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Spectral Granular Synthesis
TLDR
This article introduces a granular synthesis algorithm in which grains of sound are processed in the frequency domain and combined at spectral level. Expand
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A Voice Interface for Sound Generators: adaptive and automatic mapping of gestures to sound
TLDR
We propose the use of vocal gesture to control time-continuous and real-valued sound synthesis parameters, an automatically generated many-to-many mapping, and the adaptation to the relationship between synthesis parameter and perceptual sound features. Expand
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A Self-Organizing Gesture Map for a Voice-Controlled Instrument Interface
TLDR
We propose an unsupervised offline method that learns how to reduce and map the gestural data to a generic instrument parameter control space, while dimensionality reduction is handled separately. Expand
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Identifying Optimal Features for Multi-channel Acoustic Scene Classification
TLDR
We consider domestic multi-channel audio classification through the use of a Deep Convolutional Neural Network (DCNN) model. Expand
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Scalogram Neural Network Activations with Machine Learning for Domestic Multi-channel Audio Classification
TLDR
We proposed the idea of combining neural network deep learning techniques with machine learning in the form of the multi-class linear SVM for multi-channel audio classification. Expand
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Adapting General Purpose Interfaces to synthesis Engines using Unsupervised Dimensionality Reduction Techniques and inverse Mapping from Features to parameters
TLDR
In this paper we develop adaptive techniques for mapping generic user interfaces to synthesis engines, providing control directly over the perceptual features with greatest variance. Expand
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VOICE FEATURES FOR CONTROL: A VOCALIST DEPENDENT METHOD FOR NOISE MEASUREMENT AND INDEPENDENT SIGNALS COMPUTATION
Information about the human spoken and singing voice is conveyed through the articulations of the individual’s vocal folds and vocal tract. The signal receiver, either human or machine, works atExpand
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TSAM: a tool for analyzing, modeling, and mapping the timbre of sound synthesizers
TLDR
Synthesis algorithms often have a large number of adjustable parameters that determine the generated sound and its resultant psychoacoustic features. Expand
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Vocal Control of Sound Synthesis Personalized by Unsupervised Machine Listening and Learning
TLDR
We describe a user-driven adaptive method to control the sonic response of digital musical instruments using information extracted from the timbre of the human voice. Expand
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