Paper Archive

The aim of this work is to make an instrument that is timbrally consistent over pitch and loudness. This particular work is not attempting to reproduce an existing instrument’s timbre, but to produce a timbrally dynamic virtual instrument that can be designed by the user. In this paper there is a brief introduction to timbre and synthesis methods, followed by a proposal on how to make timbrally consistent virtual instruments out of given timbres.

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In this paper a novel algorithm for sound texture synthesis is presented. The goal of this algorithm is to produce new examples of a given sampled texture, the synthesized textures being of any desired duration. The algorithm is based on a montage approach to synthesis in that the synthesized texture is made up of pieces of the original sample concatenated together in a new sequence. This montage approach preserves both the high level evolution and low level detail of the original texture. Included in the algorithm is a measure of uniqueness, which can be used for the identification of regions in the original texture containing events that are atypical of the texture, and hence avoid their unnatural repetition at the synthesis stage.

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This paper investigates the synthesis of audio textures and the structure of generative latent spaces using Variational Autoencoders (VAEs) within two paradigms of neural audio synthesis: DSP-inspired and data-driven approaches. For each paradigm, we propose VAE-based frameworks that allow fine-grained temporal control. We introduce datasets across three categories of environmental sounds to support our investigations. We evaluate and compare the models’ reconstruction performance using objective metrics, and investigate their generative capabilities and latent space structure through latent space interpolations.

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