Lars Ødegaard Bentsen

• Bentsen, Lars Ødegaard; Warakagoda, Narada Dilp; Stenbro, Roy & Engelstad, Paal (2024). Relative evaluation of probabilistic methods for spatio-temporal wind forecasting. Journal of Cleaner Production. ISSN 0959-6526. 434. doi: 10.1016/j.jclepro.2023.139944. Full text in Research Archive
• Bentsen, Lars Ødegaard; Warakagoda, Narada Dilp; Stenbro, Roy & Engelstad, Paal E. (2023). A Unified Graph Formulation for Spatio-Temporal Wind Forecasting. Energies. ISSN 1996-1073. 16(20). doi: 10.3390/en16207179. Full text in Research Archive
• Bentsen, Lars Ødegaard; Warakagoda, Narada Dilp; Stenbro, Roy & Engelstad, Paal (2022). Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures. Applied Energy. ISSN 0306-2619. 333. doi: 10.1016/j.apenergy.2022.120565. Full text in Research Archive
• Bentsen, Lars Ødegaard; Warakagoda, Narada Dilp; Stenbro, Roy & Engelstad, Paal (2022). Probabilistic Wind Park Power Prediction using Bayesian Deep Learning and Generative Adversarial Networks. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 2362. doi: 10.1088/1742-6596/2362/1/012005. Full text in Research Archive
• Bentsen, Lars Ødegaard; Simionato, Riccardo; Wallace, Benedikte & Krzyzaniak, Michael Joseph (2022). Transformer and LSTM Models for Automatic Counterpoint Generation using Raw Audio. Proceedings of the SMC Conferences. ISSN 2518-3672. doi: 10.5281/zenodo.6572847. Full text in Research Archive Show summary

  A study investigating Transformer and LSTM models applied to raw audio for automatic generation of counterpoint was conducted. In particular, the models learned to generate missing voices from an input melody, using a collection of raw audio waveforms of various pieces of Bach’s work, played on different instruments. The research demonstrated the efficacy and behaviour of the two deep learning (DL) architectures when applied to raw audio data, which are typically characterised by much longer sequences than symbolic music representations, such as MIDI. Currently, the LSTM model has been the quintessential DL model for sequence-based tasks, such as generative audio models, but the research conducted in this study shows that the Transformer model can achieve competitive results on a fairly complex raw audio task. The research therefore aims to spark further research and investigation into how Trans- former models can be used for applications typically dominated by recurrent neural networks (RNN). In general, both models yielded excellent results and generated sequences with temporal patterns similar to the input targets for songs that were not present in the training data, as well as for a sample taken from a completely different dataset.

• Bentsen, Lars Ødegaard; Warakagoda, Narada Dilp; Stenbro, Roy & Engelstad, Paal (2022). Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 2265. doi: 10.1088/1742-6596/2265/2/022035. Full text in Research Archive

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