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ORCIDDCASE 2018, Surrey, UK
- Mark D. Plumbley, Christian Kroos, Juan Pablo Bello, Gaël Richard, Daniel P. W. Ellis, Annamaria Mesaros:
Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events, DCASE 2018, Surrey, UK, November 19-20, 2018. 2018, ISBN 978-952-15-4262-6 - Annamaria Mesaros, Toni Heittola, Tuomas Virtanen:
A multi-device dataset for urban acoustic scene classification. 9-13 - Félix Gontier, Pierre Aumond
, Mathieu Lagrange, Catherine Lavandier, Jean-François Petiot:
Towards perceptual soundscape characterization using event detection algorithms. 14-18 - Romain Serizel, Nicolas Turpault, Hamid Eghbal-Zadeh, Ankit Parag Shah:
Large-scale weakly labeled semi-supervised sound event detection in domestic environments. 19-23 - Zhicun Xu, Peter Smit, Mikko Kurimo:
The Aalto system based on fine-tuned AudioSet features for DCASE2018 task2 - general purpose audio tagging. 24-28 - Liping Yang, Xinxing Chen, Lianjie Tao:
Acoustic scene classification using multi-scale features. 29-33 - Truc Nguyen, Franz Pernkopf:
Acoustic scene classification using a convolutional neural network ensemble and nearest neighbor filters. 34-38 - Zhao Ren, Qiuqiang Kong, Kun Qian, Mark D. Plumbley, Björn W. Schuller:
Attention-based convolutional neural networks for acoustic scene classification. 39-43 - Kevin Wilkinghoff:
General-purpose audio tagging by ensembling convolutional neural networks based on multiple features. 44-48 - Qingkai Wei, Yanfang Liu, Xiaohui Ruan:
A report on audio tagging with deeper CNN, 1D-ConvNet and 2D-ConvNet. 49-53 - Thi Ngoc Tho Nguyen, Ngoc Khanh Nguyen, Douglas L. Jones, Woon-Seng Gan:
DCASE 2018 task 2: iterative training, label smoothing, and background noise normalization for audio event tagging. 54-58 - Shota Ikawa, Kunio Kashino:
Acoustic event search with an onomatopoeic query: measuring distance between onomatopoeic words and sounds. 59-63 - Léo Cances, Thomas Pellegrini, Patrice Guyot:
Sound event detection from weak annotations: weighted-GRU versus multi-instance-learning. 64-68 - Eduardo Fonseca, Manoj Plakal, Frederic Font, Daniel P. W. Ellis, Xavier Favory, Jordi Pons, Xavier Serra:
General-purpose tagging of Freesound audio with AudioSet labels: task description, dataset, and baseline. 69-73 - Wootaek Lim, Sangwon Suh, Youngho Jeong:
Weakly labeled semi-supervised sound event detection using CRNN with inception module. 74-77 - Yuanbo Hou, Qiuqiang Kong, Jun Wang, Shengchen Li:
Polyphonic audio tagging with sequentially labelled data using CRNN with learnable gated linear units. 78-82 - Robert Harb, Franz Pernkopf:
Sound event detection using weakly labelled semi-supervised data with GCRNNs, VAT and self-adaptive label refinement. 83-87 - Bogdan Pantic:
Ensemble of convolutional neural networks for general-purpose audio tagging. 88-92 - Shengyun Wei, Kele Xu, Dezhi Wang, Feifan Liao, Huaimin Wang, Qiuqiang Kong:
Sample mixed-based data augmentation for domestic audio tagging. 93-97 - Yingmei Guo, Mingxing Xu, Jianming Wu, Yanan Wang, Keiichiro Hoashi:
Multi-scale convolutional recurrent neural network with ensemble method for weakly labeled sound event detection. 98-102 - Octave Mariotti, Matthieu Cord, Olivier Schwander:
Exploring deep vision models for acoustic scene classification. 103-107 - Ivan Himawan, Michael Towsey, Paul Roe:
3D convolutional recurrent neural networks for bird sound detection. 108-112 - Tomasz Maka:
Audio feature space analysis for acoustic scene classification. 113-117 - Jee-weon Jung, Hee-Soo Heo, Hye-jin Shim, Ha-Jin Yu:
DNN based multi-level feature ensemble for acoustic scene classification. 118-122 - Veronica Morfi, Dan Stowell:
Data-efficient weakly supervised learning for low-resource audio event detection using deep learning. 123-127 - Brian Margolis, Madhav Ghei, Bryan Pardo:
Applying triplet loss to siamese-style networks for audio similarity ranking. 128-132 - Inês Nolasco, Emmanouil Benetos:
To bee or not to bee: Investigating machine learning approaches for beehive sound recognition. 133-137 - Shayan Gharib, Konstantinos Drossos, Emre Cakir, Dmitriy Serdyuk, Tuomas Virtanen:
Unsupervised adversarial domain adaptation for acoustic scene classification. 138-142 - Mario Lasseck:
Acoustic bird detection with deep convolutional neural networks. 143-147 - Bongjun Kim, Madhav Ghei, Bryan Pardo, Zhiyao Duan:
Vocal Imitation Set: a dataset of vocally imitated sound events using the AudioSet ontology. 148-152 - Yunpeng Li, Ivan Kiskin, Marianne Sinka, Davide Zilli, Henry Chan, Eva Herreros-Moya, Theeraphap Chareonviriyaphap, Rungarun Tisgratog, Kathy Willis, Stephen J. Roberts:
Fast mosquito acoustic detection with field cup recordings: an initial investigation. 153-157 - Christian Roletscheck, Tobias Watzka, Andreas Seiderer, Dominik Schiller, Elisabeth André:
Using an evolutionary approach to explore convolutional neural networks for acoustic scene classification. 158-162 - Sidrah Liaqat, Narjes Bozorg, Neenu Jose, Patrick Conrey, Antony Tamasi, Michael T. Johnson:
Domain tuning methods for bird audio detection. 163-167 - Benjamin R. Hammond, Philip J. B. Jackson:
Robust median-plane binaural sound source localization. 168-172 - Khaled Koutini, Hamid Eghbal-zadeh, Gerhard Widmer:
Iterative knowledge distillation in R-CNNs for weakly-labeled semi-supervised sound event detection. 173-177 - Matthias Dorfer, Gerhard Widmer:
Training general-purpose audio tagging networks with noisy labels and iterative self-verification. 178-182 - Helen L. Bear, Emmanouil Benetos:
An extensible cluster-graph taxonomy for open set sound scene analysis. 183-187 - Changsong Yu, Karim Said Barsim, Qiuqiang Kong, Bin Yang:
Multi-level attention model for weakly supervised audio classification. 188-192 - Kele Xu, Boqing Zhu, Dezhi Wang, Yuxing Peng, Huaimin Wang, Lilun Zhang, Bo Li:
Meta learning based audio tagging. 193-196 - Il-Young Jeong, Hyungui Lim:
Audio tagging system using densely connected convolutional networks. 197-201 - Hossein Zeinali, Lukás Burget, Jan Honza Cernocký:
Convolutional neural networks and x-vector embedding for DCASE2018 Acoustic Scene Classification challenge. 202-206 - Marcel Lederle, Benjamin Wilhelm:
Combining high-level features of raw audio waves and mel-spectrograms for audio tagging. 207-211 - Turab Iqbal, Qiuqiang Kong, Mark D. Plumbley, Wenwu Wang:
General-purpose audio tagging from noisy labels using convolutional neural networks. 212-216 - Qiuqiang Kong, Turab Iqbal, Yong Xu, Wenwu Wang, Mark D. Plumbley:
DCASE 2018 Challenge Surrey cross-task convolutional neural network baseline. 217-221
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