Sound Event Detection

46 papers with code • 4 benchmarks • 17 datasets

Sound Event Detection (SED) is the task of recognizing the sound events and their respective temporal start and end time in a recording. Sound events in real life do not always occur in isolation, but tend to considerably overlap with each other. Recognizing such overlapping sound events is referred as polyphonic SED.

Source: A report on sound event detection with different binaural features

Libraries

Use these libraries to find Sound Event Detection models and implementations

Most implemented papers

Towards Deep Learning Models Resistant to Adversarial Attacks

MadryLab/mnist_challenge ICLR 2018

Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal.

Lightweight Convolutional Neural Networks By Hypercomplex Parameterization

elegan23/hypernets 8 Oct 2021

Hypercomplex neural networks have proved to reduce the overall number of parameters while ensuring valuable performances by leveraging the properties of Clifford algebras.

Recurrent Neural Networks for Polyphonic Sound Event Detection in Real Life Recordings

yardencsGitHub/tf_syllable_segmentation_annotation 4 Apr 2016

In this paper we present an approach to polyphonic sound event detection in real life recordings based on bi-directional long short term memory (BLSTM) recurrent neural networks (RNNs).

Learning Sound Event Classifiers from Web Audio with Noisy Labels

edufonseca/icassp19 4 Jan 2019

To foster the investigation of label noise in sound event classification we present FSDnoisy18k, a dataset containing 42. 5 hours of audio across 20 sound classes, including a small amount of manually-labeled data and a larger quantity of real-world noisy data.

SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks

giusenso/seld-tcn 3 Mar 2020

The understanding of the surrounding environment plays a critical role in autonomous robotic systems, such as self-driving cars.

ACCDOA: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization and Detection

sharathadavanne/seld-dcase2021 29 Oct 2020

Conventional NN-based methods use two branches for a sound event detection (SED) target and a direction-of-arrival (DOA) target.

Couple Learning for semi-supervised sound event detection

Toshiba-China-RDC/dcase20_task4 12 Oct 2021

The recently proposed Mean Teacher method, which exploits large-scale unlabeled data in a self-ensembling manner, has achieved state-of-the-art results in several semi-supervised learning benchmarks.

RCT: Random Consistency Training for Semi-supervised Sound Event Detection

audio-westlakeu/rct 21 Oct 2021

Sound event detection (SED), as a core module of acoustic environmental analysis, suffers from the problem of data deficiency.

Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection

cchinchristopherj/Right-Whale-Unsupervised-Model 21 Feb 2017

Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure.

A Closer Look at Weak Label Learning for Audio Events

ankitshah009/WALNet-Weak_Label_Analysis 24 Apr 2018

In this work, we first describe a CNN based approach for weakly supervised training of audio events.