Separating Varying Numbers of Sources with Auxiliary Autoencoding Loss

27 March 2020

Papers citing "Separating Varying Numbers of Sources with Auxiliary Autoencoding Loss"

7 / 7 papers shown

Title
Deep neural network techniques for monaural speech enhancement: state of the art analysis P. Ochieng 28 21 0 01 Dec 2022
SA-SDR: A novel loss function for separation of meeting style data Thilo von Neumann K. Kinoshita Christoph Boeddeker Marc Delcroix Reinhold Haeb-Umbach 24 20 0 29 Oct 2021
Multi-ACCDOA: Localizing and Detecting Overlapping Sounds from the Same Class with Auxiliary Duplicating Permutation Invariant Training Kazuki Shimada Yuichiro Koyama Shusuke Takahashi Naoya Takahashi E. Tsunoo Yuki Mitsufuji 13 63 0 14 Oct 2021
What's All the FUSS About Free Universal Sound Separation Data? Scott Wisdom Hakan Erdogan D. Ellis Romain Serizel Nicolas Turpault Eduardo Fonseca Justin Salamon Prem Seetharaman J. Hershey 25 81 0 02 Nov 2020
Exploring the time-domain deep attractor network with two-stream architectures in a reverberant environment Hangting Chen Pengyuan Zhang 6 6 0 01 Jul 2020
Multimodal Target Speech Separation with Voice and Face References Leyuan Qu C. Weber S. Wermter CVBM 19 19 0 17 May 2020
On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement Morten Kolbæk Z. Tan S. H. Jensen Jesper Jensen AAML 58 125 0 03 Sep 2019