Table of Contents

• A multi-device dataset for urban acoustic scene classificationAnnamaria Mesaros, Toni Heittola, Tuomas Virtanen. 9-13 [doi]
• Towards perceptual soundscape characterization using event detection algorithmsFélix Gontier, Pierre Aumond, Mathieu Lagrange, Catherine Lavandier, Jean-François Petiot. 14-18 [doi]
• Large-scale weakly labeled semi-supervised sound event detection in domestic environmentsRomain Serizel, Nicolas Turpault, Hamid Eghbal-zadeh, Ankit Parag Shah. 19-23 [doi]
• The Aalto system based on fine-tuned AudioSet features for DCASE2018 task2 - general purpose audio taggingZhicun Xu, Peter Smit, Mikko Kurimo. 24-28 [doi]
• Acoustic scene classification using multi-scale featuresLiping Yang, Xinxing Chen, Lianjie Tao. 29-33 [doi]
• Acoustic scene classification using a convolutional neural network ensemble and nearest neighbor filtersTruc Nguyen, Franz Pernkopf. 34-38 [doi]
• Attention-based convolutional neural networks for acoustic scene classificationZhao Ren, Qiuqiang Kong, Kun Qian 0003, Mark D. Plumbley, Björn W. Schuller. 39-43 [doi]
• General-purpose audio tagging by ensembling convolutional neural networks based on multiple featuresKevin Wilkinghoff. 44-48 [doi]
• A report on audio tagging with deeper CNN, 1D-ConvNet and 2D-ConvNetQingkai Wei, Yanfang Liu, Xiaohui Ruan. 49-53 [doi]
• DCASE 2018 task 2: iterative training, label smoothing, and background noise normalization for audio event taggingThi Ngoc Tho Nguyen, Ngoc Khanh Nguyen 0003, Douglas L. Jones, Woon-Seng Gan. 54-58 [doi]
• Acoustic event search with an onomatopoeic query: measuring distance between onomatopoeic words and soundsShota Ikawa, Kunio Kashino. 59-63 [doi]
• Sound event detection from weak annotations: weighted-GRU versus multi-instance-learningLéo Cances, Thomas Pellegrini, Patrice Guyot. 64-68 [doi]
• General-purpose tagging of Freesound audio with AudioSet labels: task description, dataset, and baselineEduardo Fonseca, Manoj Plakal, Frederic Font, Daniel P. W. Ellis, Xavier Favory, Jordi Pons, Xavier Serra. 69-73 [doi]
• Weakly labeled semi-supervised sound event detection using CRNN with inception moduleWootaek Lim, Sangwon Suh, Youngho Jeong. 74-77 [doi]
• Polyphonic audio tagging with sequentially labelled data using CRNN with learnable gated linear unitsYuanbo Hou, Qiuqiang Kong, Jun Wang, Shengchen Li. 78-82 [doi]
• Sound event detection using weakly labelled semi-supervised data with GCRNNs, VAT and self-adaptive label refinementRobert Harb, Franz Pernkopf. 83-87 [doi]
• Ensemble of convolutional neural networks for general-purpose audio taggingBogdan Pantic. 88-92 [doi]
• Sample mixed-based data augmentation for domestic audio taggingShengyun Wei, Kele Xu, Dezhi Wang, Feifan Liao, Huaimin Wang, Qiuqiang Kong. 93-97 [doi]
• Multi-scale convolutional recurrent neural network with ensemble method for weakly labeled sound event detectionYingmei Guo, Mingxing Xu, Jianming Wu, Yanan Wang, Keiichiro Hoashi. 98-102 [doi]
• Exploring deep vision models for acoustic scene classificationOctave Mariotti, Matthieu Cord, Olivier Schwander. 103-107 [doi]
• 3D convolutional recurrent neural networks for bird sound detectionIvan Himawan, Michael Towsey, Paul Roe. 108-112 [doi]
• Audio feature space analysis for acoustic scene classificationTomasz Maka. 113-117 [doi]
• DNN based multi-level feature ensemble for acoustic scene classificationJee-weon Jung, Hee-Soo Heo, Hye-jin Shim, Ha-Jin Yu. 118-122 [doi]
• Data-efficient weakly supervised learning for low-resource audio event detection using deep learningVeronica Morfi, Dan Stowell. 123-127 [doi]
• Applying triplet loss to siamese-style networks for audio similarity rankingBrian Margolis, Madhav Ghei, Bryan Pardo. 128-132 [doi]
• To bee or not to bee: Investigating machine learning approaches for beehive sound recognitionInês Nolasco, Emmanouil Benetos. 133-137 [doi]
• Unsupervised adversarial domain adaptation for acoustic scene classificationShayan Gharib, Konstantinos Drossos, Emre Cakir, Dmitriy Serdyuk, Tuomas Virtanen. 138-142 [doi]
• Acoustic bird detection with deep convolutional neural networksMario Lasseck. 143-147 [doi]
• Vocal Imitation Set: a dataset of vocally imitated sound events using the AudioSet ontologyBongjun Kim, Madhav Ghei, Bryan Pardo, Zhiyao Duan. 148-152 [doi]
• Fast mosquito acoustic detection with field cup recordings: an initial investigationYunpeng Li, Ivan Kiskin, Marianne Sinka, Davide Zilli, Henry Chan, Eva Herreros-Moya, Theeraphap Chareonviriyaphap, Rungarun Tisgratog, Kathy Willis, Stephen Roberts. 153-157 [doi]
• Using an evolutionary approach to explore convolutional neural networks for acoustic scene classificationChristian Roletscheck, Tobias Watzka, Andreas Seiderer, Dominik Schiller, Elisabeth André. 158-162 [doi]
• Domain tuning methods for bird audio detectionSidrah Liaqat, Narjes Bozorg, Neenu Jose, Patrick Conrey, Antony Tamasi, Michael T. Johnson. 163-167 [doi]
• Robust median-plane binaural sound source localizationBenjamin R. Hammond, Philip J. B. Jackson. 168-172 [doi]
• Iterative knowledge distillation in R-CNNs for weakly-labeled semi-supervised sound event detectionKhaled Koutini, Hamid Eghbal-zadeh, Gerhard Widmer. 173-177 [doi]
• Training general-purpose audio tagging networks with noisy labels and iterative self-verificationMatthias Dorfer, Gerhard Widmer. 178-182 [doi]
• An extensible cluster-graph taxonomy for open set sound scene analysisHelen L. Bear, Emmanouil Benetos. 183-187 [doi]
• Multi-level attention model for weakly supervised audio classificationChangsong Yu, Karim Said Barsim, Qiuqiang Kong, Bin Yang. 188-192 [doi]
• Meta learning based audio taggingKele Xu, Boqing Zhu, Dezhi Wang, Yuxing Peng, Huaimin Wang, Lilun Zhang, Bo Li. 193-196 [doi]
• Audio tagging system using densely connected convolutional networksIl-Young Jeong, Hyungui Lim. 197-201 [doi]
• Convolutional neural networks and x-vector embedding for DCASE2018 Acoustic Scene Classification challengeHossein Zeinali, Lukás Burget, Jan Honza Cernocký. 202-206 [doi]
• Combining high-level features of raw audio waves and mel-spectrograms for audio taggingMarcel Lederle, Benjamin Wilhelm. 207-211 [doi]
• General-purpose audio tagging from noisy labels using convolutional neural networksTurab Iqbal, Qiuqiang Kong, Mark D. Plumbley, Wenwu Wang. 212-216 [doi]
• DCASE 2018 Challenge Surrey cross-task convolutional neural network baselineQiuqiang Kong, Turab Iqbal, Yong Xu 0004, Wenwu Wang, Mark D. Plumbley. 217-221 [doi]