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Semi-supervised Acoustic Event Detection based on tri-training
[article]
2019
arXiv
pre-print
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Based on the classic tri-training approach, our proposed method shows accuracy improvement over both the supervised training baseline, and semisupervised self-training set-up, in all pre-defined acoustic ...
Recent acoustic event detectors are based on large-scale neural networks, which are typically trained with huge amounts of labeled data. ...
We propose an ensemble method based on the classic tri-training that shows improvements in all acoustic events we investigate in a realistic semi-supervised setting (Internet-scale unlabeled dataset with ...
arXiv:1904.12926v1
fatcat:sjx4qz4b3vccvp7ocwgn4nlvp4
Semi-supervised Acoustic Event Detection Based on Tri-training
2019
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Based on the classic tri-training approach, our proposed method shows accuracy improvement over both the supervised training baseline, and semisupervised self-training set-up, in all pre-defined acoustic ...
Recent acoustic event detectors are based on large-scale neural networks, which are typically trained with huge amounts of labeled data. ...
We propose an ensemble method based on the classic tri-training that shows improvements in all acoustic events we investigate in a realistic semi-supervised setting (Internet-scale unlabeled dataset with ...
doi:10.1109/icassp.2019.8683710
dblp:conf/icassp/ShiSKRMW19
fatcat:33op2g5rtbavjfjlyrjee5hziu
Audio Event and Scene Recognition: A Unified Approach using Strongly and Weakly Labeled Data
[article]
2017
arXiv
pre-print
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The primary problem domain focus of this paper is acoustic event and scene detection in audio recordings. We first propose a naive formulation for leveraging labeled data in both forms. ...
We then propose a more general framework for Supervised and Weakly Supervised Learning (SWSL). Based on this general framework, we propose a graph based approach for SWSL. ...
EXPERIMENTS AND RESULTS We evaluate the proposed supervised and weakly supervised learning on both audio event and acoustic scene detection tasks. ...
arXiv:1611.04871v3
fatcat:jclmmvkhzrhyfgjygjpxxafw6a
Peer Collaborative Learning for Polyphonic Sound Event Detection
[article]
2021
arXiv
pre-print
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This paper describes that semi-supervised learning called peer collaborative learning (PCL) can be applied to the polyphonic sound event detection (PSED) task, which is one of the tasks in the Detection ...
and Classification of Acoustic Scenes and Events (DCASE) challenge. ...
This paper adopts a knowledge distillation model based on semi-supervised learning called "peer collaborative learning" (PCL) [16] . ...
arXiv:2110.03511v1
fatcat:cbor4tqn2vadxbb3hrwbclfhle
Weakly Labeled Sound Event Detection Using Tri-training and Adversarial Learning
[article]
2019
arXiv
pre-print
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This paper considers a semi-supervised learning framework for weakly labeled polyphonic sound event detection problems for the DCASE 2019 challenge's task4 by combining both the tri-training and adversarial ...
Given this dataset, we apply the tri-training where two different classifiers are used to obtain pseudo labels on the weakly labeled and unlabeled dataset, and the final classifier is trained using the ...
This paper presents a sound event detection combining adversarial learning and tri-training. ...
arXiv:1910.06790v1
fatcat:x5kcng4kcncgfc3odugzmcghbm
An Approach for Self-Training Audio Event Detectors Using Web Data
[article]
2017
arXiv
pre-print
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Audio Event Detection (AED) aims to recognize sounds within audio and video recordings. AED employs machine learning algorithms commonly trained and tested on annotated datasets. ...
The audio event detectors are trained on the labeled audio and ran on the unlabeled audio downloaded from YouTube. ...
Unlabeled audio from videos can help audio event detection. arXiv:1609.06026v3 [cs.SD] 27 Jun 2017 II. SEMI-SUPERVISED SELF-TRAINING OF AUDIO EVENT DETECTORS
Fig. 1 . 1 Fig. 1. ...
arXiv:1609.06026v3
fatcat:cn4z3paieffphntfmt7ww756qy
Polyphonic Music Note Onset Detection Using Semi-Supervised Learning
2007
Zenodo
Preserved Fulltext
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We also tried training on the score-alignment data without bootstrapping. The results show it is important to have both a large data set and actual acoustic data. ...
Semi-supervised learning From score alignment, we obtain a large set of labeled training data, but the labels are based on rather large windows, and the chroma vector features are chosen more for gross ...
doi:10.5281/zenodo.1417384
fatcat:p5d27s5tlnchjkpsyyfj2uvfpa
An Approach For Self-Training Audio Event Detectors Using Web Data
2018
Zenodo
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Unlabeled audio from videos can help audio event detection. Fig. 1 . 1 Flow of the semi-supervised self-training of Audio Event Detection. ...
SEMI-SUPERVISED SELF-TRAINING OF AUDIO EVENT DETECTORS Semi-supervised self-training is an algorithm that iteratively re-trains a model and our particular framework is illustrated in Figure 1 . ...
doi:10.5281/zenodo.1159183
fatcat:k6kuonmwlrckzjwurzwomdpsg4
Detecting Gunshots Using Wearable Accelerometers
2014
PLoS ONE
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Existing monitoring systems rely heavily on location-based monitoring methods, which have incomplete geographic coverage and do not provide information on illegal firearm use. ...
These results suggest the feasibility of using inexpensive wearable sensors to detect firearm discharges. ...
Instead, a motion-based detection framework was chosen. ...
doi:10.1371/journal.pone.0106664
pmid:25184416
pmcid:PMC4153670
fatcat:p4q746w4xbg57bcom7d4shi554
Acoustic anomaly detection via latent regularized gaussian mixture generative adversarial networks
[article]
2020
arXiv
pre-print
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Acoustic anomaly detection aims at distinguishing abnormal acoustic signals from the normal ones. It suffers from the class imbalance issue and the lacking in the abnormal instances. ...
In this paper, a novel Gaussian Mixture Generative Adversarial Network (GMGAN) is proposed under semi-supervised learning framework, in which the underlying structure of training data is not only captured ...
anomaly detection to learn an regularized latent space by using adversarial training under semi-supervised learning framework. ...
arXiv:2002.01107v2
fatcat:r4saotwkrrhmzay3rcscrqwwqa
Semi-Supervised Deep Learning in High-Speed Railway Track Detection Based on Distributed Fiber Acoustic Sensing
2022
Sensors
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We propose a track detection method that innovatively leverages semi-supervised deep learning based on image recognition, with a particular pre-processing for the dataset and a greedy algorithm for the ...
Therefore, approaches based on optical sensors have become the most remarkable strategy in terms of deployment cost and real-time performance. ...
In this paper, we propose a track detection system that innovatively leverages semi-supervised deep learning based on image recognition. ...
doi:10.3390/s22020413
pmid:35062373
pmcid:PMC8779117
fatcat:k7lstxzn3jhmhozgefjmhlrwo4
Review of fall detection techniques: A data availability perspective
2017
Medical Engineering and Physics
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2016 pre-print arXiv:1605.09351v1 fatcat:2upigi2fezgxrb4zzw7cepaifeOther Versions
The taxonomy identifies different categories of classification methods for the study of fall detection based on the availability of their data during training the classifiers. ...
Therefore, standard supervised machine learning methods may not be directly applied to handle this problem. ...
Fahmi et al. [59] present a semi-supervised fall detection method using smartphones by first training a supervised algorithm using decision trees, then using fall profiles to develop a semi-supervised ...
doi:10.1016/j.medengphy.2016.10.014
pmid:27889391
fatcat:pdpd5om4pjfqhmavxaqxkogjz4
An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
2021
Applied Sciences
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The Sound Event Detection task aims to determine the temporal locations of acoustic events in audio clips. ...
the DCASE Challenge (Detection and Classification of Acoustic Scenes and Events). ...
Conformer-Based Sound Event Detection with
Semi-Supervised Learning and Data Augmentation. ...
doi:10.3390/app112311561
fatcat:eejcrjkkxvfwlkrppl7xztsbiu
Semi-supervised Triplet Loss Based Learning of Ambient Audio Embeddings
2019
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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In this paper, we combine unsupervised and supervised triplet loss based learning into a semi-supervised representation learning approach. ...
They have compared this approach to supervised learning. However, in real situations, it is common to have a small labeled dataset and a large unlabeled one. ...
All classifiers are trained on the 1,578 labeled files from DCASE 2018 task 4 training set. ...
doi:10.1109/icassp.2019.8683774
dblp:conf/icassp/TurpaultSV19
fatcat:f4pcyw36gbh2fd4sqwxi3jkkem
Open Set Audio Classification Using Autoencoders Trained on Few Data
2020
Sensors
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on latent space representations to detect known classes and reject unwanted ones. ...
on transfer learning. ...
Results(%) of the proposed frameworks using DCASE (Detection and Classification of Acoustic Scenes and Events) 2019 Task 1C dataset. ...
doi:10.3390/s20133741
pmid:32635378
pmcid:PMC7374438
fatcat:nl3iuuijpjhnxic6nyvkgm57ba
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