  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
