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6th DCASE 2021, Online
- Frederic Font, Annamaria Mesaros
, Daniel P. W. Ellis, Eduardo Fonseca, Magdalena Fuentes, Benjamin Elizalde:
Proceedings of the 6th Workshop on Detection and Classification of Acoustic Scenes and Events 2021 (DCASE 2021), Online, November 15-19, 2021. 2021, ISBN 978-84-09-36072-7 - Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, Shoichiro Saito:
ToyADMOS2: Another Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection under Domain Shift Conditions. 1-5 - Qichen Han, Weiqiang Yuan, Dong Liu, Xiang Li, Zhen Yang:
Automated Audio Captioning with Weakly Supervised Pre-Training and Word Selection Methods. 6-10 - Jose A. Lopez, Georg Stemmer, Paulo Lopez-Meyer, Pradyumna Singh, Juan A. del Hoyo Ontiveros, Héctor A. Cordourier:
Ensemble Of Complementary Anomaly Detectors Under Domain Shifted Conditions. 11-15 - Javier Naranjo-Alcazar, Sergi Perez-Castanos, Maximo Cobos, Francesc J. Ferri, Pedro Zuccarello:
Squeeze-Excitation Convolutional Recurrent Neural Networks for Audio-Visual Scene Classification. 16-20 - Byeonggeun Kim, Seunghan Yang, Jangho Kim, Simyung Chang:
Domain Generalization on Efficient Acoustic Scene Classification Using Residual Normalization. 21-25 - Pascal Janetzky, Padraig Davidson, Michael Steininger, Anna Krause, Andreas Hotho:
Detecting Presence Of Speech In Acoustic Data Obtained From Beehives. 26-30 - Xinyu Cai, Heinrich Dinkel:
A Contrastive Semi-Supervised Learning Framework For Anomaly Sound Detection. 31-34 - Xinyu Cai, Heinrich Dinkel:
A Lightweight Approach for Semi-Supervised Sound Event Detection with Unsupervised Data Augmentation. 35-39 - Zhongjie Ye, Helin Wang, Dongchao Yang, Yuexian Zou:
Improving the Performance of Automated Audio Captioning via Integrating the Acoustic and Semantic Information. 40-44 - Shanshan Wang, Annamaria Mesaros, Toni Heittola, Tuomas Virtanen:
Audio-Visual Scene Classification: Analysis of DCASE 2021 Challenge Submissions. 45-49 - Pablo Zinemanas, Martín Rocamora, Eduardo Fonseca, Frederic Font, Xavier Serra:
Toward Interpretable Polyphonic Sound Event Detection with Attention Maps Based on Local Prototypes. 50-54 - Kevin Wilkinghoff:
Combining Multiple Distributions based on Sub-Cluster AdaCos for Anomalous Sound Detection under Domain Shifted Conditions. 55-59 - Benno Weck, Xavier Favory, Konstantinos Drossos, Xavier Serra:
Evaluating Off-the-Shelf Machine Listening and Natural Language Models for Automated Audio Captioning. 60-64 - Diego de Benito-Gorrón, Sergio Segovia, Daniel Ramos, Doroteo T. Toledano:
Multiple Feature Resolutions for Different Polyphonic Sound Detection Score Scenarios in DCASE 2021 Task 4. 65-69 - Andreas Triantafyllopoulos, Manuel Milling, Konstantinos Drossos, Björn W. Schuller:
Fairness and Underspecification in Acoustic Scene Classification: The Case for Disaggregated Evaluations. 70-74 - Yih-Wen Wang, Chia-Ping Chen, Chung-Li Lu, Bo-Cheng Chan:
Semi-supervised Sound Event Detection Using Multiscale Channel Attention and Multiple Consistency Training. 75-79 - Mattson Ogg, Benjamin Skerritt-Davis:
Acoustic Event Detection Using Speaker Recognition Techniques: Model Optimization and Explainable Features. 80-84 - Irene Martín-Morató, Toni Heittola, Annamaria Mesaros, Tuomas Virtanen:
Low-Complexity Acoustic Scene Classification for Multi-Device Audio: Analysis of DCASE 2021 Challenge Systems. 85-89 - Irene Martín-Morató, Annamaria Mesaros:
Diversity and Bias in Audio Captioning Datasets. 90-94 - Soichiro Okazaki, Quan Kong, Tomoaki Yoshinaga:
A Multi-Modal Fusion Approach for Audio-Visual Scene Classification Enhanced by CLIP Variants. 95-99 - Parthasaarathy Sudarsanam, Archontis Politis, Konstantinos Drossos:
Assessment of Self-Attention on Learned Features For Sound Event Localization and Detection. 100-104 - Sooyoung Park, Youngho Jeong, Taejin Lee:
Many-to-Many Audio Spectrogram Tansformer: Transformer for Sound Event Localization and Detection. 105-109 - Ibuki Kuroyanagi, Tomoki Hayashi, Yusuke Adachi, Takenori Yoshimura, Kazuya Takeda, Tomoki Toda:
An Ensemble Approach to Anomalous Sound Detection Based on Conformer-Based Autoencoder and Binary Classifier Incorporated with Metric Learning. 110-114 - Francesca Ronchini, Romain Serizel, Nicolas Turpault, Samuele Cornell:
The Impact of Non-Target Events in Synthetic Soundscapes for Sound Event Detection. 115-119 - Thi Ngoc Tho Nguyen, Karn N. Watcharasupat, Zhen Jian Lee, Ngoc Khanh Nguyen, Douglas L. Jones, Woon-Seng Gan:
What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysis. 120-124 - Archontis Politis, Sharath Adavanne, Daniel Krause, Antoine Deleforge, Prerak Srivastava, Tuomas Virtanen:
A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection. 125-129 - Sun Xinghao:
Sound Event Localization and Detection Based on Adaptive Hybrid Convolution and Multi-scale Feature Extractor. 130-134 - Nagashree Rao, Nils G. Peters:
On the Effect of Coding Artifacts on Acoustic Scene Classification. 135-139 - Jan Berg, Konstantinos Drossos:
Continual Learning for Automated Audio Captioning Using the Learning without Forgetting Approach. 140-144 - Veronica Morfi, Inês Nolasco, Vincent Lostanlen, Shubhr Singh, Ariana Strandburg-Peshkin, Lisa F. Gill, Hanna Pamula, David Benvent, Dan Stowell:
Few-Shot Bioacoustic Event Detection: A New Task at the DCASE 2021 Challenge. 145-149 - Stepan Shishkin, Danilo Hollosi, Simon Doclo, Stefan Goetze:
Active Learning for Sound Event Classification using Monte-Carlo Dropout and PANN Embeddings. 150-154 - Xiujuan Zhu, Sun Xinghao:
Multi-Scale Network based on Split Attention for Semi-supervised Sound event detection. 155-159 - Chaitanya Prasad Narisetty, Tomoki Hayashi, Ryunosuke Ishizaki, Shinji Watanabe, Kazuya Takeda:
Leveraging State-of-the-art ASR Techniques to Audio Captioning. 160-164 - Andres Fernandez, Mark D. Plumbley:
Using UMAP to Inspect Audio Data for Unsupervised Anomaly Detection Under Domain-Shift Conditions. 165-169 - Félix Gontier, Romain Serizel, Christophe Cerisara:
Automated Audio Captioning by Fine-Tuning BART with AudioSet Tags. 170-174 - Irán R. Román, Juan Pablo Bello:
micarraylib: Software for Reproducible Aggregation, Standardization, and Signal Processing of Microphone Array Datasets. 175-180 - Anthea Cheung, Qingming Tang, Chieh-Chi Kao, Ming Sun, Chao Wang:
Improved Student Model Training for Acoustic Event Detection Models. 181-185 - Yohei Kawaguchi, Keisuke Imoto, Yuma Koizumi, Noboru Harada, Daisuke Niizumi, Kota Dohi, Ryo Tanabe, Harsh Purohit, Takashi Endo:
Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Detection for Machine Condition Monitoring Under Domain Shifted Conditions. 186-190 - Magdalena Fuentes, Danielle Zhao, Vincent Lostanlen, Mark Cartwright, Charlie Mydlarz, Juan Pablo Bello:
MONYC: Music of New York City Dataset. 191-195 - Xubo Liu, Qiushi Huang, Xinhao Mei, Tom Ko, H. Lilian Tang, Mark D. Plumbley, Wenwu Wang:
CL4AC: A Contrastive Loss for Audio Captioning. 196-200 - Turab Iqbal, Yin Cao, Andrew Bailey, Mark D. Plumbley, Wenwu Wang:
ARCA23K: An Audio Dataset for Investigating Open-Set Label Noise. 201-205 - Xinhao Mei, Qiushi Huang, Xubo Liu, Gengyun Chen, Jingqian Wu, Yusong Wu, Jinzheng Zhao, Shengchen Li, Tom Ko, H. Lilian Tang, Xi Shao, Mark D. Plumbley, Wenwu Wang:
An Encoder-Decoder Based Audio Captioning System with Transfer and Reinforcement Learning. 206-210 - Xinhao Mei, Xubo Liu, Qiushi Huang, Mark D. Plumbley, Wenwu Wang:
Audio Captioning Transformer. 211-215 - Dennis Fedorishin, Nishant Sankaran, Deen Dayal Mohan, Justas Birgiolas, Philip Schneider, Srirangaraj Setlur, Venu Govindaraju:
Waveforms and Spectrograms: Enhancing Acoustic Scene Classification Using Multimodal Feature Fusion. 216-220 - Baekseung Kim, Hyejin Won, Il-Youp Kwak, Changwon Lim:
Transfer Learning followed by Transformer for Automated Audio Captioning. 221-225 - Janek Ebbers, Reinhold Haeb-Umbach:
Self-Trained Audio Tagging and Sound Event Detection in Domestic Environments. 226-230 - Michel Olvera:
Improving Sound Event Detection with Foreground-Background Classification and Domain Adaptation. 231-235
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