This technical report describes the system participating to the Detection and
Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge, Task 6:
automated audio captioning. Our submission focuses on solving two indeterminacy
problems in automated audio captioning: word selection indeterminacy and
sentence length indeterminacy. We simultaneously solve the main caption
generation and sub indeterminacy problems by estimating keywords and sentence
length through multi-task learning. We tested a simplified model of our
submission using the development-testing dataset. Our model achieved 20.7
SPIDEr score where that of the baseline system was 5.4.

该报告介绍参加 DCASE 2020 挑战 Task 6 的系统，侧重于使用多任务学习解决自动音频字幕生成中的不确定性问题，并通过在开发测试数据集上测试简化模型，实现了 20.7 的 SPIDEr 得分，远高于基线系统的 5.4。

NTT DCASE2020 Challenge Task 6 系统：使用关键字和句子长度估计的自动音频字幕

The NTT DCASE2020 Challenge Task 6 system: Automated Audio Captioning  with Keywords and Sentence Length Estimation

In this paper, we present the task description and discuss the results of the
DCASE 2020 Challenge Task 2: Unsupervised Detection of Anomalous Sounds for
Machine Condition Monitoring. The goal of anomalous sound detection (ASD) is to
identify whether the sound emitted from a target machine is normal or
anomalous. The main challenge of this task is to detect unknown anomalous
sounds under the condition that only normal sound samples have been provided as
training data. We have designed this challenge as the first benchmark of ASD
research, which includes a large-scale dataset, evaluation metrics, and a
simple baseline system. We received 117 submissions from 40 teams, and several
novel approaches have been developed as a result of this challenge. On the
basis of the analysis of the evaluation results, we discuss two new approaches
and their problems.

本文介绍了 DCASE 2020 Challenge 任务 2 的任务描述和结果，并讨论了无监督检测机器异常声音方面的研究。通过介绍了大规模数据集、评价指标和基线系统来设计此挑战赛，共收到了 40 个团队的 117 份提交，基于该挑战的结果分析，本文进一步讨论和分析了两种新方法和存在的问题。

DCASE2020 挑战赛任务 2: 无监督机器状态监测异声检测的描述与讨论

Description and Discussion on DCASE2020 Challenge Task2: Unsupervised  Anomalous Sound Detection for Machine Condition Monitoring

This paper presents the details of Task 1: Acoustic Scene Classification in
the DCASE 2020 Challenge. The task consists of two subtasks: classification of
data from multiple devices, requiring good generalization properties, and
classification using low-complexity solutions. Here we describe the datasets
and baseline systems. After the challenge submission deadline, challenge
results and analysis of the submissions will be added.

本文介绍了 DCASE 2020 挑战赛的任务 1：声学场景分类的详细信息，该任务包含两个子任务：使用多个设备的数据进行分类，需要良好的泛化性能，并且使用低复杂度解决方案进行分类。在这里，我们描述了数据集和基线系统。在挑战提交截止日期之后，将添加挑战结果和提交的分析。