest in multi-speaker speech recognition, where the utterances of multiple speakers are recognized from their mixture. Promising techniques hav…
End-to-End Speech Separation with Unfolded Iterative Phase Reconstruction
Zhong-Qiu Wang, Jonathan Le Roux, DeLiang Wang +1
This paper proposes an end-to-end approach for single-channel speaker-independent multi-speaker speech separation, where time-frequency (T-F) masking, the short-time Fourier transf…