Ryan Stables

Fundamental frequency modulation in singing voice synthesis

A model is presented for the analysis and synthesis of low frequency human-like pitch deviation, as a replacement for existing modulation techniques in singing voice synthesis systems. Fundamental frequency (f0) measurements are taken from vocalists producing a selected range of utterances without vibrato and trends in the data are observed. A probabilistic function that provides natural sounding low frequency f0 modulation to synthesized singing voices is presented and the perceptual relevance is evaluated with subjective listening tests.

Semantic Description of Timbral Transformations in Music Production

In music production, descriptive terminology is used to define perceived sound transformations. By understanding the underlying statistical features associated with these descriptions, we can aid the retrieval of contextually relevant processing parameters using natural language, and create intelligent systems capable of assisting in audio engineering. In this study, we present an analysis of a dataset containing descriptive terms gathered using a series of processing modules, embedded within a Digital Audio Workstation. By applying hierarchical clustering to the audio feature space, we show that similarity in term representations exists within and between transformation classes. Furthermore, the organisation of terms in low-dimensional timbre space can be explained using perceptual concepts such as size and dissonance. We conclude by performing Latent Semantic Indexing to show that similar groupings exist based on term frequency.