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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 鄭士康(Shyh-Kang Jeng) | |
| dc.contributor.author | Che-Jui Chang | en |
| dc.contributor.author | 張哲睿 | zh_TW |
| dc.date.accessioned | 2021-06-17T03:10:06Z | - |
| dc.date.available | 2018-07-23 | |
| dc.date.copyright | 2018-07-23 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-07-19 | |
| dc.identifier.citation | Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T., Dewolf, T., Tang, Y., &Rasmussen, D. (2012). A Large-Scale Model of the Functioning Brain. Science, 338 (NOVEMBER), 1202–1205.
Bekolay, T. (2016). Biologically inspired methods in speech recognition and synthesis: closing the loop, by University of Waterloo. Kanerva, P. (2009). Hyperdimensional Computing - An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors. Cogn Comput 1, 139-159, June. https://doi.org/10.1007/s12559-009-9009-8 Montone, G., O’Regan, J.Kevin, Terekhov, &Alexander V. (2017). Hyper-dimensional computing for a visual question-answering system that is trainable end-to-end. arXiv:1711.10185 Pimentel, M. A. F., Clifton, D. A.,Clifton, L., &Tarassenko, L. (2014). Review: A review of novelty detection. Signal Processing, 99, 215-249, June, 10.1016/j.sigpro.2013.12.026 Marchi, E., Vesperini, E., Eyben, F., Squartini, S., &Schuller, B. (2015). A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks. In Proceedings of the 40th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '15), p. 5, IEEE, Brisbane, Australia, April. Oertel, D., &Doupe, A. J. (2013). The Auditory Central Nervous System. In E. R. Kandel, J. H. Schwartz, T. M. Jessell, S. A. Siegelbaum, &A. J. Hudspeth (Eds.), Principles of Neural Science (5th ed., pp. 682–711). New York: McGraw-Hill Companies. Chandola, V., Banerjee, A., &Kumar, V. (2009). Anomaly detection. ACM Computing Surveys, 41(3), 1–6. http://doi.org/10.1145/1541880.1541882 Bekolay, T., Bergstra J, Hunsberger. E., DeWolf, T., Stewart, TC, Rasmussen, D., Choo, X., Voelker, AR &Eliasmith, C. (2014). Nengo: a Python tool for building large- scale functional brain models. Front. Neuroinform. 7:48. doi: 10.3389/fninf.2013.00048 Eliasmith, C. (2013). How to Build a Brain: A Neural Architecture for Biological Cognition. New York: Oxford University Press. Barker, J., Vincent, E., Ma, N., Christensen, H., &Green, P. (2013). The PASCAL CHiME speech separation and recognition challenge. Computer Speech and Language, 27(3), 621–633. http://doi.org/10.1016/j.csl.2012.10.004 Breunig, M. M., Kriegel, H.-P., NG, R. T., &Sander, J. 2000. LOF: Identifying density- based local outliers. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press, 93–104. Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., &Platt, J. (2000). Support vector method for novelty detection, Adv. Neural Inf. Process. Syst. 12 (3) 582– 588. Cortes, C., &Vapnik, V. (1995). Support-Vector Network. Machine Learning 20: 273. https://doi.org/10.1023/A:1022627411411 Hinton, G. E., &Salakhutdinov, R. (2006). Reducing the dimensionality of data with neural networks. Science, vol. 313, no. 5786, 504-507 Principi, E., Vesperini, F., Squartini, S., &Piazza, F. (2017). Acoustic novelty detection with adversarial autoencoders. In Neural Networks (IJCNN), 2017 International Joint Conference on. IEEE, 3324–3330 Kingma, D., &Ba, J. (2014). Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980 Davis, J., &Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on Machine learning, 233-240, June 25-29, Pittsburgh, Pennsylvania. doi>10.1145/1143844.1143874 Sharma, S., Aubin, S., &Eliasmith, C. (2016). Large-scale cognitive model design using the Nengo neural simulator. Biologically Inspired Cognitive Architectures, http://dx.doi.org/10.1016/j.bica.2016.05.001 潘郁凱。2017。仿人類雙耳聽覺的單一聲源定位。國立臺灣大學電信工程學研究 所碩士論文。 陳重源。2018。結合仿人耳聽覺定向系統的機器人室內環境導航。國立臺灣大學 電信工程學研究所碩士論文。 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69175 | - |
| dc.description.abstract | 本論文針對居家機器人之應用，提出一個仿聽覺處理之模型，以解決居家異常聲音偵測之問題。首先，以類神經網路模型模擬大腦聽覺皮質區，取得語意抽象特徵，接著，在仿大腦前額葉模型中，利用語意符號的運算，在基底核區域辨別出正異常聲音。此外，本論文亦比較不同異常偵測方法之數值結果，所提出之方法能夠在測試集中取得最佳的結果(0.972 AUC)，且僅需極少的運算時間即可完成偵測，相當適合作為居家機器人之聽覺應用模組。 | zh_TW |
| dc.description.abstract | This thesis focuses on the application of household robot and provides a humanoid auditory processing model to resolve the problem of acoustic anomaly detection in household environment. First, the proposed model uses deep neural networks to imitate auditory cortex in human brains, in order to extract abstract semantic features. Then, in the SPA module which imitates prefrontal cortex, the anomaly is detected at basal ganglia area by using symbolic computations. In addition, different anomaly detection methods are compared in this thesis. Our proposed method gets the best result on test set, with 0.972 AUC. Meanwhile, it takes less computational time to detect the anomaly, so it is very suitable for the application to a household robot. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T03:10:06Z (GMT). No. of bitstreams: 1 ntu-107-R05942055-1.pdf: 4971768 bytes, checksum: 854392d7a9abaacbda4df28111bfad5e (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 誌謝 I
中文摘要 II Abstract III 目錄 IV 表目錄 VI 圖目錄 VII 第1章 緒論 1 1.1 研究動機及目的 1 1.2 文獻回顧 2 1.3 本論文貢獻 4 1.4 章節概要 4 第2章 背景知識 5 2.1 人類聽覺路徑 5 2.2 類神經網路 7 2.3 異常偵測 10 第3章 主要聽覺計算軟體 13 3.1 深度學習套件 Keras 13 3.2 認知神經系統模擬軟體 Nengo 14 3.3 語意指標架構SPA 19 第4章 研究方法 21 4.1 系統架構 21 4.1.1 仿聽覺皮質區DNN模型 23 4.1.2 仿前額葉SPA模型 24 4.2 流程 27 第5章 實驗數值設計 28 5.1 資料蒐集 28 5.2 學習異常聲音之抽象特徵 29 5.3 辨別異常聲音 33 第6章 數值結果與討論 34 6.1 討論Nengo模擬結果之正確性 35 6.1.1 DNN模型之訓練結果 35 6.1.2 Nengo模擬之結果 36 6.2 比較不同聲音特徵擷取方式之結果 40 6.3 比較不同音量之結果 44 6.4 不同超參數之比較 46 6.4.1 特徵標準化 46 6.4.2 比較不同語意特徵維度 48 6.5 不同異常偵測方法之比較 49 第7章 結論 51 參考文獻 52 附錄 54 | |
| dc.language.iso | zh-TW | |
| dc.subject | 居家機器人 | zh_TW |
| dc.subject | 聲音異常偵測 | zh_TW |
| dc.subject | 類神經網路 | zh_TW |
| dc.subject | 仿聽覺處理 | zh_TW |
| dc.subject | Deep Neural Networks | en |
| dc.subject | Household Robot | en |
| dc.subject | Acoustic Anomaly Detection | en |
| dc.subject | Humanoid Auditory Processing | en |
| dc.title | 仿聽覺處理之聲音異常偵測 | zh_TW |
| dc.title | Humanoid Auditory Processing for Acoustic Anomaly Detection | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張智星(Jyh-Shing Jang),李宏毅(Hung-yi Lee) | |
| dc.subject.keyword | 居家機器人,聲音異常偵測,類神經網路,仿聽覺處理, | zh_TW |
| dc.subject.keyword | Household Robot,Acoustic Anomaly Detection,Deep Neural Networks,Humanoid Auditory Processing, | en |
| dc.relation.page | 56 | |
| dc.identifier.doi | 10.6342/NTU201801675 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-07-20 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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