政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/131936

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/131936

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政大機構典藏 > 資訊學院 > 資訊科學系碩士在職專班 > 學位論文 > Item 140.119/131936

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/131936

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:	2020-09-02 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] UrbanSound8K https://urbansounddataset.weebly.com/urbansound8k.html [3] DCASE Challenge http://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 DIGITS https://developer.nvidia.com/digits [19] Keras https://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] Librosa https://librosa.github.io/librosa/feature.html [26] Pydub, AudioSegment https://github.com/jiaaro/pydub [27] Sklearn.preprocessing.StandardScaler https://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
DOI:	10.6814/NCCU202001422
顯示於類別:	[資訊科學系碩士在職專班] 學位論文

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