Abstract

The clarinet is a commonly used woodwind instrument that has a unique sound quality, making it a prominent instrument in solo performances and orchestral music. Therefore, sound quality plays an important role in the perception of clarinet sound. This study provides a review of the relevant research in the field of clarinet sound analysis and synthesis, and identifies many valuable references from previous studies. In studying the noise component of clarinet sound, this study recorded audio samples of E3, E4, and E5 and preprocessed them by slicing and performing frequency domain transformations. Using 13 representative spectral features, this paper extracted features from the spectral signals and constructed a classification model of sound quality based on the SVM algorithm of machine learning. Finally, this study analyzed the shortcomings of the SVM algorithm in high-pitch analysis and proposed future research directions of combining the SVM algorithm with different sound characteristics of clarinet registers and spectral changes throughout the entire process of playing. These results shed light on guiding further exploration of the timbre of the clarinet.

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