Sound Event Detection
74 papers with code • 4 benchmarks • 18 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
FSDnoisy18k
DESED
L3DAS22
DCASE 2013
TUT Sound Events 2017
TUT-SED Synthetic 2016
L3DAS21
Most implemented papers
Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks
In order to tackle both clip-level and frame-level tasks, this paper proposes Audio Teacher-Student Transformer (ATST), with a clip-level version (named ATST-Clip) and a frame-level version (named ATST-Frame), responsible for learning clip-level and frame-level representations, respectively.
Performance and energy balance: a comprehensive study of state-of-the-art sound event detection systems
In recent years, deep learning systems have shown a concerning trend toward increased complexity and higher energy consumption.
Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks
sdeva14/eusipco17-drone-sound-detection
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This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their sound data.
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.
Ubicoustics: Plug-and-Play Acoustic Activity Recognition
Despite sound being a rich source of information, computing devices with microphones do not leverage audio to glean useful insights about their physical and social context.
Polyphonic Sound Event Detection by using Capsule Neural Network
Artificial sound event detection (SED) has the aim to mimic the human ability to perceive and understand what is happening in the surroundings.
Learning Sound Events From Webly Labeled Data
anuragkr90/webly-labeled-sounds
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28th International Joint Conference on Artificial Intelligence 2019
In the last couple of years, weakly labeled learning for sound events has turned out to be an exciting approach for audio event detection.
Robust sound event detection in bioacoustic sensor networks
As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise.
Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection
Kikyo-16/Sound_event_detection
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In this paper, a special decision surface for the weakly-supervised sound event detection (SED) and a disentangled feature (DF) for the multi-label problem in polyphonic SED are proposed.
