SanteMusiqueV1, Main, Exploration, bibRecord, 000B71

Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments.

Identifieur interne : 000B71 ( Main/Exploration ); précédent : 000B70; suivant : 000B72

Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments.

Auteurs : Wenjing Han [République populaire de Chine] ; Eduardo Coutinho [Royaume-Uni] ; Huabin Ruan [République populaire de Chine] ; Haifeng Li [République populaire de Chine] ; Björn Schuller [Royaume-Uni, République populaire de Chine, Allemagne] ; Xiaojie Yu [République populaire de Chine] ; Xuan Zhu [République populaire de Chine]

Source :

PloS one [ 1932-6203 ] ; 2016.

RBID : pubmed:27627768

Descripteurs français

KwdFr :
- Acoustique (MeSH), Algorithmes (MeSH), Apprentissage machine (MeSH), Apprentissage machine supervisé (MeSH), Apprentissage par problèmes (méthodes), Classification (MeSH), Humains (MeSH), Son (physique) (MeSH).
MESH :
- méthodes : Apprentissage par problèmes.
- Acoustique, Algorithmes, Apprentissage machine, Apprentissage machine supervisé, Classification, Humains, Son (physique).

English descriptors

KwdEn :
- Acoustics (MeSH), Algorithms (MeSH), Classification (MeSH), Humans (MeSH), Machine Learning (MeSH), Problem-Based Learning (methods), Sound (MeSH), Supervised Machine Learning (MeSH).
MESH :
- methods : Problem-Based Learning.
- Acoustics, Algorithms, Classification, Humans, Machine Learning, Sound, Supervised Machine Learning.

Abstract

Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances.

DOI: 10.1371/journal.pone.0162075
PubMed: 27627768
PubMed Central: PMC5023122

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Main, to step Corpus: 000B15
to stream Main, to step Curation: 000B15

Le document en format XML

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<front><div type="abstract" xml:lang="en">Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances. </div>
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<name sortKey="Schuller, Bjorn" sort="Schuller, Bjorn" uniqKey="Schuller B" first="Björn" last="Schuller">Björn Schuller</name>
<name sortKey="Yu, Xiaojie" sort="Yu, Xiaojie" uniqKey="Yu X" first="Xiaojie" last="Yu">Xiaojie Yu</name>
<name sortKey="Zhu, Xuan" sort="Zhu, Xuan" uniqKey="Zhu X" first="Xuan" last="Zhu">Xuan Zhu</name>
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<country name="Royaume-Uni"><noRegion><name sortKey="Coutinho, Eduardo" sort="Coutinho, Eduardo" uniqKey="Coutinho E" first="Eduardo" last="Coutinho">Eduardo Coutinho</name>
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<name sortKey="Coutinho, Eduardo" sort="Coutinho, Eduardo" uniqKey="Coutinho E" first="Eduardo" last="Coutinho">Eduardo Coutinho</name>
<name sortKey="Schuller, Bjorn" sort="Schuller, Bjorn" uniqKey="Schuller B" first="Björn" last="Schuller">Björn Schuller</name>
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<country name="Allemagne"><noRegion><name sortKey="Schuller, Bjorn" sort="Schuller, Bjorn" uniqKey="Schuller B" first="Björn" last="Schuller">Björn Schuller</name>
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   |wiki=    Sante
   |area=    SanteMusiqueV1
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   |étape=   Exploration
   |type=    RBID
   |clé=     pubmed:27627768
   |texte=   Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments.
}}

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	Serveur d'exploration Santé et pratique musicale
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Serveur d'exploration Santé et pratique musicale

Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments.

Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments.

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Abstract

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