Abstract
We explore two approaches to creatively altering vocal timbre using Differentiable Digital Signal Processing (DDSP). The first approach is inspired by classic cross-synthesis techniques. A pretrained DDSP decoder predicts a filter for a noise source and a harmonic distribution, based on pitch and loudness information extracted from the vocal input. Before synthesis, the harmonic distribution is modified by interpolating between the predicted distribution and the harmonics of the input. We provide a real-time implementation of this approach in the form of a Neutone model. In the second approach, autoencoder models are trained on datasets consisting of both vocal and instrument training data. To apply the effect, the trained autoencoder attempts to reconstruct the vocal input. We find that there is a desirable “sweet spot” during training, where the model has learned to reconstruct the phonetic content of the input vocals, but is still affected by the timbre of the instrument mixed into the training data. After further training, that effect disappears. A perceptual evaluation compares the two approaches. We find that the autoencoder in the second approach is able to reconstruct intelligible lyrical content without any explicit phonetic information provided during training.
Original language | English |
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Book series | Proceedings of the International Conference on Digital Audio Effects, DAFx |
Pages (from-to) | 363-366 |
Number of pages | 4 |
ISSN | 2413-6700 |
Publication status | Published - 2023 |
Event | 26th International Conference on Digital Audio Effects, DAFx 2023 - Copenhagen, Denmark Duration: 4 Sept 2023 → 7 Sept 2023 |
Conference
Conference | 26th International Conference on Digital Audio Effects, DAFx 2023 |
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Country/Territory | Denmark |
City | Copenhagen |
Period | 04/09/2023 → 07/09/2023 |
Sponsor | Ableton, AudioKinetic, et al., EURAL - Algorithmically Perfect, Native Instruments, Soundtoys |
Bibliographical note
Publisher Copyright:© 2023 David Südholt et al.