3 citations · 7 across the 4 of their papers we have counts for
6 papers
Adopting State-of-the-Art Pretrained Audio Representations for Music Recommender Systems
Yan-Martin Tamm, Anna Aljanaki
Over the years, Music Information Retrieval (MIR) research community has released various models pretrained on large amounts of music data. Transfer learning showcases the proven e…
Leveraging Artist Catalogs for Cold-Start Music Recommendation
Yan-Martin Tamm, Gregor Meehan, Vojtěch Nekl +4
The item cold-start problem poses a fundamental challenge for music recommendation: newly added tracks lack the interaction history that collaborative filtering (CF) requires. Exis…
Comparative Analysis of Pretrained Audio Representations in Music Recommender Systems
Yan-Martin Tamm, Anna Aljanaki
Over the years, Music Information Retrieval (MIR) has proposed various models pretrained on large amounts of music data. Transfer learning showcases the proven effectiveness of pre…
On the Characterization of Expressive Performance in Classical Music: First Results of the Con Espressione Game
Carlos Cancino-Chacón, Silvan Peter, Shreyan Chowdhury +2
A piece of music can be expressively performed, or interpreted, in a variety of ways. With the help of an online questionnaire, the Con Espressione Game, we collected some 1,500 de…
Modeling Majorness as a Perceptual Property in Music from Listener Ratings
Anna Aljanaki, Gerhard Widmer
For the tasks of automatic music emotion recognition, genre recognition, music recommendation it is helpful to be able to extract mode from any section of a musical piece as a perc…
A data-driven approach to mid-level perceptual musical feature modeling
Anna Aljanaki, Mohammad Soleymani
Musical features and descriptors could be coarsely divided into three levels of complexity. The bottom level contains the basic building blocks of music, e.g., chords, beats and ti…