Sound Event Detection
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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.
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
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.
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.
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, we focus on two common problems on SED: how to carry out efficient weakly-supervised learning and how to learn better from the unbalanced dataset in which multiple sound events often occur in co-occurrence.
MULTI-LABEL CLASSIFICATION MULTIPLE INSTANCE LEARNING SOUND EVENT DETECTION
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.
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.
Recurrent Neural Networks for Polyphonic Sound Event Detection in Real Life Recordings
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).
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.
Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled Sampling
Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 2019
• dr-costas/SEDLM
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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.
LANGUAGE MODELLING MACHINE TRANSLATION SOUND EVENT DETECTION