Download Toward the Perfect Audio Morph? Singing Voice Synthesis and Processing
This paper reviews the popular methods and models used for the synthesis of the singing voice, discussing strengths and weaknesses of each technique. Then a brief review is given of research on cross-modal visual/auditory perception of the human voice. The paper concludes with comments related to the singing synthesis systems discussed, addressing multi-modal perception, audio morphing, and the categorical perception of sound.
Download A Framework for Sonification of Vicon Motion Capture Data
This paper describes experiments on sonifying data obtained using the VICON motion capture system. The main goal is to build the necessary infrastructure in order to be able to map motion parameters of the human body to sound. For sonification the following three software frameworks were used: Marsyas, traditionally used for music information retrieval with audio analysis and synthesis, CHUCK, an on-the-fly real-time synthesis language, and Synthesis Toolkit (STK), a toolkit for sound synthesis that includes many physical models of instruments and sounds. An interesting possibility is the use of motion capture data to control parameters of digital audio effects. In order to experiment with the system, different types of motion data were collected. These include traditional performance on musical instruments, acting out emotions as well as data from individuals having impairments in sensor motor coordination. Rhythmic motion (i.e. walking) although complex, can be highly periodic and maps quite naturally to sound. We hope that this work will eventually assist patients in identifying and correcting problems related to motor coordination through sound.
Download A New Paradigm for Sound Design
A sound scene can be defined as any “environmental” sound that has a consistent background texture, with one or more potentially recurring foreground events. We describe a data-driven framework for analyzing, transforming, and synthesizing high-quality sound scenes, with flexible control over the components of the synthesized sound. Given one or more sound scenes, we provide well-defined means to: (1) identify points of interest in the sound and extract them into reusable templates, (2) transform sound components independently of the background or other events, (3) continually re-synthesize the background texture in a perceptually convincing manner, and (4) controllably place event templates over the background, varying key parameters such as density, periodicity, relative loudness, and spatial positioning. Contributions include: techniques and paradigms for template selection and extraction, independent sound transformation and flexible re-synthesis; extensions to a wavelet-based background analysis/synthesis; and user interfaces to facilitate the various phases. Given this framework, it is possible to completely transform an existing sound scene, dynamically generate sound scenes of unlimited length, and construct new sound scenes by combining elements from different sound scenes. URL:
Download Finding Latent Sources in Recorded Music with a Shift-invariant HDP
We present the Shift-Invariant Hierarchical Dirichlet Process (SIHDP), a nonparametric Bayesian model for modeling multiple songs in terms of a shared vocabulary of latent sound sources. The SIHDP is an extension of the Hierarchical Dirichlet Process (HDP) that explicitly models the times at which each latent component appears in each song. This extension allows us to model how sound sources evolve over time, which is critical to the human ability to recognize and interpret sounds. To make inference on large datasets possible, we develop an exact distributed Gibbs sampling algorithm to do posterior inference. We evaluate the SIHDP’s ability to model audio using a dataset of real popular music, and measure its ability to accurately find patterns in music using a set of synthesized drum loops. Ultimately, our model produces a rich representation of a set of songs consisting of a set of short sound sources and when they appear in each song.
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