Audio content analysis in terms of sound events is an important research
problem for a variety of applications. Recently, the development of weak
labeling approaches for audio or sound event detection (AED) and availability
of large scale weakly labeled dataset have finally opened up the possibility of
large scale AED. However, a deeper understanding of how weak labels affect the
learning for sound events is still missing from literature. In this work, we
first describe a CNN based approach for weakly supervised training of audio
events. The approach follows some basic design principle desirable in a
learning method relying on weakly labeled audio. We then describe important
characteristics, which naturally arise in weakly supervised learning of sound
events. We show how these aspects of weak labels affect the generalization of
models. More specifically, we study how characteristics such as label density
and corruption of labels affects weakly supervised training for audio events.
We also study the feasibility of directly obtaining weak labeled data from the
web without any manual label and compare it with a dataset which has been
manually labeled. The analysis and understanding of these factors should be
taken into picture in the development of future weak label learning methods.
Audioset, a large scale weakly labeled dataset for sound events is used in our
experiments.

本研究使用基于 CNN 的方法进行弱监督下的音频事件训练，研究了弱标签密度和标签纠错对模型训练的影响，还探索了从网络直接获取弱标签数据的可行性，并与手动标记数据进行了比较，这些因素的分析和理解应考虑在开发未来的弱标签学习方法中。

音频事件弱标签学习的深入研究

A Closer Look at Weak Label Learning for Audio Events

Descriptions are often provided along with recommendations to help users'
discovery. Recommending automatically generated music playlists (e.g.
personalised playlists) introduces the problem of generating descriptions. In
this paper, we propose a method for generating music playlist descriptions,
which is called as music captioning. In the proposed method, audio content
analysis and natural language processing are adopted to utilise the information
of each track.

本研究提出了一种音乐播放列表描述生成方法，使用音频内容分析和自然语言处理技术，称为音乐字幕生成。