Although this subsection may seem a bit off-topic, there are several reasons for its inclusion. First, many of the applications that are mentioned in different chapters are related to spectral processing techniques; second, the CLAM framework was born when the research group was basically involved in research into spectral domain and that definitely biased and conditioned many of the design decisions; and last, much work of the author (see C) is directly related with spectral modeling and is not reflected anywhere else in this Thesis.
The most common approach for converting a time domain signal into its frequency domain representation is the Short-Time Fourier Transform (STFT). It is a general technique from which we can implement loss-less analysis/synthesis systems. Many sound transformation systems are based on direct implementations of the basic algorithm and several examples have been presented in chapter 8.
In this chapter, we will briefly mention the Sinusoidal Model and will concentrate, with a Matlab sample code, in the Sinusoidal plus Residual Model. Anyhow, the decision as to what spectral representation to use in a particular situation is not an easy one. The boundaries are not clear and there are always compromises to take into account, such as: (1) sound fidelity, (2) flexibility, (3) coding efficiency, and (4) computational requirements. Ideally, we want to maximize fidelity and flexibility while minimizing memory consumption and computational requirements. The best choice for maximum fidelity and minimum compute time is the STFT that, anyhow, yields a rather inflexible representation and inefficient coding scheme. Thus our interest in finding higher-level representations as the ones we present in this section.
Using the output of the STFT, the Sinusoidal model represents a step towards a more flexible representations while compromising both sound fidelity and computing time. It is based on modeling the time-varying spectral characteristics of a sound as sums of time-varying sinusoids. To obtain a sinusoidal representation from a sound, an analysis is performed in order to estimate the instantaneous amplitudes and phases of the sinusoids. This estimation is generally done by first computing the STFT of the sound, then detecting the spectral peaks (and measuring the magnitude, frequency and phase of each one), and finally organizing them as time-varying sinusoidal tracks.
It is a quite general technique that can be used in a wide range of sounds and offers a gain in flexibility compared with the direct STFT implementation.