Computing Vocoder Parameters

To compute the amplitude at the output of the th subband, we can apply an envelope follower. Classically, such as in the original vocoder, this can be done by full-wave rectification and subsequent low pass filtering, as shown in Fig.G.10. This produces an approximation of the average power in each subband.

In digital signal processing, we can do much better than the classical amplitude-envelope follower: We can measure instead the instantaneous amplitude of the (assumed quasi sinusoidal) signal in each filter band using so-called analytic signal processing (introduced in §4.6). For this, we generalize (G.3) to the real-part of the corresponding analytic signal:

 (G.5)

In general, when both amplitude and phase are needed, we must compute two real signals for each vocoder channel:
 (instantaneous amplitude) (instantaneous phase) (G.6)

We call the instantaneous amplitude at time for both and . The function as a whole is called the amplitude envelope of the th channel output. The instantaneous phase at time is , and its time-derivative is instantaneous frequency.

In order to determine these signals, we need to compute the analytic signal from its real part . Ideally, the imaginary part of the analytic signal is obtained from its real part using the Hilbert transform4.6), as shown in Fig.G.11.

Using the Hilbert-transform filter, we obtain the analytic signal in rectangular'' (Cartesian) form:

 (G.7)

To obtain the instantaneous amplitude and phase, we simply convert each complex value of to polar form

 (G.8)

as given by (G.6).

Frequency Envelopes

It is convenient in practice to work with instantaneous frequency deviation instead of phase:

 (G.9)

Since the th channel of an -channel uniform filter-bank has nominal bandwidth given by , the frequency deviation usually does not exceed .

Note that is a narrow-band signal centered about the channel frequency . As detailed in Chapter 9, it is typical to heterodyne the channel signals to base band'' by shifting the input spectrum by so that the channel bandwidth is centered about frequency zero (dc). This may be expressed by modulating the analytic signal by to get

 (G.10)

The b' superscript here stands for baseband,'' i.e., the channel-filter frequency-response is centered about dc. Working at baseband, we may compute the frequency deviation as simply the time-derivative of the instantaneous phase of the analytic signal:

 (G.11)

where

 (G.12)

denotes the time derivative of . For notational simplicity, let and . Then we have

 (G.13)

For discrete time, we replace by to obtain [186]

 (G.14)

Initially, the sliding FFT was used (hop size in the notation of Chapters 8 and 9). Larger hop sizes can result in phase ambiguities, i.e., it can be ambiguous exactly how many cycles of a quasi-sinusoidal component occurred during the hop within a given channel, especially for high-frequency channels. In many applications, this is not a serious problem, as it is only necessary to recreate a psychoacoustically equivalent peak trajectory in the short-time spectrum. For related discussion, see [299].

Using (G.6) and (G.14) to compute the instantaneous amplitude and frequency for each subband, we obtain data such as shown qualitatively in Fig.G.12. A matlab algorithm for phase unwrapping is given in §F.4.1.

Envelope Compression

Once we have our data in the form of amplitude and frequency envelopes for each filter-bank channel, we can compress them by a large factor. If there are channels, we nominally expect to be able to downsample by a factor of , as discussed initially in Chapter 9 and more extensively in Chapter 11.

In early computer music [97,186], amplitude and frequency envelopes were downsampled'' by means of piecewise linear approximation. That is, a set of breakpoints were defined in time between which linear segments were used. These breakpoints correspond to knot points'' in the context of polynomial spline interpolation [286]. Piecewise linear approximation yielded large compression ratios for relatively steady tonal signals.G.10For example, compression ratios of 100:1 were not uncommon for isolated toots'' on tonal orchestral instruments [97].

A more straightforward method is to simply downsample each envelope by some factor. Since each subband is bandlimited to the channel bandwidth, we expect a downsampling factor on the order of the number of channels in the filter bank. Using a hop size in the STFT results in downsampling by the factor (as discussed in §9.8). If channels are downsampled by , then the total number of samples coming out of the filter bank equals the number of samples going into the filter bank. This may be called critical downsampling, which is invariably used in filter banks for audio compression, as discussed further in Chapter 11. A benefit of converting a signal to critically sampled filter-bank form is that bits can be allocated based on the amount of energy in each subband relative to the psychoacoustic masking threshold in that band. Bit-allocation is typically different for tonal and noise signals in a band [113,25,16].

Using the phase-vocoder to compute amplitude and frequency envelopes for additive synthesis works best for quasi-periodic signals. For inharmonic signals, the vocoder analysis method can be unwieldy: The restriction of one sinusoid per subband leads to many empty'' bands (since radix-2 FFT filter banks are always uniformly spaced). As a result, we have to compute many more filter bands than are actually needed, and the empty bands need to be `pruned'' in some way (e.g., based on an energy detector within each band). The unwieldiness of a uniform filter bank for tracking inharmonic partial overtones through time led to the development of sinusoidal modeling based on the STFT, as described in §G.11.2 below.