Recent technological developments are having a significant impact on musical instruments and singing voice learning. A proof is the number of successful software applications that are being used by aspiring musicians in their regular practice. These practicing apps offer many useful functionalities to support learning, including performance assessment technologies that analyze the sound produced by the student while playing, identifying performance errors and giving useful feedback. However, despite the advancements in these sound analysis technologies, they are still not reliable and effective enough to support the strict requirements of a professional music education context. In this article we first introduce the topic and context, reviewing some of the work done in the practice of music assessment, then going over the current state of the art in performance assessment technologies, and presenting, as a proof of concept, a complete assessment system that we have developed for supporting guitar exercises. We conclude by identifying the challenges that should be addressed in order to further advance these assessment technologies and their useful integration into professional learning contexts.
In this paper, we introduce score difficulty classification as a subtask of music information retrieval (MIR), which may be used in music education technologies, for personalised curriculum generation, and score retrieval. We introduce a novel dataset for our task, Mikrokosmos-difficulty, containing 147 piano pieces in symbolic representation and the corresponding difficulty labels derived by its composer Béla Bartók and the publishers. As part of our methodology, we propose piano technique feature representations based on different piano fingering algorithms. We use these features as input for two classifiers: a Gated Recurrent Unit neural network (GRU) with attention mechanism and gradient-boosted trees trained on score segments. We show that for our dataset fingering based features perform better than a simple baseline considering solely the notes in the score. Furthermore, the GRU with attention mechanism classifier surpasses the gradient-boosted trees. Our proposed models are interpretable and are capable of generating difficulty feedback both locally, on short term segments, and globally, for whole pieces. Code, datasets, models, and an online demo are made available for reproducibility.
No abstract
In this paper, we introduce score difficulty classification as a subtask of music information retrieval (MIR), which may be used in music education technologies, for personalised curriculum generation, and score retrieval. We introduce a novel dataset for our task, Mikrokosmos-difficulty, containing 147 piano pieces in symbolic representation and the corresponding difficulty labels derived by its composer Béla Bartók and the publishers. As part of our methodology, we propose piano technique feature representations based on different piano fingering algorithms. We use these features as input for two classifiers: a Gated Recurrent Unit neural network (GRU) with attention mechanism and gradient-boosted trees trained on score segments. We show that for our dataset fingering based features perform better than a simple baseline considering solely the notes in the score. Furthermore, the GRU with attention mechanism classifier surpasses the gradient-boosted trees. Our proposed models are interpretable and are capable of generating difficulty feedback both locally, on short term segments, and globally, for whole pieces. Code, datasets, models, and an online demo are made available for reproducibility.
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