Prediction sinusoidal models techniques

The family of techniques known as sinusoidal models use this as their basic building block and performs speech modification by finding the sinusoidal components for a waveform and performing modification by altering the parameters of the above equation, namely the amplitudes, phases and frequencies. It has some advantages over models such as TD-PSOLA in is that it allows adjustments in the frequency domain. While frequency domain adjustments are possible in the linear prediction techniques, the sinusoidal techniques facilitate this with far fewer assumptions about the nature of the signal and in particular don t assume a source and all-pole filter model. 

In prior chapters we found that spectral shape is important to our perception of sounds, such as vowel/consonant distinctions, the different timbres of the vowels eee and ahh, etc. We also discovered that sinusoids are not the only way to look at modeling the spectra of sounds (or soimd components), and that sometimes just capturing the spectral shape is the most important thing in parametric sound modeling. Chapters 5 and 6 both centered on the notion of additive synthesis, where sinusoids and other components are added to form a final wave that exhibits the desired spectral properties. In this chapter we will develop and refine the notion of subtractive synthesis and discuss techniques and tools for calibrating the parameters of subtractive synthesis to real sounds. The main technique we will use is called Linear Predictive Coding (LPC), which will allow us to automatically fit a low-order resonant filter to the spectral shape of a sound. 

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