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
L3DAS22
DESED
DCASE 2013
TUT Sound Events 2017
TUT-SED Synthetic 2016
L3DAS21
Most implemented papers
Guided learning for weakly-labeled semi-supervised sound event detection
Kikyo-16/Sound_event_detection
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Instead of designing a single model by considering a trade-off between the two sub-targets, we design a teacher model aiming at audio tagging to guide a student model aiming at boundary detection to learn using the unlabeled data.
Evaluation of post-processing algorithms for polyphonic sound event detection
We compared post-processing algorithms on the temporal prediction curves of two models: one based on the challenge's baseline and a Multiple Instance Learning (MIL) model.
Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled Sampling
dr-costas/SEDLM
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Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 2019
On the contrary, with our method there is a decrease of 4% at F1 score and an increase of 7% at ER for the TUT-SED Synthetic 2016 dataset.
City classification from multiple real-world sound scenes
In this paper, we undertake the task of automatic city classification to ask whether we can recognize a city from a set of sound scenes?
Guided Learning Convolution System for DCASE 2019 Task 4
Kikyo-16/Sound_event_detection
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In this paper, we describe in detail the system we submitted to DCASE2019 task 4: sound event detection (SED) in domestic environments.
Musical Instrument Playing Technique Detection Based on FCN: Using Chinese Bowed-Stringed Instrument as an Example
The effectiveness of the proposed framework is tested on a new dataset, its categorization of techniques is similar to our training dataset.
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.
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.
Memory Controlled Sequential Self Attention for Sound Recognition
In this paper we investigate the importance of the extent of memory in sequential self attention for sound recognition.
Multi-Task Learning for Interpretable Weakly Labelled Sound Event Detection
soham97/MTL_Weakly_labelled_audio_data
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Weakly Labelled learning has garnered lot of attention in recent years due to its potential to scale Sound Event Detection (SED) and is formulated as Multiple Instance Learning (MIL) problem.
