Hulusi timbre reveals pitch through chaotic sound patterns
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Researchers used machine learning to analyse the sound of the hulusi, a free-reed wind instrument from Yunnan Province in China.
The team trained a model to identify patterns in the instrument’s timbre, its unique sound quality, and group different pitches. Unlike instruments like the accordion, the hulusi’s pitch is determined by the length of its bamboo tubes rather than the vibration of its reeds.
The analysis focused on seven psychoacoustic features, but the researchers found that spectral centroid, sharpness, and fractal correlation dimension were most effective at distinguishing pitches. They used these features to train Kohonen self-organizing maps, a type of machine learning algorithm, to cluster the sounds. The study revealed that higher pitches on the hulusi tend to be less bright and less sharp than lower pitches.
Importantly, the fractal correlation dimension, a measure of the chaotic nature of the sound’s initial bursts, proved to be the most reliable indicator of pitch. Higher pitches exhibited less chaoticity, a finding supported by a cluster quality index the researchers developed to evaluate the model’s performance. This research provides a detailed acoustic profile of the hulusi and demonstrates how machine learning can be used to understand the characteristics of non-Western instruments.
Further work could explore the impact of different playing styles or materials on the hulusi’s timbre.
