Table of Contents

• ToyADMOS2: Another Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection under Domain Shift ConditionsNoboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, Shoichiro Saito. 1-5 [doi]
• Automated Audio Captioning with Weakly Supervised Pre-Training and Word Selection MethodsQichen Han, Weiqiang Yuan 0004, Dong Liu, Xiang Li, Zhen Yang. 6-10 [doi]
• Ensemble Of Complementary Anomaly Detectors Under Domain Shifted ConditionsJose A Lopez, Georg Stemmer, Paulo Lopez-Meyer, Pradyumna Singh, Juan A. del Hoyo Ontiveros, Héctor A. Cordourier. 11-15 [doi]
• Squeeze-Excitation Convolutional Recurrent Neural Networks for Audio-Visual Scene ClassificationJavier Naranjo-Alcazar, Sergi Perez-Castanos, Maximo Cobos, Francesc J. Ferri, Pedro Zuccarello. 16-20 [doi]
• Domain Generalization on Efficient Acoustic Scene Classification Using Residual NormalizationByeonggeun Kim, Seunghan Yang, Jangho Kim, Simyung Chang. 21-25 [doi]
• Detecting Presence Of Speech In Acoustic Data Obtained From BeehivesPascal Janetzky, Padraig Davidson, Michael Steininger, Anna Krause, Andreas Hotho. 26-30 [doi]
• A Contrastive Semi-Supervised Learning Framework For Anomaly Sound DetectionXinyu Cai, Heinrich Dinkel. 31-34 [doi]
• A Lightweight Approach for Semi-Supervised Sound Event Detection with Unsupervised Data AugmentationXinyu Cai, Heinrich Dinkel. 35-39 [doi]
• Improving the Performance of Automated Audio Captioning via Integrating the Acoustic and Semantic InformationZhongjie Ye, Helin Wang, Dongchao Yang, Yuexian Zou. 40-44 [doi]
• Audio-Visual Scene Classification: Analysis of DCASE 2021 Challenge SubmissionsShanshan Wang, Annamaria Mesaros, Toni Heittola, Tuomas Virtanen. 45-49 [doi]
• Toward Interpretable Polyphonic Sound Event Detection with Attention Maps Based on Local PrototypesPablo Zinemanas, Martín Rocamora, Eduardo Fonseca, Frederic Font, Xavier Serra. 50-54 [doi]
• Combining Multiple Distributions based on Sub-Cluster AdaCos for Anomalous Sound Detection under Domain Shifted ConditionsKevin Wilkinghoff. 55-59 [doi]
• Evaluating Off-the-Shelf Machine Listening and Natural Language Models for Automated Audio CaptioningBenno Weck, Xavier Favory, Konstantinos Drossos, Xavier Serra. 60-64 [doi]
• Multiple Feature Resolutions for Different Polyphonic Sound Detection Score Scenarios in DCASE 2021 Task 4Diego de Benito-Gorrón, Sergio Segovia, Daniel Ramos, Doroteo T. Toledano. 65-69 [doi]
• Fairness and Underspecification in Acoustic Scene Classification: The Case for Disaggregated EvaluationsAndreas Triantafyllopoulos, Manuel Milling, Konstantinos Drossos, Björn W. Schuller. 70-74 [doi]
• Semi-supervised Sound Event Detection Using Multiscale Channel Attention and Multiple Consistency TrainingYih-Wen Wang, Chia-Ping Chen, Chung-Li Lu, Bo-Cheng Chan. 75-79 [doi]
• Acoustic Event Detection Using Speaker Recognition Techniques: Model Optimization and Explainable FeaturesMattson Ogg, Benjamin Skerritt-Davis. 80-84 [doi]
• Low-Complexity Acoustic Scene Classification for Multi-Device Audio: Analysis of DCASE 2021 Challenge SystemsIrene Martín-Morató, Toni Heittola, Annamaria Mesaros, Tuomas Virtanen. 85-89 [doi]
• Diversity and Bias in Audio Captioning DatasetsIrene Martín-Morató, Annamaria Mesaros. 90-94 [doi]
• A Multi-Modal Fusion Approach for Audio-Visual Scene Classification Enhanced by CLIP VariantsSoichiro Okazaki, Quan Kong, Tomoaki Yoshinaga. 95-99 [doi]
• Assessment of Self-Attention on Learned Features For Sound Event Localization and DetectionParthasaarathy Sudarsanam, Archontis Politis, Konstantinos Drossos. 100-104 [doi]
• Many-to-Many Audio Spectrogram Tansformer: Transformer for Sound Event Localization and DetectionSooyoung Park, Youngho Jeong, TaeJin Lee. 105-109 [doi]
• An Ensemble Approach to Anomalous Sound Detection Based on Conformer-Based Autoencoder and Binary Classifier Incorporated with Metric LearningIbuki Kuroyanagi, Tomoki Hayashi, Yusuke Adachi, Takenori Yoshimura, Kazuya Takeda, Tomoki Toda. 110-114 [doi]
• The Impact of Non-Target Events in Synthetic Soundscapes for Sound Event DetectionFrancesca Ronchini, Romain Serizel, Nicolas Turpault, Samuele Cornell. 115-119 [doi]
• What Makes Sound Event Localization and Detection Difficult? Insights from Error AnalysisThi Ngoc Tho Nguyen, Karn N. Watcharasupat, Zhen Jian Lee, Ngoc Khanh Nguyen 0003, Douglas L. Jones, Woon-Seng Gan. 120-124 [doi]
• A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and DetectionArchontis Politis, Sharath Adavanne, Daniel Krause 0001, Antoine Deleforge, Prerak Srivastava, Tuomas Virtanen. 125-129 [doi]
• Sound Event Localization and Detection Based on Adaptive Hybrid Convolution and Multi-scale Feature ExtractorSun Xinghao. 130-134 [doi]
• On the Effect of Coding Artifacts on Acoustic Scene ClassificationNagashree Rao, Nils G. Peters. 135-139 [doi]
• Continual Learning for Automated Audio Captioning Using the Learning without Forgetting ApproachJan Berg, Konstantinos Drossos. 140-144 [doi]
• Few-Shot Bioacoustic Event Detection: A New Task at the DCASE 2021 ChallengeVeronica Morfi, Inês Nolasco, Vincent Lostanlen, Shubhr Singh, Ariana Strandburg-Peshkin, Lisa F. Gill, Hanna Pamula, David Benvent, Dan Stowell. 145-149 [doi]
• Active Learning for Sound Event Classification using Monte-Carlo Dropout and PANN EmbeddingsStepan Shishkin, Danilo Hollosi, Simon Doclo, Stefan Goetze. 150-154 [doi]
• Multi-Scale Network based on Split Attention for Semi-supervised Sound event detectionXiujuan Zhu, Sun Xinghao. 155-159 [doi]
• Leveraging State-of-the-art ASR Techniques to Audio CaptioningChaitanya Prasad Narisetty, Tomoki Hayashi, Ryunosuke Ishizaki, Shinji Watanabe 0001, Kazuya Takeda. 160-164 [doi]
• Using UMAP to Inspect Audio Data for Unsupervised Anomaly Detection Under Domain-Shift ConditionsAndres Fernandez, Mark D. Plumbley. 165-169 [doi]
• Automated Audio Captioning by Fine-Tuning BART with AudioSet TagsFélix Gontier, Romain Serizel, Christophe Cerisara. 170-174 [doi]
• micarraylib: Software for Reproducible Aggregation, Standardization, and Signal Processing of Microphone Array DatasetsIrán R. Roman, Juan Pablo Bello. 175-180 [doi]
• Improved Student Model Training for Acoustic Event Detection ModelsAnthea Cheung, Qingming Tang, Chieh-Chi Kao, Ming Sun 0007, Chao Wang 0018. 181-185 [doi]
• Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Detection for Machine Condition Monitoring Under Domain Shifted ConditionsYohei Kawaguchi, Keisuke Imoto, Yuma Koizumi, Noboru Harada, Daisuke Niizumi, Kota Dohi, Ryo Tanabe, Harsh Purohit, Takashi Endo. 186-190 [doi]
• MONYC: Music of New York City DatasetMagdalena Fuentes, Danielle Zhao, Vincent Lostanlen, Mark Cartwright, Charlie Mydlarz, Juan Pablo Bello. 191-195 [doi]
• CL4AC: A Contrastive Loss for Audio CaptioningXubo Liu, Qiushi Huang, Xinhao Mei, Tom Ko, H. Tang, Mark D. Plumbley, Wenwu Wang. 196-200 [doi]
• ARCA23K: An Audio Dataset for Investigating Open-Set Label NoiseTurab Iqbal, Yin Cao, Andrew Bailey, Mark D. Plumbley, Wenwu Wang. 201-205 [doi]
• An Encoder-Decoder Based Audio Captioning System with Transfer and Reinforcement LearningXinhao Mei, Qiushi Huang, Xubo Liu, Gengyun Chen, Jingqian Wu, Yusong Wu, Jinzheng Zhao, Shengchen Li, Tom Ko, H. Tang, Xi Shao, Mark D. Plumbley, Wenwu Wang. 206-210 [doi]
• Audio Captioning TransformerXinhao Mei, Xubo Liu, Qiushi Huang, Mark D. Plumbley, Wenwu Wang. 211-215 [doi]
• Waveforms and Spectrograms: Enhancing Acoustic Scene Classification Using Multimodal Feature FusionDennis Fedorishin, Nishant Sankaran, Deen Dayal Mohan, Justas Birgiolas, Philip Schneider, Srirangaraj Setlur, Venu Govindaraju. 216-220 [doi]
• Transfer Learning followed by Transformer for Automated Audio CaptioningBaekseung Kim, Hyejin Won, Il-Youp Kwak, Changwon Lim. 221-225 [doi]
• Self-Trained Audio Tagging and Sound Event Detection in Domestic EnvironmentsJanek Ebbers, Reinhold Haeb-Umbach. 226-230 [doi]
• Improving Sound Event Detection with Foreground-Background Classification and Domain AdaptationMichel Olvera. 231-235 [doi]