论文标题
音频Barlow双胞胎：自我监督的音频表示学习
Audio Barlow Twins: Self-Supervised Audio Representation Learning
论文作者
论文摘要
Barlow Twins自制学习目标既不需要负样本或不对称的学习更新，从而与计算机视觉中最新的最新艺术相提并论。因此，我们提出了音频Barlow Twins，这是一种新颖的自我监视的音频表示方法，将Barlow Twins适应音频域。我们在大规模的音频数据集音频集上进行了预先培训，并评估了来自Hear 2021挑战中的18个任务的学习表现质量，从而取得了超出表现或以其他方式与当前最新的结果相提并论的结果，例如，歧视自我要求的自我选择的学习方法对audio表示学习。 https://github.com/jonahanton/ssl_audio上的代码。
The Barlow Twins self-supervised learning objective requires neither negative samples or asymmetric learning updates, achieving results on a par with the current state-of-the-art within Computer Vision. As such, we present Audio Barlow Twins, a novel self-supervised audio representation learning approach, adapting Barlow Twins to the audio domain. We pre-train on the large-scale audio dataset AudioSet, and evaluate the quality of the learnt representations on 18 tasks from the HEAR 2021 Challenge, achieving results which outperform, or otherwise are on a par with, the current state-of-the-art for instance discrimination self-supervised learning approaches to audio representation learning. Code at https://github.com/jonahanton/SSL_audio.
