Download CONMOD: Controllable Neural Frame-Based Modulation Effects Deep learning models have seen widespread use in modelling LFOdriven audio effects, such as phaser and flanger. Although existing neural architectures exhibit high-quality emulation of individual effects, they do not possess the capability to manipulate the output via control parameters. To address this issue, we introduce Controllable Neural Frame-based Modulation Effects (CONMOD), a single black-box model which emulates various LFOdriven effects in a frame-wise manner, offering control over LFO frequency and feedback parameters. Additionally, the model is capable of learning the continuous embedding space of two distinct phaser effects, enabling us to steer between effects and achieve creative outputs. Our model outperforms previous work while possessing both controllability and universality, presenting opportunities to enhance creativity in modern LFO-driven audio effects. Additional demo of our model is available in the accompanying website.1
Download Synthesis of Sound Textures with Tonal Components Using Summary Statistics and All-Pole Residual Modeling The synthesis of sound textures, such as flowing water, crackling fire, an applauding crowd, is impeded by the lack of a quantitative definition. McDermott and Simoncelli proposed a perceptual source-filter model using summary statistics to create compelling synthesis results for non-tonal sound textures. However, the proposed method does not work well with tonal components. Comparing the residuals of tonal sound textures and non-tonal sound textures, we show the importance of residual modeling. We then propose a method using auto regressive modeling to reduce the amount of data needed for resynthesis and delineate a modified method for analyzing and synthesizing both tonal and non-tonal sound textures. Through user evaluation, we find that modeling the residuals increases the realism of tonal sound textures. The results suggest that the spectral content of the residuals has an important role in sound texture synthesis, filling the gap between filtered noise and sound textures as defined by McDermott and Simoncelli. Our proposed method opens possibilities of applying sound texture analysis to musical sounds such as rapidly bowed violins.