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Found 7 papers, 2 papers with code
Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks
In this paper, we propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition, which is referred to as progressive tandem learning of deep SNNs.
SpEx+: A Complete Time Domain Speaker Extraction Network
To eliminate such mismatch, we propose a complete time-domain speaker extraction solution, that is called SpEx+.
Ranked #1 on
Speech Extraction
on WSJ0-2mix-extr
Speech Extraction
Audio and Speech Processing
Sound
Time-domain speaker extraction network
The inaccuracy of phase estimation is inherent to the frequency domain processing, that affects the quality of signal reconstruction.
Audio and Speech Processing Sound
SpEx: Multi-Scale Time Domain Speaker Extraction Network
Inspired by Conv-TasNet, we propose a time-domain speaker extraction network (SpEx) that converts the mixture speech into multi-scale embedding coefficients instead of decomposing the speech signal into magnitude and phase spectra.
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
Optimization of Speaker Extraction Neural Network with Magnitude and Temporal Spectrum Approximation Loss
The SpeakerBeam-FE (SBF) method is proposed for speaker extraction.
Fantastic 4 system for NIST 2015 Language Recognition Evaluation
This article describes the systems jointly submitted by Institute for Infocomm (I$^2$R), the Laboratoire d'Informatique de l'Universit\'e du Maine (LIUM), Nanyang Technology University (NTU) and the University of Eastern Finland (UEF) for 2015 NIST Language Recognition Evaluation (LRE).
