Music Performance Analysis is based on the evaluation of performance parameters such as pitch, dynamics, timbre, tempo and timing. While timbre is the least specific parameter among these and is often only implicitly understood, prominent brass pedagogues have reported that the presence of excessive muscle tension and inefficiency in playing by a musician is reflected in the timbre quality of the sound produced. In this work, we explore the application of machine learning to automatically assess timbre quality in trumpet playing, given both its educational value and connection to performance quality. An extensive dataset consisting of more than 19,000 tones played by 110 trumpet players of different expertise has been collected. A subset of 1,481 tones from this dataset was labeled by eight professional graders on a scale of 1 to 4 based on the perceived efficiency of sound production. Statistical analysis is performed to identify the correlation among the assigned ratings by the expert graders. A Random Forest classifier is trained using the mode of the ratings and its accuracy and variability is assessed with respect to the variability in human graders as a reference. An analysis of the important discriminatory features identifies stability of spectral peaks as a critical factor in trumpet timbre quality.

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