论文标题
ICASSP 2022深噪声抑制挑战
ICASSP 2022 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。这是第四次DNS挑战，以前的版本在Interspeech 2020，ICASSP 2021和Interspeech 2021中举行。我们开放源数据集和测试集供研究人员培训其深层噪声抑制模型，以及基于ITU-T P.835的主观评估框架，以对挑战率进行评分和排名。我们提供对DNSMOS P.835和单词准确度（WACC）API的访问权限，以挑战参与者，以帮助进行迭代模型改进。在此挑战中，我们介绍了以下更改：（i）在盲验测试集中包含移动设备方案； （ii）包括基线的个性化噪声抑制轨道； （iii）将WACC添加为客观指标； （iv）包括DNSMOS p.835; （v）制造了培训数据集和测试集成型号（48 kHz）。我们使用平均WACC和主观得分P.835 SIG，BAK和OVRL来获得排名DNS模型的最终分数。我们认为，作为一个研究社区，我们在挑战嘈杂的现实世界情景方面取得了出色的语音质量还有很长的路要走。
The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality. This is the 4th DNS challenge, with the previous editions held at INTERSPEECH 2020, ICASSP 2021, and INTERSPEECH 2021. We open-source datasets and test sets for researchers to train their deep noise suppression models, as well as a subjective evaluation framework based on ITU-T P.835 to rate and rank-order the challenge entries. We provide access to DNSMOS P.835 and word accuracy (WAcc) APIs to challenge participants to help with iterative model improvements. In this challenge, we introduced the following changes: (i) Included mobile device scenarios in the blind test set; (ii) Included a personalized noise suppression track with baseline; (iii) Added WAcc as an objective metric; (iv) Included DNSMOS P.835; (v) Made the training datasets and test sets fullband (48 kHz). We use an average of WAcc and subjective scores P.835 SIG, BAK, and OVRL to get the final score for ranking the DNS models. We believe that as a research community, we still have a long way to go in achieving excellent speech quality in challenging noisy real-world scenarios.
