AI-Music researcher & ML Engineer

Thesis: "Beyond Autonomous Creation in AI Music Generation: Multi-Agent Perception-Policy Separation with Process-Oriented Evaluation for User-Centric, Adaptive Systems"

Thesis: "Neural networks for Music Information Retrieval and algorithmic jazz composition"

Thesis: "Design and Development of E-Commerce Web Applications"

Digital Xalience AS

• Validated AI furniture recognition feasibility, assessing foundation models and zero-shot segmentation to inform strategic R&D roadmap
• Evaluated computer vision frameworks including YOLO and Roboflow for enterprise scalability across company sizes
• Assessed dataset availability and quality from Open Images, ShapeNet, Stanford 3D Scanning Repository for training data pipeline

NTNU Music Technology Department

• Investigated latent space representations in neural audio models for enhanced artistic control
• Developing user interaction modalities for enhanced expressive capabilities in neural audio synthesis systems for live electronics
• Advanced understanding of neural audio synthesis applications in live performance contexts

Interdisciplinary Art-AI Project with Artist, Pei-han Lin

• Developed generative models with StyleGAN by manipulating latent space vectors to control semantic features
• Created a framework for encoding features to explore generative dimensions in synthetic image creation
• Manipulated latent vectors along chosen directional paths with specific boundary conditions for effective generation control
• Integrated AI-generated content into multi-disciplinary installations spanning painting, sculpture, and sound art
• Featured in multiple exhibitions and various art galleries

Interreg-funded EU Collaboration | UiT & Lapland University of Applied Sciences

• Implemented CNN and LSTM neural networks for single-step and multi-step energy load forecasting
• Created multi-agent systems, utilizing zero-intelligence and reinforcement learning for energy markets and V2G (Vehicle-to-Grid) energy optimization
• Incorporated Arctic-specific variables including temperature fluctuations, occupancy patterns, and tourism demand
• Presented findings at CIRED 2023 conference in Rome

Unsupervised anomaly detection for variable-length audio loops using HTS-AT and Deep SVDD.

MIDI segmentation and loop extraction pipeline for symbolic music.

Templates and infrastructure for process-oriented evaluation of music generation systems.

Python implementation of Growing Hierarchical Self-Organizing Maps for unsupervised clustering.

Utilities for GHSOM visualization, analysis, and automatic curriculum extraction.

RL-based combinatorial music generation with hierarchical curriculum and inference-time adaptation.

Dadman, S., Bremdal, B. & Bergsland, A. (2026). Towards Reflective, Situated, and Practitioner-Focused Evaluation and Design Support in Human-AI Music Creation. Submitted to ACM Transactions on Computer-Human Interaction (TOCHI); under peer-review.

Dadman, S. & Bremdal, B. (2026). ARIA: Autonomous Reinforcement-Learning with Intelligent Abstraction for User-Centric Symbolic Music Generation. ResearchGate. doi:10.13140/RG.2.2.22913.31843

Dadman, S., Bremdal, B., Bang, B. & Dalmo, R. (2025). Learning Normal Patterns in Musical Loops. Northern Lights Deep Learning Conference. https://openreview.net/forum?id=Pr22XVnMW1

Dadman, S., Bremdal, B. & Bergsland, A. (2025). Workflow-Based Evaluation of Music Generation Systems: Open-Source Case Study. arXiv. doi:https://doi.org/10.48550/arXiv.2507.01022

Dadman, S. & Bremdal, B. (2024). Crafting Creative Melodies: A User-Centric Approach for Symbolic Music Generation. Electronics. doi:10.3390/electronics13061116

Dadman, S. (2023). Boosting Creativity with AI: Exploring Advanced Models, Multi-Agent Systems, and Design Grammar. . doi:10.13140/RG.2.2.24877.67041

Dadman, S. & Bremdal, B. (2023). Multi-Agent Reinforcement Learning for Structured Symbolic Music Generation. Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection. doi:10.1007/978-3-031-37616-0_5

Naeimaei, A. & Dadman, S. (2023). Mindful Integration of AI in the Design Industry: Opportunities and Implications. NORA Annual Conference.

Dadman, S. & Bremdal, B. (2023). Using Light Weight Electric Vehicles for V2G Services in the Arctic. IET Conference Proceedings. doi:10.1049/icp.2023.1230

Bremdal, B. & Dadman, S. (2023). Predicting Peak Prices in the Current Day-Ahead Market. IET Conference Proceedings. doi:10.1049/icp.2023.1244

Bremdal, B., Ilieva, I., Tangrand, K. & Dadman, S. (2023). E-Mobility and Batteries—A Business Case for Flexibility in the Arctic Region. World Electric Vehicle Journal. doi:10.3390/wevj14030061

Dadman, S., Bremdal, B., Bang, B. & Dalmo, R. (2022). Toward Interactive Music Generation: A Position Paper. IEEE Access. doi:10.1109/ACCESS.2022.3225689

Dadman, S., Bremdal, B. & Tangrand, K. (2021). The Role of Electric Snowmobiles and Rooftop Energy Production in the Arctic: The Case of Longyearbyen. J. Clean Energy Technol.

poster Learning Normal Patterns in Musical Loops — Northern Lights Deep Learning Conference 2026 (Tromsø, NO) [paper]

lecture Control and Explore: Neural Audio Synthesis with VAEs for Live-Electronics and Interactive Performances — Faglig Forum, Music Technology Department, NTNU, Norway (Trondheim, NO) [slides]

lecture Artificial Intelligence and Music: Deep Learning and Agents for Music Generation — SINTEF-ZEB Lab, Trondheim, Norway (Trondheim, NO) [slides] [video]

podcast A user-centric approach for symbolic music generation — CreateMe podcast, University of Agder (Agder, NO) [audio]

lecture Melody and Machine: Exploring AI's Role in Music Creation — Algoritmi, UiT The Arctic University of Norway (Tromso, NO) [slides] [video]

poster Integration and influence of artificial intelligence in the design industry — NORA Annual Conference 2023 (Oslo, NO) [paper]

demo Application of multi-agent systems and reinforcement learning methods in interactive music generation — AI+ Conference (Oslo, NO) [video] [code]

lecture Interactive music generation with Artificial Intelligence — University of Oslo, Brain Talk webinar (Oslo, NO) [video]

lecture Application of multi-agent systems and reinforcement learning methods in computational creativity and music generation — UiT, The Arctic University of Norway, Bodo (Bodo, NO) [slides]

exhibition Art x AI — UiT, The Arctic University of Norway, Narvik (Narvik, NO) [video]

exhibition If I Were Standing in your Shoes — Tromsø kunstforening (Tromsø, NO) [article]

exhibition This is a Protest Gesture to Showcase Norway's Violation of Basic Human Rights — Storgata (Tromsø, NO) [article]

presentation Application of deep learning methods in music generation — UiT, The Arctic University of Norway, Narvik PhD. Conference (Narvik, NO) [slides] [paper]

demo Application of deep learning methods in symbolic music generation — AI+ Conference (Oslo, NO) [video] [code]

demo Deep Jazz Composer — UiT, The Arctic University of Norway, Narvik Research Week (Narvik, NO) [video] [code]

interview Composing Jazz with Deep Learning — NRK P3 and Nyheter (Norway) [audio]

media Har utviklet kunstig intelligens som lager jazzmusikk — Forskning.no (Oslo, NO) [article]

media Utvikler kunstig intelligens som komponerer jazzmusikk — UiT Highlights (Oslo, NO) [article]

media Narvik-studenten utvikler kunstig intelligens som komponerer musikk — Fremover.no (Narvik, NO) [article]

media Musikk i koder — Jurnalen.oslomet.no (Oslo, NO) [article]