Remixed2remixed: Domain Adaptation for Speech Enhancement by Noise2noise Learning with Remixing

This paper proposes a domain adaptation method for speech enhancement called Remixed2Remixed. The proposed method adopts Noise2Noise (N2N) learning to adapt models trained on artificially generated (out-of-domain: OOD) noisy-clean pairs of data to better separate real-world recorded (in-domain) noisy data. The proposed method employs a teacher model trained on OOD data to acquire pseudo-in-domain speech and noise signals, which are shuffled and remixed twice in each batch to generate two bootstrapped mixtures. The student model is then trained by optimizing an N2N-based cost function computed using these two bootstrapped mixtures. As the training strategy is similar to that of the recently proposed RemixIT, we also investigate the effectiveness of the N2N-based loss as a regularization of RemixIT. Experimental results on the CHiME-7 unsupervised domain adaptation for conversational speech enhancement (UDASE) task revealed that the proposed method outperformed the challenging baseline system, RemixIT, and reduced the performance blurring caused by the teacher models.