Active learning of intuitive control knobs for synthesizers using gaussian processes
By Cheng-Zhi Anna Huang, David Kristjanson Duvenaud, Kenneth C. Arnold, B. Partridge, Josiah Oberholtzer, Krzysztof Z Gajos. 2014
14 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition
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Citations in the corpus
- DDSP: Differentiable Digital Signal Processing (2020, neutral): “A separate thread of research has tried to estimate parameters for commercial syn-thesizers using gradient-free methods (Huang et al., 2014; Hoffman & Cook, 2006).”
- Human-AI Co-Creation in Songwriting (2020, background): “rpoint [22, 32], harmonizing melodies [13, 43, 51], more general infilling [29, 35, 41, 61], exploring more adventurous chord progressions [27, 37, 49], semantic controls to music and sound generation [11, 24, 36, 63], building new instruments through custom mappings or unsupervised learning [20,25],”