English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 63753/63753 (100%)
造訪人次 : 19203464      線上人數 : 268
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋


    請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/77387


    題名: 深度類神經網路於環境音偵測之應用與改良;The Applications and Improvements of Deep Neural Networks in Environmental Sound Recognition
    作者: 林郁凱;Lin, Yu-Kai
    貢獻者: 軟體工程研究所
    關鍵詞: 深度神經網路;卷積神經網路;環境音偵測;特徵融合;Deep Neuron network;Convolutional Neuron Network;Environmental Sound Recognition;Feature Combination
    日期: 2018-08-23
    上傳時間: 2018-08-31 14:36:22 (UTC+8)
    出版者: 國立中央大學
    摘要: 類神經網路已能在聲音辨識上取得極好的成績,多種不同的聲音特徵都被嘗試作為網路的輸入進行訓練辨識,然而以原始聲音訊號作為網路輸入,測試神經網路是否能夠自行擷取出聲音特徵依舊是一門挑戰。本文改良了現有原始訊號網路的架構,利用高層數的深度神經網路成功提升了訊號輸入分析的效果,以擬似頻譜轉換的方式,探討正確的參數設定,最終提出的1d-2d network 於ESC50中可成功達到73.55%的正確率。
    除此之外,本文亦提出一種特徵融合的網路架構,利用全域池化層的特性,整合出一種較具彈性的結合方式。利用此類網路,本文成功結合了利用原始訊號輸入及利用對數梅爾頻譜係數輸入的兩種網路,我們提出的ParallelNet在ESC50中以上述輸入得到了81.55%的辨識效果,達到了人類辨識水平。
    ;Neural network has achieved a great result in the sound recognition, many different kinds of acoustic features have been tried as the training input with the network. However, there is still under doubt that the whether the neural network could efficiently extract features from the raw audio signal input. This study improved the raw-signal-input network from other researches, with the deeper network architectures, the raw signals get the well analysis with our network, we also make the discussion in several kinds of network settings, with the spectrogram-like conversion, our network could reach the accuracy of 73.55% in the open-audio-dataset ESC50.
    Besides, in this study, we proposed a network architectures that could combine different kinds of networks feed with different features. With the help of global pooling, a flexible fusion way is well integrated into the network. Our experiment successfully combined two different networks which use different kinds of audio feature inputs—raw audio signal and log-mel spectrum. By the above settings, the ParallelNet we proposed finally reaches the accuracy of 81.55% in ESC50, which also reaches the recognition level of human being.
    顯示於類別:[軟體工程研究所 ] 博碩士論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    index.html0KbHTML29檢視/開啟


    在NCUIR中所有的資料項目都受到原著作權保護.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回饋  - 隱私權政策聲明