Vocal timbre effects with differentiable digital signal processing

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.</p

A Faust Implementation of Coupled Finite Difference Schemes

Physical models using finite difference schemes (FDS) are typically implemented using mutable data structures. The FDS library of the Faust programming language, where such data structures are not available, is instead based on a cellular automaton approach. This paper proposes a mechanism by which multiple one-dimensional FDS based on the Faust FDS library approach can be coupled together. The coupling is achieved by composing the various FDS algorithms in parallel and modifying the Faust FDS library routing to calculate the connection forces. The mechanism is demonstrated by coupling multiple stiff string models to a bridge, modeled as an ideal damped bar

Real-time Timbre Transfer and Sound Synthesis using DDSP

Neural audio synthesis is an actively researched topic, having yielded a wide range of techniques that leverages machine learning architectures. Google Magenta elaborated a novel approach called Differential Digital Signal Processing (DDSP) that incorporates deep neural networks with preconditioned digital signal processing techniques, reaching state-of-the-art results especially in timbre transfer applications. However, most of these techniques, including the DDSP, are generally not applicable in real-time constraints, making them ineligible in a musical workflow. In this paper, we present a real-time implementation of the DDSP library embedded in a virtual synthesizer as a plug-in that can be used in a Digital Audio Workstation. We focused on timbre transfer from learned representations of real instruments to arbitrary sound inputs as well as controlling these models by MIDI. Furthermore, we developed a GUI for intuitive high-level controls which can be used for post-processing and manipulating the parameters estimated by the neural network. We have conducted a user experience test with seven participants online. The results indicated that our users found the interface appealing, easy to understand, and worth exploring further. At the same time, we have identified issues in the timbre transfer quality, in some components we did not implement, and in installation and distribution of our plugin. The next iteration of our design will address these issues. Our real-time MATLAB and JUCE implementations are available at https://github.com/SMC704/juce-ddsp and https://github.com/SMC704/matlab-ddsp , respectively

Pruning Deep Neural Network Models of Guitar Distortion Effects

Publisher Copyright: AuthorDeep neural networks have been successfully used in the task of black-box modeling of analog audio effects such as distortion. Improving the processing speed and memory requirements of the inference step is desirable to allow such models to be used on a wide range of hardware and concurrently with other software. In this paper, we propose a new application of recent advancements in neural network pruning methods to recurrent black-box models of distortion effects using a Long Short-Term Memory architecture. We compare the efficacy of the method on four different datasets; one distortion pedal and three vacuum tube amplifiers. Iterative magnitude pruning allows us to remove over 99&#x0025; of parameters from some models without a loss of accuracy. We evaluate the real-time performance of the pruned models and find that a 3x-4x speedup can be achieved, compared to an unpruned baseline. We show that training a larger model and then pruning it outperforms an unpruned model of equivalent hidden size. A listening test confirms that pruning does not degrade the perceived sound quality, but may even slightly improve it. The proposed techniques can be used to design computationally efficient deep neural networks for processing the sound of the electric guitar in real time.Peer reviewe

A Hybrid Cloud Deployment Architecture For Privacy-preserving Collaborative Genome-Wide Association Studies

International audienc