Guided Learning Convolution System for DCASE 2019 Task 4

@inproceedings{Lin2019GuidedLC,
  title={Guided Learning Convolution System for DCASE 2019 Task 4},
  author={Liwei Lin and Xiangdong Wang and Hong Liu and Yueliang Qian},
  booktitle={DCASE},
  year={2019}
}
In this paper, we describe in detail the system we submitted to DCASE2019 task 4: sound event detection (SED) in domestic environments. We employ a convolutional neural network (CNN) with an embedding-level attention pooling module to solve it. By considering the interference caused by the co-occurrence of multiple events in the unbalanced dataset, we utilize the disentangled feature to raise the performance of the model. To take advantage of the unlabeled data, we adopt Guided Learning for… 
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References

SHOWING 1-10 OF 16 REFERENCES
Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection
TLDR
Experiments show that the proposed SDS and DF significantly improve the detection performance of the embedding-level MIL approach with an attention pooling module and outperform the first place system in the challenge by $\mathbf {6.6}$ percentage points.
Sound Event Detection in Domestic Environments with Weakly Labeled Data and Soundscape Synthesis
TLDR
The paper introduces Domestic Environment Sound Event Detection (DESED) dataset mixing a part of last year dataset and an additional synthetic, strongly labeled, dataset provided this year that’s described more in detail.
What you need is a more professional teacher
TLDR
This work designs two extremely different models for different targets, one of which just pursues finer information for the final target, and one which is more professional to achieve higher coarse-level classification accuracy.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
TLDR
Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.
Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments
TLDR
This paper presents DCASE 2018 task 4.0, which evaluates systems for the large-scale detection of sound events using weakly labeled data (without time boundaries) and explores the possibility to exploit a large amount of unbalanced and unlabeled training data together with a small weakly labeling training set to improve system performance.
Guided Learning for the combination of weakly-supervised and semi-supervised learning
TLDR
This work presents an end-to-end semi-supervised learning process termed Guided Learning for these two different models to improve the training efficiency and presents a new approach which outperforms the first place result on DCASE2018 Task 4 which employs Mean Teacher with a well-design CRNN network.
Adam: A Method for Stochastic Optimization
TLDR
This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Audio Set: An ontology and human-labeled dataset for audio events
TLDR
The creation of Audio Set is described, a large-scale dataset of manually-annotated audio events that endeavors to bridge the gap in data availability between image and audio research and substantially stimulate the development of high-performance audio event recognizers.
Scaper: A library for soundscape synthesis and augmentation
TLDR
Given a collection of iso-lated sound events, Scaper acts as a high-level sequencer that can generate multiple soundscapes from a single, probabilistically defined, “specification”, to increase the variability of the output.
Freesound Datasets: A Platform for the Creation of Open Audio Datasets
Comunicacio presentada al 18th International Society for Music Information Retrieval Conference celebrada a Suzhou, Xina, del 23 al 27 d'cotubre de 2017.
...
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