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| Title: | 混合人聲之聲音場景辨識
Classification of Acoustic Scenes with Mixtures of Human Voice and Background Audio |
| Authors: | 李御國
Li, Yu-Guo |
| Contributors: | 廖文宏
Liao, Wen-Hung
李御國
Li, Yu-Guo |
| Keywords: | 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 |
| Date: | 2020 |
| Issue Date: | 2020-09-02 13:15:07 (UTC+8) |
| Abstract: | 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. |
| Reference: | [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.
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[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).
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[19] Keras
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[26] Pydub, AudioSegment
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[27] Sklearn.preprocessing.StandardScaler
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[28] description of acoustic scene classes in TUT Acoustic scenes 2016 dataset.
http://www.cs.tut.fi/sgn/arg/dcase2016/acoustic-scenes |
| Description: | 碩士
國立政治大學
資訊科學系碩士在職專班
105971016 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0105971016 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202001422 |
| Appears in Collections: | [資訊科學系碩士在職專班] 學位論文
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