Sound Event Detection: A Tutorial

Abstract

The goal of automatic sound event detection (SED) methods is to recognize what is happening in an audio signal and when it is happening. In practice, the goal is to recognize at what temporal instances different sounds are active within an audio signal. This paper gives a tutorial presentation of sound event detection, including its definition, signal processing and machine learning approaches, evaluation, and future perspectives.

to appear in IEEE Signal Processing Magazine, Volume 38, Issue 5

References in corpus (5)

• Distilling the Knowledge in a Neural Network
• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Acoustic Scene Classification
• A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection
• Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking

Cited by in corpus (2)

• Improving Polyphonic Sound Event Detection on Multichannel Recordings with the Sørensen-Dice Coefficient Loss and Transfer Learning
• Evaluating robustness of You Only Hear Once(YOHO) Algorithm on noisy audios in the VOICe Dataset