We provide the technical report for Ego4D audio-only diarization challenge in ECCV 2022. Speaker diarization takes the audio streams as input and outputs the homogeneous segments according to the speaker's identity. It aims to solve the problem of "Who spoke when." In this paper, we explore a Detection-based method to tackle the audio-only speaker diarization task. Our method first extracts audio features by audio backbone and then feeds the feature to a detection-generate network to get the speaker proposals. Finally, after postprocessing, we can get the diarization results. The validation dataset validates this method, and our method achieves 53.85 DER on the test dataset. These results rank 3rd on the leaderboard of Ego4D audio-only diarization challenge 2022.
翻译：我们为ECCV 2022 提供Ego4D 音频专用分解挑战的技术报告。 议长二分法根据发言者的身份将音频流作为输入和输出的同质部分。 它旨在解决“ 何时发言” 的问题 。 在本文中, 我们探索一种基于检测的方法来解决音频专用分解任务 。 我们的方法首先通过音频主干网提取音频特征, 然后将功能反馈到检测- generate 网络以获得演讲者的建议 。 最后, 后处理后, 我们可以得到分解结果 。 验证数据集验证了这个方法, 我们的方法在测试数据集上达到了53.85DER。 这些结果在Ego4D 音频专用分解仪的首列上排名第三, 挑战 2022 。
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