主动学习直观的控制旋钮合成器使用高斯过程

Cheng-Zhi Anna Huang, D. Duvenaud, Kenneth C. Arnold, B. Partridge, Josiah Oberholtzer, Krzysztof Z Gajos
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引用次数: 12

摘要

典型的合成器只提供对声音合成的低级参数的控制,例如波形或滤波器包络。相比之下,作曲家通常想要调整和表达更高层次的品质,比如“可怕”或“稳定”的声音。我们开发了一个系统,允许用户直接控制抽象的,高质量的声音。为了做到这一点,我们的系统学习了从合成器控制设置映射到高水平质量感知水平的功能。考虑到这些功能,我们的系统可以生成高级旋钮,直接调整声音,使其具有或多或少的这些品质。我们使用高斯过程(一种非参数贝叶斯模型)对从控制参数到每个高质量程度的函数映射进行建模。这些模型可以适应学习函数的复杂性,考虑控制参数之间的非线性相互作用,并允许我们描述学习函数的不确定性。通过跟踪正在学习的功能的不确定性,我们可以使用主动学习来快速校准工具,通过向用户查询系统期望最能提高其性能的声音。我们通过模拟表明,这种基于模型的主动学习方法在某些类别的目标概念上学习高级旋钮比几个基线更快,并给出了自动构建的旋钮的例子,这些旋钮可以调整非线性、高级概念的水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Active learning of intuitive control knobs for synthesizers using gaussian processes
Typical synthesizers only provide controls to the low-level parameters of sound-synthesis, such as wave-shapes or filter envelopes. In contrast, composers often want to adjust and express higher-level qualities, such as how "scary" or "steady" sounds are perceived to be. We develop a system which allows users to directly control abstract, high-level qualities of sounds. To do this, our system learns functions that map from synthesizer control settings to perceived levels of high-level qualities. Given these functions, our system can generate high-level knobs that directly adjust sounds to have more or less of those qualities. We model the functions mapping from control-parameters to the degree of each high-level quality using Gaussian processes, a nonparametric Bayesian model. These models can adjust to the complexity of the function being learned, account for nonlinear interaction between control-parameters, and allow us to characterize the uncertainty about the functions being learned. By tracking uncertainty about the functions being learned, we can use active learning to quickly calibrate the tool, by querying the user about the sounds the system expects to most improve its performance. We show through simulations that this model-based active learning approach learns high-level knobs on certain classes of target concepts faster than several baselines, and give examples of the resulting automatically- constructed knobs which adjust levels of non-linear, high- level concepts.
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