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A differentiable digital signal processing (DDSP) autoencoder is a musicalsound synthesizer that combines a deep neural network (DNN) and spectralmodeling synthesis. It allows us to flexibly edit sounds by changing thefundamental frequency, timbre feature, and loudness (synthesis parameters)extracted from an input sound. However, it is designed for a monophonicharmonic sound and cannot handle mixtures of harmonic sounds. In this paper, wepropose a model (DDSP mixture model) that represents a mixture as the sum ofthe outputs of multiple pretrained DDSP autoencoders. By fitting the output ofthe proposed model to the observed mixture, we can directly estimate thesynthesis parameters of each source. Through synthesis parameter extractionexperiments, we show that the proposed method has high and stable performancecompared with a straightforward method that applies the DDSP autoencoder to thesignals separated by an audio source separation method.

Differentiable Digital Signal Processing Mixture Model for Synthesis Parameter Extraction from Mixture of Harmonic Sounds