About
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
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
||||||
|
||||||
|
Datasets
Greatest papers with code
PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
arXiv 2020
• qiuqiangkong/audioset_tagging_cnn
• 
However, there is limited research on pretraining neural networks on large datasets for audio pattern recognition.
ACOUSTIC SCENE CLASSIFICATION AUDIO TAGGING SCENE CLASSIFICATION SOUND EVENT DETECTION
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.
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.
ACTIVITY RECOGNITION DATA AUGMENTATION ENVIRONMENTAL SOUND CLASSIFICATION SOUND EVENT DETECTION
Adaptive pooling operators for weakly labeled sound event detection
In this work, we treat SED as a multiple instance learning (MIL) problem, where training labels are static over a short excerpt, indicating the presence or absence of sound sources but not their temporal locality.
MULTIPLE INSTANCE LEARNING SOUND EVENT DETECTION TIME SERIES
Sound event detection in domestic environments withweakly labeled data and soundscape synthesis
This paper presents Task 4 of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge and provides a first analysis of the challenge results.
Ranked #3 on
Sound Event Detection
on DESED
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.
A Closer Look at Weak Label Learning for Audio Events
• ankitshah009/WALNet-Weak_Label_Analysis
• 
In this work, we first describe a CNN based approach for weakly supervised training of audio events.
Sound Event Detection with Depthwise Separable and Dilated Convolutions
The number of the channels of the CNNs and size of the weight matrices of the RNNs have a direct effect on the total amount of parameters of the SED method, which is to a couple of millions.
Improving Sound Event Detection In Domestic Environments Using Sound Separation
Performing sound event detection on real-world recordings often implies dealing with overlapping target sound events and non-target sounds, also referred to as interference or noise.
Ranked #1 on
Sound Event Detection
on DESED
Training Sound Event Detection On A Heterogeneous Dataset
Training a sound event detection algorithm on a heterogeneous dataset including both recorded and synthetic soundscapes that can have various labeling granularity is a non-trivial task that can lead to systems requiring several technical choices.
Ranked #2 on
Sound Event Detection
on DESED