NELS-Never-Ending Learner of Sounds

Published in Neural Information Processing Systems (NIPS 2017), 2018

Paper Link

Sounds are essential to how humans perceive and interact with the world. These 10 sounds are captured in recordings and shared on the Internet on a minute-by- 11 minute basis. These recordings, which are predominantly videos, constitute the largest archive of sounds we’ve ever seen. However, most of these recordings have undescribed content making necessary methods for automatic audio content 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

Citation: Elizalde, Benjamin, Rohan Badlani, Ankit Shah, Anurag Kumar, and Bhiksha Raj. “NELS-Never-Ending Learner of Sounds.”