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題名 混合人聲之聲音場景辨識
Classification of Acoustic Scenes with Mixtures of Human Voice and Background Audio作者 李御國
Li, Yu-Guo貢獻者 廖文宏
Liao, Wen-Hung
李御國
Li, Yu-Guo關鍵詞 卷積神經網路
DCASE音訊資料集
聲音場景辨識
線上身份驗證
Voice-based Online Identity Verification
Convolutional Neural Network
DCASE Dataset
Acoustic Scene Classification日期 2020 上傳時間 2-九月-2020 13:15:07 (UTC+8) 摘要 日常生活環境週遭聲音，從來不是單獨事件，而是多種音源重疊在一起，使得環境音辨識充滿了各種挑戰。本研究以DCASE2016 比賽Task1所提供的音訊資料，包括海邊(Beach)與輕軌電車(Tram)等共15種場景的環境錄音為基礎，搭配16位人聲進行合成，針對混合人聲後的場景進行分析與辨識。聲音特徵萃取採用了普遍使用於聲音辨識的對數梅爾頻譜(Log-Mel Spectrogram)，用以保留最多聲音特徵，並利用卷積神經網路(CNN)來分辨出這些相互疊合聲音場景，整體平均辨識率達79%，於車輛(Car)類別辨識率可達93%，希望能將其運用在線上身份驗證之聲紋辨識的前處理階段。
The sounds around the environment of daily life are never separate events but consist of overlapping audio sources, making environmental sound recognition a challenging issue. This research employs audio data provided by Task1 of the Detection and Classification of Acoustic Scenes and Events 2016 (DCASE2016) competition, including environmental recordings of 15 scenes in different settings such as beach and tram. They are mixed with 16 human voices to create a new dataset. Acoustic features are extracted from the Log-Mel spectrogram, which is commonly used in voice recognition to retain the most distinct sound properties. Convolutional neural network (CNN) is employed to distinguish these overlapping sound scenes. We achiveve an overall accuracy of 79% and 93% accudacy in the ‘car’ scene. We expect the outcome to be applied as the pre-processing stage of voice-based online identity verification.參考文獻 [1] ESC Dataset https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YDEPUT[2] UrbanSound8Khttps://urbansounddataset.weebly.com/urbansound8k.html[3] DCASE Challengehttp://dcase.community/[4] Liao, Wen-Hung, Jin-Yao Wen, and Jen-Ho Kuo. "Streaming audio classification in smart home environments." The First Asian Conference on Pattern Recognition. IEEE, 2011.[5] Nordby, Jon Opedal. Environmental sound classification on microcontrollers using Convolutional Neural Networks. MS thesis. Norwegian University of Life Sciences, Ås, 2019.[6] Wu, Yuzhong, and Tan Lee. "Enhancing sound texture in CNN-based acoustic scene classification." ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019.[7] Salamon, Justin, and Juan Pablo Bello. "Deep convolutional neural networks and data augmentation for environmental sound classification." IEEE Signal Processing Letters 24.3 (2017): 279-283.[8] Dai Wei, Juncheng Li, et al. "Acoustic scene recognition with deep neural networks (DCASE challenge 2016)." Robert Bosch Research and Technology Center 3 (2016).[9] Hussain, Khalid, Mazhar Hussain, and Muhammad Gufran Khan. "An Improved Acoustic Scene Classification Method Using Convolutional Neural Networks (CNNs)." American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) 44.1 (2018): 68-76.[10] Han, Yoonchang, and Kyogu Lee. "Acoustic scene classification using convolutional neural network and multiple-width frequency-delta data augmentation." arXiv preprint arXiv:1607.02383 (2016).[11] Kim, Jaehun, and Kyogu Lee. "Empirical study on ensemble method of deep neural networks for acoustic scene classification." Proc. of IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) (2016).[12] Santoso, Andri, Chien-Yao Wang, and Jia-Ching Wang. Acoustic scene classification using network-in-network based convolutional neural network. DCASE2016 Challenge, Tech. Rep, 2016.[13] Becker, Sören, et al. "Interpreting and explaining deep neural networks for classification of audio signals." arXiv preprint arXiv:1807.03418 (2018).[14] Keren, Gil, and Björn Schuller. "Convolutional RNN: an enhanced model for extracting features from sequential data." 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016.[15] CH.Tseng，初探卷積神經網路https://chtseng.wordpress.com/2017/09/12/%E5%88%9D%E6%8E%A2%E5%8D%B7%E7%A9%8D%E7%A5%9E%E7%B6%93%E7%B6%B2%E8%B7%AF/[16] Lin, Min, Qiang Chen, and Shuicheng Yan. "Network in network." arXiv preprint arXiv:1312.4400 (2013).[17] Y. LeCun, Y. Bengio, G. Hinton, L. Y., B. Y., and H. G., “Deep learning,” Nature, vol. 521,no. 7553, pp. 436–444, 2015.[18] NVIDIA DIGITShttps://developer.nvidia.com/digits[19] Kerashttps://keras.io/[20] François Chollet，Deep learning 深度學習必讀：Keras 大神帶你用 Python 實作，旗標，ISBN：9789863125501，2019[21] 郭秋田等，多媒體導論與應用第三版，旗標，ISBN:9574426246，2008。[22] 丁建均，時頻分析近年來的發展http://www.ancad.com.tw/Training/ppt_download/%E4%B8%81%E5%BB%BA%E5%9D%87%E6%95%99%E6%8E%880628.pdf[23] Pu Sun, “Comparison of STFT and Wavelet Transform in Timefrequency Analysis”,2014.[24] Solovyev, Roman A., et al. "Deep Learning Approaches for Understanding Simple Speech Commands." arXiv preprint arXiv:1810.02364 (2018).[25] Librosahttps://librosa.github.io/librosa/feature.html[26] Pydub, AudioSegmenthttps://github.com/jiaaro/pydub[27] Sklearn.preprocessing.StandardScalerhttps://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html[28] description of acoustic scene classes in TUT Acoustic scenes 2016 dataset.http://www.cs.tut.fi/sgn/arg/dcase2016/acoustic-scenes 描述 碩士
國立政治大學
資訊科學系碩士在職專班
105971016資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105971016 資料類型 thesis dc.contributor.advisor 廖文宏 zh_TW dc.contributor.advisor Liao, Wen-Hung en_US dc.contributor.author (作者) 李御國 zh_TW dc.contributor.author (作者) Li, Yu-Guo en_US dc.creator (作者) 李御國 zh_TW dc.creator (作者) Li, Yu-Guo en_US dc.date (日期) 2020 en_US dc.date.accessioned 2-九月-2020 13:15:07 (UTC+8) - dc.date.available 2-九月-2020 13:15:07 (UTC+8) - dc.date.issued (上傳時間) 2-九月-2020 13:15:07 (UTC+8) - dc.identifier (其他 識別碼) G0105971016 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131936 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系碩士在職專班 zh_TW dc.description (描述) 105971016 zh_TW dc.description.abstract (摘要) 日常生活環境週遭聲音，從來不是單獨事件，而是多種音源重疊在一起，使得環境音辨識充滿了各種挑戰。本研究以DCASE2016 比賽Task1所提供的音訊資料，包括海邊(Beach)與輕軌電車(Tram)等共15種場景的環境錄音為基礎，搭配16位人聲進行合成，針對混合人聲後的場景進行分析與辨識。聲音特徵萃取採用了普遍使用於聲音辨識的對數梅爾頻譜(Log-Mel Spectrogram)，用以保留最多聲音特徵，並利用卷積神經網路(CNN)來分辨出這些相互疊合聲音場景，整體平均辨識率達79%，於車輛(Car)類別辨識率可達93%，希望能將其運用在線上身份驗證之聲紋辨識的前處理階段。 zh_TW dc.description.abstract (摘要) The sounds around the environment of daily life are never separate events but consist of overlapping audio sources, making environmental sound recognition a challenging issue. This research employs audio data provided by Task1 of the Detection and Classification of Acoustic Scenes and Events 2016 (DCASE2016) competition, including environmental recordings of 15 scenes in different settings such as beach and tram. They are mixed with 16 human voices to create a new dataset. Acoustic features are extracted from the Log-Mel spectrogram, which is commonly used in voice recognition to retain the most distinct sound properties. Convolutional neural network (CNN) is employed to distinguish these overlapping sound scenes. We achiveve an overall accuracy of 79% and 93% accudacy in the ‘car’ scene. We expect the outcome to be applied as the pre-processing stage of voice-based online identity verification. en_US dc.description.tableofcontents 第一章 緒論 11.1 研究動機 11.2 論文架構 4第二章 相關研究 52.1 文獻探討 52.2 工具探討 9第三章 研究方法 123.1 基本構想 123.2 前期研究 133.2.1 音訊輸入(Input Signal) 133.2.2 短時傅立葉轉換(Short-Time Fourier Transform) 143.2.3 梅爾頻譜轉換(Mel Spectrogram) 153.2.4 對數梅爾頻譜轉換(Log-Mel Spectrogram) 163.3 研究架構設計 173.3.1 問題陳述 173.3.2 研究架構 183.3.3 研究工具 203.3.4 前期測試 213.3.4.1 音訊資料前處理 223.3.4.2 特徵描述設定 223.3.4.3 模型設定 223.3.4.4 初測結果與特徵描述選定 233.3.4.5 模型選定 263.3.4.6 資料長度選定 273.4 目標設定 28第四章 研究過程與結果分析 294.1 研究過程 304.1.1 聲音前置處理 304.1.2 聲音音量正規化 304.1.3 聲音合成 314.1.4 特徵描述 314.1.5 模型訓練 344.2 預測項目 374.2.1 預測情境1：純場景音(-20dB)式 374.2.2 預測情境2：場景音音量(-20dB)小於人聲音量(-13dB) 394.2.3 預測情境3：場景音音量(-20dB)小於人聲音量(-13dB) 404.2.4 預測情境4：場景音音量(-20dB)等於人聲音量(-20dB) 424.2.5 預測情境5：場景音音量(-20dB)大於人聲音量(-35dB) 434.3 成果分析以及探討 454.3.1 從整體正確率來檢視預測結果 454.3.2 從混淆矩陣來檢視預測結果 464.4 延伸探討 49第五章 結論與未來研究方向 515.1 結論 515.2 未來研究方向 51參考文獻 53附錄 56 zh_TW dc.format.extent 5105838 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105971016 en_US dc.subject (關鍵詞) 卷積神經網路 zh_TW dc.subject (關鍵詞) DCASE音訊資料集 zh_TW dc.subject (關鍵詞) 聲音場景辨識 zh_TW dc.subject (關鍵詞) 線上身份驗證 zh_TW dc.subject (關鍵詞) Voice-based Online Identity Verification en_US dc.subject (關鍵詞) Convolutional Neural Network en_US dc.subject (關鍵詞) DCASE Dataset en_US dc.subject (關鍵詞) Acoustic Scene Classification en_US dc.title (題名) 混合人聲之聲音場景辨識 zh_TW dc.title (題名) Classification of Acoustic Scenes with Mixtures of Human Voice and Background Audio en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] ESC Dataset https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YDEPUT[2] UrbanSound8Khttps://urbansounddataset.weebly.com/urbansound8k.html[3] DCASE Challengehttp://dcase.community/[4] Liao, Wen-Hung, Jin-Yao Wen, and Jen-Ho Kuo. "Streaming audio classification in smart home environments." The First Asian Conference on Pattern Recognition. IEEE, 2011.[5] Nordby, Jon Opedal. Environmental sound classification on microcontrollers using Convolutional Neural Networks. MS thesis. Norwegian University of Life Sciences, Ås, 2019.[6] Wu, Yuzhong, and Tan Lee. "Enhancing sound texture in CNN-based acoustic scene classification." ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019.[7] Salamon, Justin, and Juan Pablo Bello. "Deep convolutional neural networks and data augmentation for environmental sound classification." IEEE Signal Processing Letters 24.3 (2017): 279-283.[8] Dai Wei, Juncheng Li, et al. "Acoustic scene recognition with deep neural networks (DCASE challenge 2016)." Robert Bosch Research and Technology Center 3 (2016).[9] Hussain, Khalid, Mazhar Hussain, and Muhammad Gufran Khan. "An Improved Acoustic Scene Classification Method Using Convolutional Neural Networks (CNNs)." American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) 44.1 (2018): 68-76.[10] Han, Yoonchang, and Kyogu Lee. "Acoustic scene classification using convolutional neural network and multiple-width frequency-delta data augmentation." arXiv preprint arXiv:1607.02383 (2016).[11] Kim, Jaehun, and Kyogu Lee. "Empirical study on ensemble method of deep neural networks for acoustic scene classification." Proc. of IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) (2016).[12] Santoso, Andri, Chien-Yao Wang, and Jia-Ching Wang. Acoustic scene classification using network-in-network based convolutional neural network. DCASE2016 Challenge, Tech. Rep, 2016.[13] Becker, Sören, et al. "Interpreting and explaining deep neural networks for classification of audio signals." arXiv preprint arXiv:1807.03418 (2018).[14] Keren, Gil, and Björn Schuller. "Convolutional RNN: an enhanced model for extracting features from sequential data." 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016.[15] CH.Tseng，初探卷積神經網路https://chtseng.wordpress.com/2017/09/12/%E5%88%9D%E6%8E%A2%E5%8D%B7%E7%A9%8D%E7%A5%9E%E7%B6%93%E7%B6%B2%E8%B7%AF/[16] Lin, Min, Qiang Chen, and Shuicheng Yan. "Network in network." arXiv preprint arXiv:1312.4400 (2013).[17] Y. LeCun, Y. Bengio, G. Hinton, L. Y., B. Y., and H. G., “Deep learning,” Nature, vol. 521,no. 7553, pp. 436–444, 2015.[18] NVIDIA DIGITShttps://developer.nvidia.com/digits[19] Kerashttps://keras.io/[20] François Chollet，Deep learning 深度學習必讀：Keras 大神帶你用 Python 實作，旗標，ISBN：9789863125501，2019[21] 郭秋田等，多媒體導論與應用第三版，旗標，ISBN:9574426246，2008。[22] 丁建均，時頻分析近年來的發展http://www.ancad.com.tw/Training/ppt_download/%E4%B8%81%E5%BB%BA%E5%9D%87%E6%95%99%E6%8E%880628.pdf[23] Pu Sun, “Comparison of STFT and Wavelet Transform in Timefrequency Analysis”,2014.[24] Solovyev, Roman A., et al. "Deep Learning Approaches for Understanding Simple Speech Commands." arXiv preprint arXiv:1810.02364 (2018).[25] Librosahttps://librosa.github.io/librosa/feature.html[26] Pydub, AudioSegmenthttps://github.com/jiaaro/pydub[27] Sklearn.preprocessing.StandardScalerhttps://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html[28] description of acoustic scene classes in TUT Acoustic scenes 2016 dataset.http://www.cs.tut.fi/sgn/arg/dcase2016/acoustic-scenes zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202001422 en_US
