🚀 go-pugleaf

cmh@eng.cam.ac.uk (C.M. Hicks) writes:
> - Signal Modelling approach. Model the signal (eg linear prediction) and
>   adjust the model parameters so that when the signal is reconstructed, it
>   is at the changed pitch.
>   Main problems: model choice, block boundaries, computational load.

Xavier Serra did some really excellent work on this approach when he was
at Stanford.  His thesis talk included some great examples of just the
pitchy (deterministic) and noisy components of a musical signal.  He also
played some examples that were pitch shifted.  They sounded perfect to
my ears.

Here's the complete reference from INSPEC.

							Malcolm Slaney
							Apple Perception Group
							and Stanford CCRMA

TITLE:         Spectral modeling synthesis: a sound analysis/synthesis system
                 based on a deterministic plus stochastic decomposition.
AUTHOR:        Serra, X. (Dept. of Music, Stanford Univ., CA, USA); Smith, J.,
                 III
PUBLICATION:   Computer Music Journal (Winter 1990) vol.14, no.4, p. 12-24. 21
                 refs.
DOCUMENT TYPE: Journal article
LANGUAGE:      English
ABSTRACT:  When generating musical sound on a digital computer, it is important
   to have a good model whose parameters provide a rich source of meaningful
   sound transformations. This paper addresses the synthesis technique of
   spectrum modeling. It describes a technique called spectral modeling
   synthesis (SMS) that models time-varying spectra as (1) a collection of
   sinusoids controlled through time by piecewise linear amplitude and
   frequency envelopes (the deterministic part), and (2) a time-varying
   filtered noise component (the stochastic part). The analysis procedure first
   extracts the sinusoidal trajectories by tracking peaks in a sequence of
   short-time Fourier transforms. These peaks are then removed by spectral
   subtraction. The remaining 'noise floor' is then modeled as white noise
   through a time-varying filter. A piecewise linear approximation to the upper
   spectral envelope of the noise is computed for each successive spectrum, and
   the stochastic part is synthesized by means of the overlap-add technique.
   The SMS technique has proved to give general, high quality transformations
   for a wide variety of musical signals.