ICASSP 2023 深度噪声抑制挑战赛

IF 2.9 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Harishchandra Dubey;Ashkan Aazami;Vishak Gopal;Babak Naderi;Sebastian Braun;Ross Cutler;Alex Ju;Mehdi Zohourian;Min Tang;Mehrsa Golestaneh;Robert Aichner
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引用次数: 0

摘要

ICASSP 2023 深度噪声抑制(DNS)挑战赛是 DNS 系列挑战赛的第五届。DNS 挑战赛于 2019 年至 2023 年举办,旨在促进 DNS 领域的研究。前几届 DNS 挑战赛分别在 INTERSPEECH 2020、ICASSP 2021、INTERSPEECH 2021 和 ICASSP 2022 上举行。本次挑战赛旨在推进能够共同解决去噪、去混响和干扰通话抑制问题的模型,并将耳机和免提电话场景作为不同赛道的重点。该挑战赛通过为每个测试片段提供随附的注册片段(每个片段仅包含主要说话者)来促进个性化深度噪声抑制,这些片段可用于计算说话者身份特征并区分主要语音和干扰语音。虽然提交给挑战赛的大多数模型都是个性化的,但在两个赛道中都有相同的团队获胜。与噪声盲测试集相比,最佳模型的挑战得分分别提高了 0.145 和 0.141。我们还进行了其他分析,并与之前的挑战赛进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ICASSP 2023 Deep Noise Suppression Challenge
The ICASSP 2023 Deep Noise Suppression (DNS) Challenge marks the fifth edition of the DNS challenge series. DNS challenges were organized from 2019 to 2023 to foster research in the field of DNS. Previous DNS challenges were held at INTERSPEECH 2020, ICASSP 2021, INTERSPEECH 2021, and ICASSP 2022. This challenge aims to advance models capable of jointly addressing denoising, dereverberation, and interfering talker suppression, with separate tracks focusing on headset and speakerphone scenarios. The challenge facilitates personalized deep noise suppression by providing accompanying enrollment clips for each test clip, each containing the primary talker only, which can be used to compute a speaker identity feature and disentangle primary and interfering speech. While the majority of models submitted to the challenge were personalized, the same teams emerged as the winners in both tracks. The best models demonstrated improvements of 0.145 and 0.141 in the challenge's score, respectively, when compared to the noisy blind test set. We present additional analysis and draw comparisons to previous challenges.
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CiteScore
5.30
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