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Spectral Granular Synthesis
TLDR
A granular synthesis algorithm in which grains of sound are processed in the frequency domain and combined at spectral level, in which each grain contributes to the sound synthesis with its magnitude spectrum only.
A Voice Interface for Sound Generators: adaptive and automatic mapping of gestures to sound
TLDR
This paper proposes the exploitation of vocal gesture as an extension or alternative to traditional physical controllers, which uses dynamic aspects of vocal sound to control variations in the timbre of the synthesized sound.
Identifying Optimal Features for Multi-channel Acoustic Scene Classification
TLDR
Experimental results show that when using the sum of the scalogram features of the four channels, the resulting average classification accuracy for a range of domestic audio scenes is close to 98%, outperforming the commonly used cepstral and spectral-based methods.
A Self-Organizing Gesture Map for a Voice-Controlled Instrument Interface
TLDR
An unsupervised offline method that learns how to reduce and map the gestural data to a generic instrument parameter control space and applies this method to the existing Voice-Controlled Interface for musical instruments, obtaining sensible usability improvements.
Scalogram Neural Network Activations with Machine Learning for Domestic Multi-channel Audio Classification
TLDR
This paper looks at domestic multi-channel audio classification through a comparison of various combinations of existing pre-trained Neural Network (NN) models, with Support Vector Machine (SVM) for classification.
TSAM: a tool for analyzing, modeling, and mapping the timbre of sound synthesizers
TLDR
An extensive set of sound descriptors are used which are ranked using a novel metric based on statistical analysis which enables the study of how changes to a synthesis parameter affect timbral descriptors, and provides a multidimensional model for the mapping of the synthesis control through specific timbre spaces.
Adapting General Purpose Interfaces to synthesis Engines using Unsupervised Dimensionality Reduction Techniques and inverse Mapping from Features to parameters
TLDR
Adapt techniques for mapping generic user interfaces to synthesis engines guarantee a linear relationship between control signals and perceptual features, and at the same time, reduces the control space dimensionality maintaining the maximum explorability of the sonic space.
PYNQ- Torch: a framework to develop PyTorch accelerators on the PYNQ platform
TLDR
The framework proposed in this paper enables fast prototyping of custom hardware accelerators for deep learning for PyTorch applications written in Python and running on PYNQ compatible platforms, which are based on Xilinx Zynq Systems on Chips.
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