Modeling Perceptual Loudness of Piano Tone: Theory and Applications
This work addresses the challenge of accurately predicting loudness for natural musical tones, which is important for applications in computer music and psychoacoustics, though it is incremental as it builds on existing loudness theories.
The paper tackles the problem of modeling perceptual loudness for natural piano tones, which previous studies had not adequately addressed for complex timbres, by developing a machine-learning model based on spectral features and applying it to piano control transfer, with experiments showing significant outperformance over baselines.
The relationship between perceptual loudness and physical attributes of sound is an important subject in both computer music and psychoacoustics. Early studies of "equal-loudness contour" can trace back to the 1920s and the measured loudness with respect to intensity and frequency has been revised many times since then. However, most studies merely focus on synthesized sound, and the induced theories on natural tones with complex timbre have rarely been justified. To this end, we investigate both theory and applications of natural-tone loudness perception in this paper via modeling piano tone. The theory part contains: 1) an accurate measurement of piano-tone equal-loudness contour of pitches, and 2) a machine-learning model capable of inferring loudness purely based on spectral features trained on human subject measurements. As for the application, we apply our theory to piano control transfer, in which we adjust the MIDI velocities on two different player pianos (in different acoustic environments) to achieve the same perceptual effect. Experiments show that both our theoretical loudness modeling and the corresponding performance control transfer algorithm significantly outperform their baselines.