Sound Event Detection
94 papers with code • 5 benchmarks • 20 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
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.
PHNNs: Lightweight Neural Networks via Parameterized Hypercomplex Convolutions
In this paper, we define the parameterization of hypercomplex convolutional layers and introduce the family of parameterized hypercomplex neural networks (PHNNs) that are lightweight and efficient large-scale models.
WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research
To address this data scarcity issue, we introduce WavCaps, the first large-scale weakly-labelled audio captioning dataset, comprising approximately 400k audio clips with paired captions.
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).
Adaptive pooling operators for weakly labeled sound event detection
marl/autopool
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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.
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
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-Random-Consistency-Training
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Sound event detection (SED), as a core module of acoustic environmental analysis, suffers from the problem of data deficiency.
