2024W

• Intelligent Audio and Music Analysis / 194.039 / VU

• Coordinated approacH to the EurOpean effoRt on aUdio-visual Search engines
  2010 – 2013 / European Commission

• Unsupervised cross-modal audio representation learning from unstructured multilingual text / Schindler, A., Gordea, S., & Knees, P. (2020). Unsupervised cross-modal audio representation learning from unstructured multilingual text. In Proceedings of the 35th Annual ACM Symposium on Applied Computing. 35th Annual ACM Symposium on Applied Computing (SAC ´20), Brno, Czechia. ACM. https://doi.org/10.1145/3341105.3374114
• Multi-modal music information retrieval: augmenting audio-analysis with visual computing for improved music Video analysis / Schindler, A. (2019). Multi-modal music information retrieval: augmenting audio-analysis with visual computing for improved music Video analysis [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2019.72065
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• Multi-Task Music Representation Learning from Multi-Label Embeddings / Schindler, A., & Knees, P. (2019). Multi-Task Music Representation Learning from Multi-Label Embeddings. In 2019 International Conference on Content-Based Multimedia Indexing (CBMI). 2019 International Conference on Content-Based Multimedia Indexing (CBMI), Dublin, Ireland. IEEE. https://doi.org/10.1109/cbmi.2019.8877462
• Fashion and Apparel Classification using Convolutional Neural Networks / Schindler, A., Lidy, T., Karner, S., & Hecker, M. (2017). Fashion and Apparel Classification using Convolutional Neural Networks. In 10th Forum Media Technology 2017 (p. 4). http://hdl.handle.net/20.500.12708/57293
• Multi-Temporal Resolution Convolutional Neural Networks for the DCASE Acoustic Scene Classification Task / Schindler, A., Lidy, T., & Rauber, A. (2017). Multi-Temporal Resolution Convolutional Neural Networks for the DCASE Acoustic Scene Classification Task. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017) (p. 5). http://hdl.handle.net/20.500.12708/57295
• Multi-Temporal Resolution Convolutional Neural Networks for Acoustic Scene Classification / Schindler, A., Lidy, T., & Rauber, A. (2017). Multi-Temporal Resolution Convolutional Neural Networks for Acoustic Scene Classification. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017) (pp. 118–122). http://hdl.handle.net/20.500.12708/57294
• A Multi-modal Deep Neural Network approach to Bird-song identification / Fazekas, B., Schindler, A., & Lidy, T. (2017). A Multi-modal Deep Neural Network approach to Bird-song identification. In BirdCLEF 2017 (p. 6). http://hdl.handle.net/20.500.12708/57291
• Harnessing Music-Related Visual Stereotypes for Music Information Retrieval / Schindler, A., & Rauber, A. (2016). Harnessing Music-Related Visual Stereotypes for Music Information Retrieval. ACM Transactions on Intelligent Systems and Technology, 8(2), 1–21. https://doi.org/10.1145/2926719
• Comparing shallow versus deep neural network architectures for automatic music genre classification / Schindler, A., Lidy, T., & Rauber, A. (2016). Comparing shallow versus deep neural network architectures for automatic music genre classification. In Proceedings of the 9th Forum Media Technology (FMT2016) (p. 5). St. Pölten University of Applied Sciences, Institute of Creative\Media/Technologies. http://hdl.handle.net/20.500.12708/56807
• CQT-based convolutional neural networks for audio scene classification / Lidy, T., & Schindler, A. (2016). CQT-based convolutional neural networks for audio scene classification. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2016 Workshop (DCASE2016) (p. 5). http://hdl.handle.net/20.500.12708/56806
• The Europeana Sounds Music Information Retrieval Pilot / Schindler, A., Gordea, S., & van Biessum, H. (2016). The Europeana Sounds Music Information Retrieval Pilot. In Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection (pp. 109–117). Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-48974-2_13
• Parallel convolutional neural networks for music genre and mood classification / Lidy, T., & Schindler, A. (2016). Parallel convolutional neural networks for music genre and mood classification. http://hdl.handle.net/20.500.12708/39105
• CQT-based convolutional neural networks for audio scene classification and domestic audio tagging / Lidy, T., & Schindler, A. (2016). CQT-based convolutional neural networks for audio scene classification and domestic audio tagging. http://hdl.handle.net/20.500.12708/39104
• Klingende Bausteine für die Industrie : Ein Projekt ebnet der Musiktechnologie den Weg in den Markt / Lidy, T., & Schindler, A. (2015). Klingende Bausteine für die Industrie : Ein Projekt ebnet der Musiktechnologie den Weg in den Markt. OCG Journal, 02, 17–18. https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:3-1232
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• MusicBricks: Connecting Digital Creators to the Internet of Music Things / Lidy, T., Schindler, A., & Magas, M. (2015). MusicBricks: Connecting Digital Creators to the Internet of Music Things. ERCIM News, 101, 39–40. https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:3-1227
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• Facilitating Comprehensive Benchmarking Experiments on the Million Song Dataset / Schindler, A., Mayer, R., & Rauber, A. (2012). Facilitating Comprehensive Benchmarking Experiments on the Million Song Dataset. In Proceedings of the 13th International Society for Music Information Retrieval Conference (ISMIR 2012) (pp. 469–474). http://hdl.handle.net/20.500.12708/54558
• Quality of service driven workflows within the Microsoft .NET environment / Schindler, A. (2009). Quality of service driven workflows within the Microsoft .NET environment [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-36503
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• Audio tampering detection: Deep learning methodologies for multi-layered threat detection / Dörömbözi, A. (2023). Audio tampering detection: Deep learning methodologies for multi-layered threat detection [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.96575
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• Predicting machine outages using deep learning / Tatowsky, A. (2021). Predicting machine outages using deep learning [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.56590
  Download: PDF (3.09 MB)
• Large-scale bird song identification using convolutional neural networks / Fazekas, B. (2018). Large-scale bird song identification using convolutional neural networks [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2018.55981
  Download: PDF (3.27 MB)