A Review of Deep Learning Based Methods for Acoustic Scene Classification
Source abstract
Abstract
The number of publications on acoustic scene classification (ASC) in environmental audio recordings has constantly increased over the last few years. This was mainly stimulated by the annual Detection and Classification of Acoustic Scenes and Events (DCASE) competition with its first edition in 2013. All competitions so far involved one or multiple ASC tasks. With a focus on deep learning based ASC algorithms, this article summarizes and groups existing approaches for data preparation, i.e., feature representations, feature pre-processing, and data augmentation, and for data modeling, i.e., neural network architectures and learning paradigms. Finally, the paper discusses current algorithmic limitations and open challenges in order to preview possible future developments towards the real-life application of ASC systems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Music and Audio Processing · Speech and Audio Processing · Diverse Musicological Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Semantic Music Technologies, Fraunhofer IDMT, Ehrenbergstraße 31, 98693 Ilmenau, Germany
References (99)
Show source-provided references
1. Virtanen, T., Plumbley, M.D., and Ellis, D. (2018). Computational Analysis of Sound Scenes and Events, Springer International Publishing.10.1007/978-3-319-63450-0 ↗
2. Mesaros, A., Heittola, T., and Virtanen, T. (2017, January 15–18). Assessment of Human and Machine Performance in Acoustic Scene Classification: DCASE 2016 Case Study. Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, NY, USA.10.1109/WASPAA.2017.8170047 ↗
3. Acoustic Scene Classification: Classifying environments from the sounds they produce10.1109/MSP.2014.2326181 ↗
4. Xia, X., Togneri, R., Sohel, F., Zhao, Y., and Huang, D. (2019). A Survey: Neural Network-Based Deep Learning for Acoustic Event Detection. Circuits, Systems, and Signal Processing, Springer.10.1007/s00034-019-01094-1 ↗
5. Dang, A., Vu, T.H., and Wang, J.C. (2017, January 8–10). A survey of Deep Learning for Polyphonic Sound Event Detection. Proceedings of the International Conference on Orange Technologies (ICOT), Singapore.10.1109/ICOT.2017.8336092 ↗
6. Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge10.1109/TASLP.2017.2778423 ↗
7. Han, Y., Park, J., and Lee, K. (2017, January 16–17). Convolutional Neural Networks with Binaural Representations and Background Subtraction for Acoustic Scene Classification. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Munich, Germany.
8. Mars, R., Pratik, P., Nagisetty, S., and Lim, C. (2019, January 25–26). Acoustic Scene Classification from Binaural Signals using Convolutional Neural Networks. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA.10.33682/6c9z-gd15 ↗
9. Green, M.C., and Murphy, D. (2017, January 16–17). Acoustic Scene Classification using Spatial Features. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Munich, Germany.
10. Zieliński, S.K., and Lee, H. (2018, January 9–12). Feature Extraction of Binaural Recordings for Acoustic Scene Classification. Proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS), Poznań, Poland.10.15439/2018F182 ↗
11. Qian, K., Ren, Z., Pandit, V., Yang, Z., Zhang, Z., and Schuller, B. (2017, January 16–17). Wavelets Revisited for the Classification of Acoustic Scenes. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Munich, Germany.
12. Ren, Z., Pandit, V., Qian, K., Yang, Z., Zhang, Z., and Schuller, B. (2017, January 16–17). Deep Sequential Image Features for Acoustic Scene Classification. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Munich, Germany.
13. Li, Z., Hou, Y., Xie, X., Li, S., Zhang, L., Du, S., and Liu, W. (2019, January 8–12). Multi-Level Attention Model with Deep Scattering Spectrum for Acoustic Scene Classification. Proceedings of the IEEE International Conference on Multimedia and Expo Workshops (ICMEW), Shanghai, China.10.1109/ICMEW.2019.00074 ↗
14. Chen, H., Zhang, P., Bai, H., Yuan, Q., Bao, X., and Yan, Y. (2018, January 2–6). Deep convolutional neural network with scalogram for audio scene modeling. Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), Hyderabad, India.10.21437/Interspeech.2018-1524 ↗
15. Chen, H., Liu, Z., Liu, Z., Zhang, P., and Yan, Y. (2019, January 25–26). Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA.
16. Ye, J., Kobayashi, T., Toyama, N., Tsuda, H., and Murakawa, M. (2018). Acoustic scene classification using efficient summary statistics and multiple spectro-temporal descriptor fusion. Appl. Sci., 8.10.3390/app8081363 ↗
17. Li, Y., Li, X., Zhang, Y., Wang, W., Liu, M., and Feng, X. (2018, January 16–17). Acoustic Scene Classification Using Deep Audio Feature and BLSTM Network. Proceedings of the 6th International Conference on Audio, Language and Image Processing (ICALIP), Shanghai, China.10.1109/ICALIP.2018.8455765 ↗
18. Bisot, V., Essid, S., and Richard, G. (September, January 31). HOG and Subband Power Distribution Image Features for Acoustic Scene Classification. Proceedings of the 23rd European Signal Processing Conference (EUSIPCO), Nice, France.
19. Environment Sound Classification using Multiple Feature Channels and Deep Convolutional Neural Networks
20. Moritz, N., Schröder, J., Goetze, S., Anemüller, J., and Kollmeier, B. (2016, January 3). Acoustic Scene Classification using Time-Delay Neural Networks and Amplitude Modulation Filter Bank Features. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Budapest, Hungary.
21. Park, S., Mun, S., Lee, Y., and Ko, H. (2017, January 16–17). Acoustic Scene Classification Based on Convolutional Neural Network using Double Image Features. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Munich, Germany.
22. Fonseca, E., Gong, R., Bogdanov, D., Slizovskaia, O., Gomez, E., and Serra, X. (2017, January 16–17). Acoustic Scene Classification by Ensembling Gradient Boosting Machine and Convolutional Neural Networks. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Munich, Germany.
23. Maka, T. (2018, January 19–20). Audio Feature Space Analysis for Acoustic Scene Classification. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Surrey, UK.
24. Abidin, S., Togneri, R., and Sohel, F. (2017, January 5–9). Enhanced LBP Texture Features from Time Frequency Representations for Acoustic Scene Classification. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.10.1109/ICASSP.2017.7952231 ↗
25. Jiménez, A., Elizalde, B., and Raj, B. (2017, January 16–17). DCASE 2017 Task 1: Acoustic Scene Classification using Shift-Invariant Kernels and Random Features. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Munich, Germany.
26. Huang, J., Lu, H., Lopez-Meyer, P., Maruri, H.A.C., and Ontiveros, J.A.d.H. (2019, January 25–26). Acoustic Scene Classification using Deep Learning-Based Ensemble Averaging. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA.10.33682/8rd2-g787 ↗
27. Singh, A., Rajan, P., and Bhavsar, A. (2019, January 25–26). Deep Multi-View Features from Raw Audio for Acoustic Scene Classification. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA.10.33682/05gk-pd08 ↗
28. Chen, H., Zhang, P., and Yan, Y. (2019, January 12–17). An Audio Scene Classification Framework with Embedded Filters and a DCT-Based Temporal Module. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.10.1109/ICASSP.2019.8683636 ↗
29. Amiriparian, S., Freitag, M., Cummins, N., Gerczuk, M., Pugachevskiy, S., and Schuller, B. (2018, January 3–7). A Fusion of Deep Convolutional Generative Adversarial Networks and Sequence to Sequence Autoencoders for Acoustic Scene Classification. Proceedings of the 26th European Signal Processing Conference (EUSIPCO), Rome, Italy.10.23919/EUSIPCO.2018.8553225 ↗
30. Feature Learning with Matrix Factorization Applied to Acoustic Scene Classification10.1109/TASLP.2017.2690570 ↗
31. Benetos, E., Lagrange, M., and Dixon, S. (2012, January 17–21). Characterisation of Acoustic Scenes using a Temporally-Constrained Shift-Invariant Model. Proceedings of the 15th International Conference on Digital Audio Effects (DAFx-12), York, UK.
32. Seo, H., Park, J., and Park, Y. (2019, January 25–26). Acoustic Scene Classification using Various Pre-Processed Features and Convolutional Neural Networks. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA.
33. Wang, Y., Getreuer, P., Hughes, T., Lyon, R.F., and Saurous, R.A. (2017, January 5–9). Trainable Frontend for Robust and Far-Field Keyword Spotting. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.10.1109/ICASSP.2017.7953242 ↗
34. Per-channel energy normalization: Why and how10.1109/LSP.2018.2878620 ↗
35. Wu, Y., and Lee, T. (2019, January 12–17). Enhancing Sound Texture in CNN based Acoustic Scene Classification. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.10.1109/ICASSP.2019.8683490 ↗
36. Rafii, Z., and Pardo, B. (2012, January 8–12). Music/Voice Separation using the Similarity Matrix. Proceedings of the 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal.
37. Nguyen, T., and Pernkopf, F. (2018, January 19–20). Acoustic Scene Classification using a Convolutional Neural Network Ensemble and Nearest Neighbor Filters. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Surrey, UK.
38. Mariotti, O., Cord, M., and Schwander, O. (2018, January 19–20). Exploring Deep Vision Models for Acoustic Scene Classification. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Surrey, UK.
39. ImageNet Large Scale Visual Recognition Challenge10.1007/s11263-015-0816-y ↗
40. Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., and Ritter, M. (2017, January 5–9). Audio Set: An Ontology and Human-Labeled Dataset for Audio Events. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.10.1109/ICASSP.2017.7952261 ↗
41. Abeßer, J., Mimilakis, S.I., Gräfe, R., and Lukashevich, H. (2017, January 16–17). Acoustic Scene Classification By Combining Autoencoder-Based Dimensionality Reduction and Convolutional Neural Networks. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Munich, Germany.
42. Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification10.1109/LSP.2017.2657381 ↗
43. Xu, J.X., Lin, T.C., Yu, T.C., Tai, T.C., and Chang, P.C. (2018, January 10–12). Acoustic Scene Classification Using Reduced MobileNet Architecture. Proceedings of the IEEE International Symposium on Multimedia (ISM), Taichung, Taiwan.10.1109/ISM.2018.00038 ↗
44. Koutini, K., Eghbal-zadeh, H., and Widmer, G. (2019, January 25–26). Receptive-Field-Regularized CNN Variants for Acoustic Scene Classification. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA.10.33682/cjd9-kc43 ↗
45. Zhang, H., Cisse, M., Dauphin, Y.N., and Lopez-Paz, D. (May, January 30). mixup: Beyond Empirical Risk Minimization. Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada.
46. Park, D.S., Chan, W., Zhang, Y., Chiu, C.C., Zoph, B., Cubuk, E.D., and Le, Q.V. (2019, January 2–15). Specaugment: A simple data augmentation method for automatic speech recognition. Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), Graz, Austria.10.21437/Interspeech.2019-2680 ↗
47. Zhong, Z., Zheng, L., Kang, G., Li, S., and Yang, Y. (2017). Random Erasing Data Augmentation. arXiv.
48. Lasseck, M. (2018, January 19–20). Acoustic bird detection with deep convolutional neural networks. Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE), Surrey, UK.
49. Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems (NIPS), Curran Associates, Inc.
50. Mun, S., Shon, S., Kim, W., Han, D.K., and Ko, H. (2017, January 5–9). Deep Neural Network Based Learning and Transferring Mid-Level Audio Features for Acoustic Scene Classification. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.10.1109/ICASSP.2017.7952265 ↗
51. Mun, S., Park, S., Han, D.K., and Ko, H. (2017, January 16–17). Generative Adversarial Networks based Acoustic Scene Training Set Augmentation and Selection using SVM Hyperplane. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Munich, Germany.
52. Kong, Q., Xu, Y., Iqbal, T., Cao, Y., Wang, W., and Plumbley, M.D. (2019, January 12–17). Acoustic Scene Generation with Conditional SampleRNN. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.10.1109/ICASSP.2019.8683727 ↗
53. Roletscheck, C., Watzka, T., Seiderer, A., Schiller, D., and André, E. (2019, January 25–26). Using an Evolutionary Approach To Explore Convolutional Neural Networks for Acoustic Scene Classification. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA.
54. Ren, Z., Kong, Q., Han, J., Plumbley, M.D., and Schuller, B.W. (2019, January 12–17). Attention based Atrous Convolutional Neural Networks: Visualisation and Understanding Perspectives of Acoustic Scenes. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.10.1109/ICASSP.2019.8683434 ↗
55. Koutini, K., Eghbal-zadeh, H., Widmer, G., and Kepler, J. (2019, January 25–26). CP-JKU Submissions to DCASE’19: Acoustic Scene Classification and Audio Tagging with REceptive-Field-Regularized CNNs. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA.
56. Yang, L., Chen, X., and Tao, L. (2018, January 19–20). Acoustic Scene Classification using Multi-Scale Features. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Surrey, UK.
57. Cho, J., Yun, S., Park, H., Eum, J., and Hwang, K. (2019, January 25–26). Acoustic Scene Classification Based on a Large-Margin Factorized CNN. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA.
58. Wang, C.Y., Wang, J.C., Wu, Y.C., and Chang, P.C. (2017, January 14–15). Asymmetric Kernel Convolution Neural Networks for Acoustic Scenes Classification. Proceedings of the IEEE International Symposium on Consumer Electronics (ISCE), Kuala Lumpur, Malaysia.
59. Basbug, A.M., and Sert, M. (February, January 30). Acoustic Scene Classification Using Spatial Pyramid Pooling with Convolutional Neural Networks. Proceedings of the 13th IEEE International Conference on Semantic Computing (ICSC), Newport, CA, USA.
60. Marchi, E., Tonelli, D., Xu, X., Ringeval, F., Deng, J., Squartini, S., and Schuller, B. (2016, January 3). Pairwise Decomposition with Deep Neural Networks and Multiscale Kernel Subspace Learning for Acoustic Scene Classification. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Budapest, Hungary.
61. Bisot, V., Serizel, R., Essid, S., and Richard, G. (2017, January 16–17). Nonnegative Feature Learning Methods for Acoustic Scene Classification. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Munich, Germany.
62. Takahashi, G., Yamada, T., Ono, N., and Makino, S. (2018, January 2–15). Performance Evaluation of Acoustic Scene Classification using DNN-GMM and Frame-Concatenated Acoustic Features. Proceedings of the 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Honolulu, HI, USA.10.1109/APSIPA.2017.8282314 ↗
63. Bae, S.H., Choi, I., and Kim, N.S. (2016, January 3). Acoustic Scene Classification using Parallel Combination of LSTM and CNN. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Budapest, Hungary.
64. Zöhrer, M., and Pernkopf, F. (2016, January 3). Gated Recurrent Networks Applied to Acoustic Scene Classification and Acoustic Event Detection. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Budapest, Hungary.
65. Jati, A., Nadarajan, A., Mundnich, K., and Narayanan, S. (2020, January 4–8). Characterizing dynamically varying acoustic scenes from egocentric audio recordings in workplace setting. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.
66. Mesaros, A., Heittola, T., and Virtanen, T. (2019, January 25–26). Acoustic Scene Classification in DCASE 2019 Challenge:Closed and Open Set Classification and Data Mismatch Setups. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA.10.33682/m5kp-fa97 ↗
67. Saki, F., Guo, Y., and Hung, C.Y. (2019, January 25–26). Open-Set Evolving Acoustic Scene Classification System. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA.10.33682/en2t-9m14 ↗
68. Wilkinghoff, K., and Kurth, F. (2019, January 25–26). Open-Set Acoustic Scene Classification with Deep Convolutional Autoencoders. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA.10.33682/340j-wd27 ↗
69. Lehner, B., Koutini, K., Schwarzlmüller, C., Gallien, T., and Widmer, G. (2019, January 25–26). Acoustic Scene Classification with Reject Option based on Resnets. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA.
70. Mcdonnell, M.D., and Gao, W. (2019, January 25–26). Acoustic Scene Classification Using Deep Residual Networks With Late Fusion of Separated High and Low Frequency Paths. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA.10.1109/ICASSP40776.2020.9053274 ↗
71. Phaye, S.S.R., Benetos, E., and Wang, Y. (2019, January 12–17). Subspectralnet—Using Sub-Spectrogram based Convolutional Neural Networks for Acoustic Scene Classification. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brighton, UK.10.1109/ICASSP.2019.8683288 ↗
72. Dang, A., Vu, T.H., and Wang, J.C. (2018, January 18–20). Acoustic Scene Classification using Convolutional Neural Networks and Multi-Scale Multi-Feature Extraction. Proceedings of the IEEE International Conference on Consumer Electronics (ICCE), Hue City, Vietnam.10.1109/ICCE.2018.8326315 ↗
73. Ren, Z., Kong, Q., Qian, K., Plumbley, M.D., and Schuller, B.W. (2018, January 19–20). Attention based Convolutional Neural Networks for Acoustic Scene Classification. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Surrey, UK.
74. Wang, C.Y., Santoso, A., and Wang, J.C. (2018, January 2–15). Acoustic Scene Classification using Self-Determination Convolutional Neural Network. Proceedings of the 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Honolulu, HI, USA.10.1109/APSIPA.2017.8281995 ↗
75. Ruder, S. (2017). An Overview of Multi-Task Learning in Deep Neural Networks. arXiv.
76. Bear, H.L., Nolasco, I., and Benetos, E. (2019, January 2–15). Towards joint sound scene and polyphonic sound event recognition. Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), Graz, Austria.10.21437/Interspeech.2019-2169 ↗
77. Xu, Y., Huang, Q., Wang, W., and Plumbley, M.D. (2016, January 3). Hierarchical Learning for DNN-Based Acoustic Scene Classification. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Budapest, Hungary.
78. Nwe, T.L., Dat, T.H., and Ma, B. (2018, January 2–15). Convolutional Neural Network with Multi-Task Learning Scheme for Acoustic Scene Classification. Proceedings of the 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Honolulu, HI, USA.10.1109/APSIPA.2017.8282241 ↗
79. Classifying environmental sounds using image recognition networks10.1016/j.procs.2017.08.250 ↗
80. Aytar, Y., Vondrick, C., and Torralba, A. (2016). SoundNet: Learning Sound Representations from Unlabeled Video. Advances in Neural Information Processing Systems (NIPS), Curran Associates, Inc.10.1109/CVPR.2016.18 ↗
81. Singh, A., Thakur, A., Rajan, P., and Bhavsar, A. (2018, January 3–7). A Layer-Wise Score Level Ensemble Framework for Acoustic Scene Detection. Proceedings of the 26th European Signal Processing Conference (EUSIPCO), Rome, Italy.10.23919/EUSIPCO.2018.8553052 ↗
82. Kumar, A., Khadkevich, M., and Fugen, C. (2018, January 15–20). Knowledge Transfer from Weakly Labeled Audio Using Convolutional Neural Network for Sound Events and Scenes. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Alberta, AB, Canada.10.1109/ICASSP.2018.8462200 ↗
83. Zeinali, H., Burget, L., and Cernocky, J. (2018, January 19–20). Convolutional Neural Networks and X-Vector Embeddings for DCASE2018 Acoustic Scene Classification Challenge. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Surrey, UK.
84. Weiping, Z., Jiantao, Y., Xiaotao, X., Xiangtao, L., and Shaohu, P. (2017, January 16–17). Acoustic Scene Classification using Deep Convolutional Neural Networks and Multiple Spectrogram Fusions. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Munich, Germany.
85. Gharib, S., Drossos, K., Emre, C., Serdyuk, D., and Virtanen, T. (2018, January 19–20). Unsupervised Adversarial Domain Adaptation for Acoustic Scene Classification. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Surrey, UK.
86. Kosmider, M. (2019, January 25–26). Calibrating Neural Networks for Secondary Recording Devices. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA.
87. Mun, S., and Shon, S. (2019, January 12–17). Domain Mismatch Robust Acoustic Scene Classification Using Channel Information Conversion. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.10.1109/ICASSP.2019.8683514 ↗
88. Drossos, K., Magron, P., and Virtanen, T. (2019, January 20–23). Unsupervised Adversarial Domain Adaptation based on the Wasserstein Distance for Acoustic Scene Classification. Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, NY, USA.10.1109/WASPAA.2019.8937231 ↗
89. Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., and Samek, W. (2015). On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE, 10.10.1371/journal.pone.0130140 ↗
90. SONYC: A System for the Monitoring, Analysis and Mitigation of Urban Noise Pollution10.1145/3224204 ↗
91. Abeßer, J., Götze, M., Clauß, T., Zapf, D., Kühn, C., Lukashevich, H., Kühnlenz, S., and Mimilakis, S. (2019, January 25–26). Urban Noise Monitoring in the Stadtlärm Project—A Field Report. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), New York, NY, USA.10.33682/s9w3-5341 ↗
92. Grollmisch, S., Abeßer, J., Liebetrau, J., and Lukashevich, H. (2019, January 2–6). Sounding Industry: Challenges and Datasets for Industrial Sound Analysis (ISA). Proceedings of the 27th European Signal Processing Conference (EUSIPCO), A Coruna, Spain.10.23919/EUSIPCO.2019.8902941 ↗
93. Automatic Environmental Sound Recognition: Performance Versus Computational Cost10.1109/TASLP.2016.2592698 ↗
94. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18–23). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.10.1109/CVPR.2018.00474 ↗
95. Drossos, K., Mimilakis, S.I., Gharib, S., Li, Y., and Virtanen, T. (2020). Sound Event Detection with Depthwise Separable and Dilated Convolutions. arXiv.10.1109/IJCNN48605.2020.9207532 ↗
96. Gordon, A., Eban, E., Nachum, O., Chen, B., Wu, H., Yang, T.J., and Choi, E. (2018, January 18–23). MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.10.1109/CVPR.2018.00171 ↗
97. Tan, M., and Le, Q.V. (2019, January 9–15). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, CA, USA.
98. Mesaros, A., Heittola, T., and Virtanen, T. (2018, January 19–20). A Multi-Device Dataset for Urban Acoustic Scene Classification. Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE), Surrey, UK.
99. Continual Lifelong Learning with Neural Networks: A Review10.1016/j.neunet.2019.01.012 ↗