本文介绍了Hitachi团队的建议自动采样系统，为自动采样的第一个共享任务（Automin-2021）。我们利用可参考方法（即，不使用培训分钟）进行自动采样（任务A），首先将转录成块分成块，随后将这些块与精细调整的预先训练的BART模型总结一下论聊天对话的概述语料库。此外，我们将参数挖掘技术应用于生成的分钟，以一种结构良好和连贯的方式重新组织它们。我们利用多个相关性分数来确定在给出的转录物或另一分钟时是否从相同的会议中衍生出一分钟（任务B和C）。在这些分数之上，我们培养传统的机器学习模型来绑定它们并进行最终决策。因此，我们的任务方法是在语法正确和流畅性方面，在所有提交的所有提交和最佳系统中实现最佳充分性评分。对于任务B和C，所提出的模型成功地表现了大多数投票基线。

We propose GANStrument, a generative adversarial model for instrument sound synthesis. Given a one-shot sound as input, it is able to generate pitched instrument sounds that reflect the timbre of the input within an interactive time. By exploiting instance conditioning, GANStrument achieves better fidelity and diversity of synthesized sounds and generalization ability to various inputs. In addition, we introduce an adversarial training scheme for a pitch-invariant feature extractor that significantly improves the pitch accuracy and timbre consistency. Experimental results show that GANStrument outperforms strong baselines that do not use instance conditioning in terms of generation quality and input editability. Qualitative examples are available online.