1 citations · 1 across the 7 of their papers we have counts for
7 papers
A Comparative Study on Positional Encoding for Time-frequency Domain Dual-path Transformer-based Source Separation Models
Kohei Saijo, Tetsuji Ogawa
In this study, we investigate the impact of positional encoding (PE) on source separation performance and the generalization ability to long sequences (length extrapolation) in Tra…
Is MixIT Really Unsuitable for Correlated Sources? Exploring MixIT for Unsupervised Pre-training in Music Source Separation
Kohei Saijo, Yoshiaki Bando
In music source separation (MSS), obtaining isolated sources or stems is highly costly, making pre-training on unlabeled data a promising approach. Although source-agnostic unsuper…
Remixing-based Unsupervised Source Separation from Scratch
Kohei Saijo, Tetsuji Ogawa
We propose an unsupervised approach for training separation models from scratch using RemixIT and Self-Remixing, which are recently proposed self-supervised learning methods for re…
Self-Remixing: Unsupervised Speech Separation via Separation and Remixing
Kohei Saijo, Tetsuji Ogawa
We present Self-Remixing, a novel self-supervised speech separation method, which refines a pre-trained separation model in an unsupervised manner. The proposed method consists of…
Spatial Loss for Unsupervised Multi-channel Source Separation
Kohei Saijo, Robin Scheibler
We propose a spatial loss for unsupervised multi-channel source separation. The proposed loss exploits the duality of direction of arrival (DOA) and beamforming: the steering and b…
Remix-cycle-consistent Learning on Adversarially Learned Separator for Accurate and Stable Unsupervised Speech Separation
Kohei Saijo, Tetsuji Ogawa
A new learning algorithm for speech separation networks is designed to explicitly reduce residual noise and artifacts in the separated signal in an unsupervised manner. Generative…
Independence-based Joint Dereverberation and Separation with Neural Source Model
Kohei Saijo, Robin Scheibler
We propose an independence-based joint dereverberation and separation method with a neural source model. We introduce a neural network in the framework of time-decorrelation iterat…