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
Benchmarks
These leaderboards are used to track progress in Sound Event Detection
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Libraries
Use these libraries to find Sound Event Detection models and implementationsDatasets
DCASE 2016
DCASE 2013
FSDnoisy18k
DESED
TUT Sound Events 2017
TUT-SED Synthetic 2016
L3DAS22
L3DAS21
BirdVox-full-night
Most implemented papers
Towards Deep Learning Models Resistant to Adversarial Attacks
MadryLab/mnist_challenge
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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
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
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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
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
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
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
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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
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
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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
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In this work, we first describe a CNN based approach for weakly supervised training of audio events.
