3 papers
eess.AS2026
Input-Adaptive Spectral Feature Compression by Sequence Modeling for Source Separation
Kohei Saijo, Yoshiaki Bando
Time-frequency domain dual-path models have demonstrated strong performance and are widely used in source separation. Because their computational cost grows with the number of freq…
eess.AS2025
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…
cs.SD2025
Formula-Supervised Sound Event Detection: Pre-Training Without Real Data
Yuto Shibata, Keitaro Tanaka, Yoshiaki Bando +3
In this paper, we propose a novel formula-driven supervised learning (FDSL) framework for pre-training an environmental sound analysis model by leveraging acoustic signals parametr…