Download Subjective Evaluation of Sound Quality and Control of Drum Synthesis with Stylewavegan In this paper we investigate into perceptual properties of StyleWaveGAN, a drum synthesis method proposed in a previous publication. For both, the sound quality as well as the control precision StyleWaveGAN has been shown to deliver state of the art performance for quantitative metrics (FAD and MSE of the control parameters). The present paper aims to provide insight into the perceptual relevance of these results. Accordingly, we performed a subjective evaluation of the sound quality as well as a subjective evaluation of the precision of the control using timbre descriptors from the AudioCommons toolbox. We evaluate the sound quality with mean opinion score and make measurements of psychophysical response to the variations of the control. By means of the perceptual tests, we demonstrate that StyleWaveGAN produces better sound quality than state-of-the-art model DrumGAN and that the mean control error is lower than the absolute threshold of perception at every point of measurement used in the experiment.
Download Differentiable Piano Model for Midi-to-Audio Performance Synthesis Recent neural-based synthesis models have achieved impressive results for musical instrument sound generation. In particular, the Differentiable Digital Signal Processing (DDSP) framework enables the usage of spectral modeling analysis and synthesis techniques in fully differentiable architectures. Yet currently, it has only been used for modeling monophonic instruments. Leveraging the interpretability and modularity of this framework, the present work introduces a polyphonic differentiable model for piano sound synthesis, conditioned on Musical Instrument Digital Interface (MIDI) inputs. The model architecture is motivated by high-level acoustic modeling knowledge of the instrument which, in tandem with the sound structure priors inherent to the DDSP components, makes for a lightweight, interpretable and realistic sounding piano model. The proposed model has been evaluated in a listening test, demonstrating improved sound quality compared to a benchmark neural-based piano model, with significantly less parameters and even with reduced training data. The same listening test indicates that physical-modeling-based models still achieve better quality, but the differentiability of our lightened approach encourages its usage in other musical tasks dealing with polyphonic audio and symbolic data.