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.
翻译:声音的感知响声和物理特性之间的关系是计算机音乐和心理心理学的一个重要主题。早期的“等声等距”研究可以追溯到1920年代,从那时以来,对强度和频率的测高声度进行了多次修改。然而,大多数研究仅仅侧重于合成声,而关于具有复杂音调的自然音调的引导理论很少被证明是合理的。为此,我们通过模拟钢琴音调来研究本文中自然调音感知的理论和应用。理论部分包括:(1) 准确测量钢琴-调等音等同音等同音等同音等同音,和(2) 机器学习模型,纯粹根据经过人类主题测量培训的光谱特征推断出响亮度。关于应用,我们把理论应用于钢琴控制传输,在其中我们调整两种不同钢琴钢琴演奏器(在不同音响响环境中)上的MIDI速度,以取得同样的感知效果。实验表明,我们的理论声震建模和相应的性能控制转换都大大超出其基线。