ARI guest talk by Arthur Flexer

13. February 2019

14.30 o'clock

Seminar Room, Wohllebengasse 12-14 / Ground Floor 

In applying machine learning to musical audio signals, the general goal is to learn a mapping from an input (e.g. a song) to an output (e.g. an annotation such as genre). Since the human perception of music and its annotation is highly subjective with low inter-rater agreement, the validity of such machine learning experiments is unclear. Because it is not meaningful to have computational models that go beyond the level of human agreement, these levels of inter-rater agreement present a natural upper bound for any algorithmic approach. We illustrate this fundamental evaluation problem using results from modeling music similarity between pieces of music, as utilized in automatic music recommendation.


Upcoming Events

Improving speech technology with the open source VOiCES dataset

ARI guest talk by Michael R. Lomnitz

19. September 2019


Seminar Room, Wohllebengasse 12-14 / Ground Floor

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The 10th ISCA Speech Synthesis Workshop

20. - 22. September 2019

Vienna, Austria