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.

arXiv commentto appear in IEEE Signal Processing Magazine, Volume 38, Issue 5

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• PHNNs: Lightweight Neural Networks via Parameterized Hypercomplex Convolutions
• A Squeeze-and-Excitation and Transformer based Cross-task System for Environmental Sound Recognition
• Spectrogram features for audio and speech analysis
• 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
• Sub-band Domain Multi-Hypothesis Acoustic Echo Canceler Based Acoustic Scene Analysis