arXiv 2111.013202021-11-02eess.AScs.SD

AVASpeech-SMAD：一个具有标签共现的强标签语音和音乐活动检测数据集

AVASpeech-SMAD: A Strongly Labelled Speech and Music Activity Detection Dataset with Label Co-Occurrence

Center for Music Technology, Georgia Institute of Technology（佐治亚理工学院音乐技术中心）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Netflix, Inc.（Netflix公司）

Yun-Ning Hung, Karn N. Watcharasupat, Chih-Wei Wu, Iroro Orife, Kelian Li, Pavan Seshadri, Junyoung Lee

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摘要：

我们提出了一个数据集，AVASpeech-SMAD，以协助语音和音乐活动检测研究。通过帧级音乐标签，该数据集扩展了现有的AVASpeech数据集，该数据集最初由45小时的音频和语音活动标签组成。据我们所知，拟议的AVASpeech-SMAD是第一个开源数据集，具有强大的音乐和语音复调标签。该数据集通过迭代交叉检查过程进行手动注释和验证。还实施了简单的自动检查，以进一步提高标签的质量。两个国家的最先进的SMAD系统的评估结果也提供了作为基准，供未来参考。

英文摘要：

We propose a dataset, AVASpeech-SMAD, to assist speech and music activity detection research. With frame-level music labels, the proposed dataset extends the existing AVASpeech dataset, which originally consists of 45 hours of audio and speech activity labels. To the best of our knowledge, the proposed AVASpeech-SMAD is the first open-source dataset that features strong polyphonic labels for both music and speech. The dataset was manually annotated and verified via an iterative cross-checking process. A simple automatic examination was also implemented to further improve the quality of the labels. Evaluation results from two state-of-the-art SMAD systems are also provided as a benchmark for future reference.

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