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Developing digital sound synthesizers is crucial to the music industry as itprovides a low-cost way to produce high-quality sounds with rich timbres.Existing traditional synthesizers often require substantial expertise todetermine the overall framework of a synthesizer and the parameters ofsubmodules. Since expert knowledge is hard to acquire, it hinders theflexibility to quickly design and tune digital synthesizers for diverse sounds.In this paper, we propose ``NAS-FM'', which adopts neural architecture search(NAS) to build a differentiable frequency modulation (FM) synthesizer. Tunablesynthesizers with interpretable controls can be developed automatically fromsounds without any prior expert knowledge and manual operating costs. Indetail, we train a supernet with a specifically designed search space,including predicting the envelopes of carriers and modulators with differentfrequency ratios. An evolutionary search algorithm with adaptive oscillatorsize is then developed to find the optimal relationship between oscillators andthe frequency ratio of FM. Extensive experiments on recordings of differentinstrument sounds show that our algorithm can build a synthesizer fullyautomatically, achieving better results than handcrafted synthesizers. Audiosamples are available at https://nas-fm.github.io/.

NAS-FM: Neural Architecture Search for Tunable and Interpretable Sound Synthesis based on Frequency Modulation