Sounds are essential to how humans perceive and interact with the world and are captured in recordings and shared on the Internet on a minute-by-minute basis. These recordings, which are predominantly videos, constitute the largest archive of sounds we know. However, most of these recordings have undescribed content making necessary methods for automatic sound analysis, indexing and retrieval. These methods have to address multiple challenges, such as the relation between sounds and language, numerous and diverse sound classes, and large-scale evaluation. We propose a system that continuously learns from the web relations between sounds and language, improves sound recognition models over time and evaluates its learning competency in the large-scale without references. We introduce the Never-Ending Learner of Sounds (NELS), a project for continuously learning of sounds and their associated knowledge, available on line in nels.cs.cmu.edu

翻译：声音对人类感知和与世界互动至关重要，它们被捕捉在录音中，并每分钟都在互联网上分享。这些录音（主要是视频）构成了我们已知最大的声音档案。然而，这些录音中的大多数内容未经描述，因此需要开发自动声音分析、索引和检索的方法。这些方法必须应对多重挑战，例如声音与语言之间的关系、声音类别的多样性和广泛性，以及大规模评估。我们提出一个系统，它持续从网络上学到声音与语言之间的关系，随时间改进声音识别模型，并在无参考的情况下大规模评估其学习能力。我们介绍了永不停止的声学学习器（NELS），这是一个持续学习声音及其相关知识的项目，在线访问地址为nels.cs.cmu.edu。