RESEARCH

A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition

July 13, 2020

Abstract

An important problem in machine auditory perception is to recognize and detect sound events. In this paper, we propose a sequential self-teaching approach to learning sounds. Our main proposition is that it is harder to learn sounds in adverse situations such as from weakly labeled and/or noisy labeled data, and in these situations a single stage of learning is not sufficient. Our proposal is a sequential stage-wise learning process that improves generalization capabilities of a given modeling system. We justify this method via technical results and on Audioset, the largest sound events dataset, our sequential learning approach can lead to up to 9% improvement in performance. A comprehensive evaluation also shows that the method leads to improved transferability of knowledge from previously trained models, thereby leading to improved generalization capabilities on transfer learning tasks.

Download the Paper

AUTHORS

Written by

Anurag Kumar

Vamsi Krishna Ithapu

Publisher

International Conference on Machine Learning (ICML)

Research Topics

Artificial Intelligence

Related Publications

June 02, 2019

Simple Attention-Based Representation Learning for Ranking Short Social Media Posts | Facebook AI Research

This paper explores the problem of ranking short social media posts with respect to user queries using neural networks. Instead of starting with a complex architecture, we proceed from the bottom up and examine the effectiveness of a simple,…

Peng Shi, Jinfeng Rao, Jimmy Lin

June 02, 2019

June 09, 2019

THEORY

First-order Adversarial Vulnerability of Neural Networks and Input Dimension | Facebook AI Research

Over the past few years, neural networks were proven vulnerable to adversarial images: targeted but imperceptible image perturbations lead to drastically different predictions. We show that adversarial vulnerability increases with the gradients…

Carl-Johann Simon-Gabriel, Yann Ollivier, Bernhard Scholkopf, Leon Bottou, David Lopez-Paz

June 09, 2019

May 31, 2019

INTEGRITY

Abusive Language Detection with Graph Convolutional Networks | Facebook AI Research

Abuse on the Internet represents a significant societal problem of our time. Previous research on automated abusive language detection in Twitter has shown that community-based profiling of users is a promising technique for this task. However,…

Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, Ekaterina Shutova

May 31, 2019

June 01, 2019

Probabilistic Planning with Reduced Models | Facebook AI Research

Reduced models are simplified versions of a given domain, designed to accelerate the planning process. Interest in reduced models has grown since the surprising success of determinization in the first international probabilistic planning…

Luis Pineda, Shlomo Zilberstein

June 01, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.