1 Ocak 2022 Konferans bildirisi Açık Erişim

Sun, Jianyuan; Liu, Xubo; Mei, Xinhao; Zhao, Jinzheng; Plumbley, Mark D.; Kilic, Volkan; Wang, Wenwu

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        "name": "Sun, Jianyuan"
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        "affiliation": "Univ Surrey, Ctr Vis Speech & Signal Proc CVSSP, Surrey, England", 
        "name": "Liu, Xubo"
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      {
        "affiliation": "Univ Surrey, Ctr Vis Speech & Signal Proc CVSSP, Surrey, England", 
        "name": "Mei, Xinhao"
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      {
        "affiliation": "Univ Surrey, Ctr Vis Speech & Signal Proc CVSSP, Surrey, England", 
        "name": "Zhao, Jinzheng"
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      {
        "affiliation": "Univ Surrey, Ctr Vis Speech & Signal Proc CVSSP, Surrey, England", 
        "name": "Plumbley, Mark D."
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      {
        "affiliation": "Izmir Katip Celebi Univ, Dept Elect & Elect Engn, Izmir, Turkey", 
        "name": "Kilic, Volkan"
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      {
        "affiliation": "Univ Surrey, Ctr Vis Speech & Signal Proc CVSSP, Surrey, England", 
        "name": "Wang, Wenwu"
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    "description": "Acoustic scene classification (ASC) aims to classify an audio clip based on the characteristic of the recording environment. In this regard, deep learning based approaches have emerged as a useful tool for ASC problems. Conventional approaches to improving the classification accuracy include integrating auxiliary methods such as attention mechanism, pre-trained models and ensemble multiple sub-networks. However, due to the complexity of audio clips captured from different environments, it is difficult to distinguish their categories without using any auxiliary methods for existing deep learning models using only a single classifier. In this paper, we propose a novel approach for ASC using deep neural decision forest (DNDF). DNDF combines a fixed number of convolutional layers and a decision forest as the final classifier. The decision forest consists of a fixed number of decision tree classifiers, which have been shown to offer better classification performance than a single classifier in some datasets. In particular, the decision forest differs substantially from traditional random forests as it is stochastic, differentiable, and capable of using the back-propagation to update and learn feature representations in neural network. Experimental results on the DCASE2019 and ESC-50 datasets demonstrate that our proposed DNDF method improves the ASC performance in terms of classification accuracy and shows competitive performance as compared with state-of-the-art baselines.", 
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      "title": "2022 30TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2022)"
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    "science_branches": [
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    "title": "Deep Neural Decision Forest for Acoustic Scene Classification"
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