In this article we describe the approximation we follow to analyze the performance of a singer when singing a reference song. The idea is to rate the performance of a singer in the same way that a music tutor would do it, not only giving a score but also giving feedback about how the user has performed regarding expression, tuning and tempo/timing characteristics. Also a discussion on what visual feedback should be relevant for the user is discussed. Segmentation at an intra-note level is done using an algorithm based on untrained HMMs with probabilistic models built out of a set of heuristic rules that determine regions and their probability of being expressive features. A real-time karaoke-like system is presented where a user can sing and visualize simultaneously feedback and results of the performance. The technology can be applied to a wide set of applications that range from pure entertainment to more serious education oriented.
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