10 citations · 31 across the 7 of their papers we have counts for
8 papers
Perception of AI-Generated Music -- The Role of Composer Identity, Personality Traits, Music Preferences, and Perceived Humanness
David Stammer, Hannah Strauss, Peter Knees
The rapid rise of AI-generated art has sparked debate about potential biases in how audiences perceive and evaluate such works. This study investigates how composer information and…
Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning
Pavan Seshadri, Shahrzad Shashaani, Peter Knees
Modern music streaming services are heavily based on recommendation engines to serve content to users. Sequential recommendation -- continuously providing new items within a single…
Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation
Pavan Seshadri, Peter Knees
Music streaming services heavily rely on their recommendation engines to continuously provide content to their consumers. Sequential recommendation consequently has seen considerab…
Content-driven Music Recommendation: Evolution, State of the Art, and Challenges
Yashar Deldjoo, Markus Schedl, Peter Knees
The music domain is among the most important ones for adopting recommender systems technology. In contrast to most other recommendation domains, which predominantly rely on collabo…
Unsupervised Cross-Modal Audio Representation Learning from Unstructured Multilingual Text
Alexander Schindler, Sergiu Gordea, Peter Knees
We present an approach to unsupervised audio representation learning. Based on a triplet neural network architecture, we harnesses semantically related cross-modal information to e…
Multi-Task Music Representation Learning from Multi-Label Embeddings
Alexander Schindler, Peter Knees
This paper presents a novel approach to music representation learning. Triplet loss based networks have become popular for representation learning in various multimedia retrieval d…