參考文獻 |
[1] Fonseca, E.; Gong, R.; Bogdanov, D.; Slizovskaia, O.; Gomez, E.; Serra, X. Acoustic Scene Classification by Ensembling Gradient Boosting Machine and Convolutional Neural Networks. In Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Munich, Germany, 16–17 November 2017.
[2] Maka, T. Audio Feature Space Analysis for Acoustic Scene Classification. In Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Surrey, UK, 19–20 November 2018.
[3] Bisot, V.; Essid, S.; Richard, G. HOG and Subband Power Distribution Image Features for Acoustic Scene Classification. In Proceedings of the 23rd European Signal Processing Conference (EUSIPCO), Nice, France, 31 August–4 September 2015; pp. 719–723, doi:10.1109/EUSIPCO.2015.7362477. [CrossRef]
[4] Jiménez, A.; Elizalde, B.; Raj, B. DCASE 2017 Task 1: Acoustic Scene Classification using Shift-Invariant Kernels and Random Features. In Proceedings of the Detection and Classification of Acoustic Scenes and EventsWorkshop (DCASE), Munich, Germany, 16–17 November 2017.
[5] D. Stowell, D. Giannoulis, E. Benetos, M. Lagrange, and M. D. Plumbley, “Detection and classification of acoustic scenes and events,” IEEE Trans. on Multimedia, vol. 17, no. 10, pp. 1733–1746, October 2015.
[6] A. Mesaros, T. Heittola, E. Benetos, P. Foster, M. Lagrange, T. Virtanen, and M. D. Plumbley, “Detection and classification of acoustic scenes and events: Outcome of the dcase 2016 challenge,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 2, pp. 379–393, Feb 2018.
[7] A. Mesaros, T. Heittola, A. Diment, B. Elizalde, A. Shah, E. Vincent, B. Raj, and T. Virtanen, “DCASE2017 challenge setup: Tasks, datasets and baseline system,” in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), November 2017, pp. 85–92.
[8] H. Eghbal-zadeh, B. Lehner, M. Dorfer, and G. Widmer, “A hybrid approach with multi-channel i-vectors and convolutional neural networks for acoustic scene classification,” in 2017 25th European Signal Processing Conference (EUSIPCO), Aug 2017, pp. 2749–2753.
[9] E. Marchi, D. Tonelli, X. Xu, F. Ringeval, J. Deng, S. Squartini, and B. Schuller, “Pairwise decomposition with deep neural networks and multiscale kernel subspace learning for acoustic scene classification,” in Proc. of the Detection and Classification of Acoustic Scenes and Events 2016 Workshop (DCASE2016), September 2016, pp. 65–69.
[10] M. Valenti, S. Squartini, A. Diment, G. Parascandolo, and T. Virtanen, “A convolutional neural network approach for acoustic scene classification,” in 2017 International Joint Conference on Neural Networks (IJCNN), May 2017, pp. 1547–1554.
[11] S. H. Bae, I. Choi, and N. S. Kim, “Acoustic scene classification using parallel combination of LSTM and CNN,” in Proc. of the Detection and Classification of Acoustic Scenes and Events 2016 Workshop (DCASE2016), September 2016, pp. 11–15.
[12] V. Bisot, R. Serizel, S. Essid, and G. Richard, “Feature learning with matrix factorization applied to acoustic scene classification,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 25, no. 6, pp. 1216–1229, June 2017.
[13] A. J. Eronen, V. T. Peltonen, J. T. Tuomi, A. P. Klapuri, S. Fagerlund, T. Sorsa, G. Lorho, and J. Huopaniemi, "Audio-based context recognition," IEEE Trans. Audio, Speech, and Language Processing, vol. 14, no. 1, pp. 321-329, 2006.
[14] S. Chu, S. Narayanan, and C. J. Kuo, "Environmental sound recognition with time-frequency audio features," IEEE Trans. Audio, Speech, and Language Processing, vol. 17, no. 6, pp. 1142- 1158, 2009.
[15] V. Carletti, P. Foggia, G. Percannella, A. Saggese, N. Strisciuglio, and M. Vento, "Audio surveillance using a bag of aural words classifier," in IEEE Int. Conf. on Advanced Video and Signal Based Surveillance, pp. 81-86, 2013.
[16] J.-J. Aucouturier, B. Defreville, and F. Pachet, "The bag-offrames approach to audio pattern recognition: A sufficient model for urban soundscapes but not for polyphonic music," The Journal of the Acoustical Society of America, vol. 122, no. 2, pp. 881-891, 2007.
[17] S. Pancoast and M. Akbacak, "Bag-of-Audio-Words approach for multimedia event classification," in INTERSPEECH 2012, pp. 2105-2108, 2012.
[18] W. Choi, S. Kim, M. Keum, D. K. Han, and H. Ko, “Acoustic and visual signal based context awareness system for mobile application,” IEEE Trans. Consum. Electron., vol. 57, no. 2, pp. 738-746, 2011.
[19] Singh, A.; Rajan, P.; Bhavsar, A. Deep Multi-View Features from Raw Audio for Acoustic Scene Classification. In Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA, 25–26 October 2019; pp. 229–233.
[20] Yang, L.; Chen, X.; Tao, L. Acoustic Scene Classification using Multi-Scale Features. In Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Surrey, UK, 19–20 November 2018.
[21] Singh, A.; Thakur, A.; Rajan, P.; Bhavsar, A. A Layer-Wise Score Level Ensemble Framework for Acoustic Scene Detection. In Proceedings of the 26th European Signal Processing Conference (EUSIPCO), Rome, Italy, 3–7 September 2018; pp. 837–841, doi:10.23919/EUSIPCO.2018.8553052. [CrossRef]
[22] Mars, R.; Pratik, P.; Nagisetty, S.; Lim, C. Acoustic Scene Classification from Binaural Signals using Convolutional Neural Networks. In Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA, 25–26 October 2019; pp. 149–153, doi:10.33682/6c9z-gd15. [CrossRef]
[23] Ren, Z.; Pandit, V.; Qian, K.; Yang, Z.; Zhang, Z.; Schuller, B. Deep Sequential Image Features for Acoustic Scene Classification. In Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Munich, Germany, 16–17 November 2017.
[24] Fonseca, E.; Gong, R.; Bogdanov, D.; Slizovskaia, O.; Gomez, E.; Serra, X. Acoustic Scene Classification by Ensembling Gradient Boosting Machine and Convolutional Neural Networks. In Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Munich, Germany, 16–17 November 2017.
[25] Huang, J.; Lu, H.; Lopez-Meyer, P.; Maruri, H.A.C.; Ontiveros, J.A.d.H. Acoustic Scene Classification using Deep Learning-Based Ensemble Averaging. In Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA, 25–26 October 2019; pp. 94–98.
[26] Nguyen, T.; Pernkopf, F. Acoustic Scene Classification using a Convolutional Neural Network Ensemble and Nearest Neighbor Filters. In Proceedings of the Detection and Classification of Acoustic Scenes and EventsWorkshop (DCASE), Surrey, UK, 19–20 November 2018.
[27] Zeinali, H.; Burget, L.; Cernocky, J. Convolutional Neural Networks and X-Vector Embeddings for DCASE2018 Acoustic Scene Classification Challenge. In Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Surrey, UK, 19–20 November 2018.
[28] Weiping, Z.; Jiantao, Y.; Xiaotao, X.; Xiangtao, L.; Shaohu, P. Acoustic SceneClassification usingDeepConvolutional Neural Networks and Multiple Spectrogram Fusions. In Proceedings of the Detection and Classification of Acoustic Scenes and EventsWorkshop (DCASE),Munich, Germany, 16–17 November 2017.
[29] G. Huang, Z. Liu, L. Van Der Maaten and K. Q. Weinberger, "Densely Connected Convolutional Networks," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 2261-2269, doi: 10.1109/CVPR.2017.243.
[30] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,pages 1–9,2015.
[31] G. Huang, Z. Liu, K. Q. Weinberger and L. Maaten. Densely Connected Convolutional Networks. arXiv:1608.06993v3,2016.
[32] Suh Sangwon, Park Sooyoung, Jeong Youngho and Lee Taejin, Media Coding Research Section, Electronics and Telecommunications Research Institute, Daejeon, South Korea, Designing Acoustic Scene Classification Models with CNN Variants.
[33] Hu Hu, Chao-Han Huck Yang, Xianjun Xia, Xue Bai, Xin Tang, Yajian Wang, Shutong Niu, Li Chai, Juanjuan Li, Hongning Zhu, Feng Bao, Yuanjun Zhao, Sabato Marco Siniscalchi, Yannan Wang, Jun Du and Chin-Hui Lee, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA, Tencent Media Lab, Shenzhen, China, University of Science and Technology of China, HeFei, China, Tencent Media Lab, Beijing, China, Computer Engineering School, University of Enna Kore, Italy, Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation.
[34] Monteiro Joao, Shruti Kshirsagar, Anderson Avila, Amr Aaballah, Parth Tiwari and Tiago Falk, EMT, Institut National de la Recherche Scientifique, Montreal, Canada, Development of the Inrs-Emt Scene Classification Systems for the 2020 Edition of the DCASE Challenge.
[35] Chi Zhang1, Hanxin Zhu2 and Cheng Ting3, Electronic Information Engineering, University of Electronic Science and Technology of China, Chengdu, China, 2Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China, 3University of Electronic Science and Technology of China, Chengdu, China, Simple Convolutional Networks Attempting Acoustic Scene Classification Cross Devices. |