论文标题
ICASSP 2021深噪声抑制挑战
ICASSP 2021 Deep Noise Suppression Challenge
论文作者
论文摘要
深层抑制（DNS）挑战旨在促进抑制噪声领域的创新，以实现优越的感知语音质量。我们最近在Interspeech 2020举办了一次DNS挑战特别会议。我们开了培训和测试数据集，以供研究人员训练其噪声抑制模型。我们还开了一个主观的评估框架，并使用该工具评估和选择最终获奖者。来自学术界和行业的许多研究人员为推动领域的前进做出了重大贡献。我们还了解到，作为一个研究界，我们在挑战嘈杂的实时条件方面取得了出色的语音质量还有很长的路要走。在这一挑战中，我们正在扩大培训和测试数据集。有两条曲目，其中一条侧重于实时降级，另一个曲目专注于实时个性化的深层噪声。我们还制作了一个名为DNSMO的无侵入的客观语音质量指标，可供参与者在开发阶段使用。最终评估将基于主观测试。
The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality. We recently organized a DNS challenge special session at INTERSPEECH 2020. We open sourced training and test datasets for researchers to train their noise suppression models. We also open sourced a subjective evaluation framework and used the tool to evaluate and pick the final winners. Many researchers from academia and industry made significant contributions to push the field forward. We also learned that as a research community, we still have a long way to go in achieving excellent speech quality in challenging noisy real-time conditions. In this challenge, we are expanding both our training and test datasets. There are two tracks with one focusing on real-time denoising and the other focusing on real-time personalized deep noise suppression. We also make a non-intrusive objective speech quality metric called DNSMOS available for participants to use during their development stages. The final evaluation will be based on subjective tests.
