2022-04-12 22:02:59 -03:00
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original version by: Nikos Drakos, CBLU, University of Leeds
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* revised and updated by: Marcus Hennecke, Ross Moore, Herb Swan
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<TITLE>Control streams</TITLE>
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HREF="node40.html">Audio and control computations</A>
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HREF="node201.html">Index</A></B>
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<!--End of Navigation Panel-->
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<H1><A NAME="SECTION00730000000000000000"></A>
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<A NAME="sect3.controlstreams"></A>
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<BR>
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Control streams
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</H1>
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<P>
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Control computations may come from a variety of sources, both internal and
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external to the overall computation. Examples of internally engendered control
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computations include sequencing (in which control computations must take place
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at pre-determined times) or feature detection of the audio output (for
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instance, watching for zero crossings in a signal). Externally engendered ones
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may come from input devices such as MIDI controllers, the mouse and keyboard,
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network packets, and so on. In any case, control computations may occur at
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irregular intervals, unlike audio samples which correspond to a steadily
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ticking sample clock.
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<P>
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<DIV ALIGN="CENTER"><A NAME="fig03.03"></A><A NAME="3592"></A>
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<TABLE>
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<CAPTION ALIGN="BOTTOM"><STRONG>Figure 3.3:</STRONG>
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Graphical representation of a control stream as a sequence
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of points in time.</CAPTION>
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<TR><TD><IMG
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WIDTH="280" HEIGHT="51" BORDER="0"
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SRC="img314.png"
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ALT="\begin{figure}\psfig{file=figs/fig03.03.ps}\end{figure}"></TD></TR>
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</TABLE>
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</DIV>
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<P>
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We will need a way of describing how information
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flows between control and audio computations, which we will base on the
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notion of a
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<A NAME="3595"></A><I>control stream</I>.
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This is simply a collection of numbers--possibly empty--that appear as a result of control
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computations, whether
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regularly or irregularly spaced in logical time.
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The simplest possible control stream has no information other than a
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<A NAME="3597"></A>
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<I>time sequence</I>:
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<BR><P></P>
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<DIV ALIGN="CENTER">
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<!-- MATH
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\begin{displaymath}
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\ldots , t[0], t[1], t[2], \ldots
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\end{displaymath}
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-->
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<IMG
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WIDTH="133" HEIGHT="28" BORDER="0"
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SRC="img315.png"
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ALT="\begin{displaymath}
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\ldots , t[0], t[1], t[2], \ldots
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\end{displaymath}">
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</DIV>
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<BR CLEAR="ALL">
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<P></P>
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Although the time values are best given in units of samples,
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their values aren't quantized; they may be arbitrary real numbers.
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We do require them to be sorted in nondecreasing order:
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<BR><P></P>
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<DIV ALIGN="CENTER">
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<!-- MATH
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\begin{displaymath}
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\cdots \le t[0] \le t[1] \le t[2] \le \cdots
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\end{displaymath}
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-->
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<IMG
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WIDTH="186" HEIGHT="28" BORDER="0"
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SRC="img316.png"
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ALT="\begin{displaymath}
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\cdots \le t[0] \le t[1] \le t[2] \le \cdots
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\end{displaymath}">
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</DIV>
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<BR CLEAR="ALL">
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<P></P>
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Each item in the sequence
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is called an
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<A NAME="3599"></A><I>event</I>.
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<P>
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Control streams may be shown graphically as in Figure <A HREF="#fig03.03">3.3</A>. A number
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line shows time and a sequence of arrows points to the times associated
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with each event.
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The control
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stream shown has no data (it is a time sequence). If we want to show
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data in the control stream we will write it at the base of each arrow.
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<P>
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A
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<A NAME="3602"></A><I>numeric control stream</I>
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is one that contains one number per time point, so that it
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appears as a sequence of
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ordered pairs:
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<BR><P></P>
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<DIV ALIGN="CENTER">
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<!-- MATH
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\begin{displaymath}
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\ldots , \, (t[0], x[0]), \, (t[1], x[1]), \, \ldots
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\end{displaymath}
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-->
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<IMG
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WIDTH="202" HEIGHT="28" BORDER="0"
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SRC="img317.png"
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ALT="\begin{displaymath}
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\ldots , \, (t[0], x[0]), \, (t[1], x[1]), \, \ldots
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\end{displaymath}">
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</DIV>
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<BR CLEAR="ALL">
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<P></P>
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where the <IMG
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WIDTH="27" HEIGHT="32" ALIGN="MIDDLE" BORDER="0"
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SRC="img318.png"
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ALT="$t[n]$"> are the time points
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and the <IMG
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WIDTH="31" HEIGHT="32" ALIGN="MIDDLE" BORDER="0"
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SRC="img80.png"
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ALT="$x[n]$"> are the signal's values
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at those times.
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<P>
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2022-04-12 23:17:03 -03:00
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A numeric control stream is roughly analogous to a "MIDI controller", whose
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values change irregularly, for example when a physical control is moved by a
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performer. Other control stream sources may have higher possible rates of
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change and/or more precision. On the other hand, a time sequence might be a
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sequence of pedal hits, which (MIDI implementation notwithstanding) shouldn't be
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considered as having <I>values</I>, just <I>times</I>.
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<P>
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Numeric control streams are like audio signals in that
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both are just time-varying numeric values. But
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whereas the audio signal comes at a steady rate (and so the time values need
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not be specified per sample), the control stream comes
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unpredictably--perhaps evenly, perhaps unevenly, perhaps never.
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<P>
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Let us now look at what happens when we try to convert a numeric
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control stream to
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an audio signal. As before we'll choose a block size <IMG
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WIDTH="45" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
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SRC="img312.png"
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ALT="$B=4$">. We will consider
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as a control stream a square wave of period 5.5:
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<P>
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<BR><P></P>
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<DIV ALIGN="CENTER">
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<!-- MATH
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\begin{displaymath}
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(2, 1), (4.75, 0), (7.5, 1), (10.25, 0), (13, 1), \ldots
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\end{displaymath}
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-->
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<IMG
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WIDTH="296" HEIGHT="28" BORDER="0"
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SRC="img319.png"
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ALT="\begin{displaymath}
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(2, 1), (4.75, 0), (7.5, 1), (10.25, 0), (13, 1), \ldots
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\end{displaymath}">
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</DIV>
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<BR CLEAR="ALL">
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<P></P>
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and demonstrate three ways it could be converted to an audio signal. Figure
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<A HREF="#fig03.04">3.4</A> (part a) shows the simplest, fast-as-possible, conversion.
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Each audio sample of output simply reflects the most recent value of the
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control signal. So samples 0 through 3 (which are computed at logical time
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4 because of the block size) are 1 in value because of the point (2, 1). The
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next four samples are also one, because of the two points, (4.75, 0) and
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(7.5, 1), the most recent still has the value 1.
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<P>
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Fast-as-possible conversion is most appropriate for control streams which do
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not change frequently compared to the block size. Its main advantages are
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simplicity of computation and the fastest possible response to changes. As the
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figure shows, when the control stream's updates are too fast (on the order of
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the block size), the audio signal may not be a good likeness of the sporadic
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one. (If, as in this case, the control stream comes at regular intervals of
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time, we can use the sampling theorem to analyze the result. Here the Nyquist
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frequency associated with the block rate <IMG
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WIDTH="36" HEIGHT="32" ALIGN="MIDDLE" BORDER="0"
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SRC="img320.png"
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ALT="$R/B$"> is lower than the input square
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wave's frequency, and so the output is aliased to a new frequency lower than
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the Nyquist frequency.)
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<P>
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<DIV ALIGN="CENTER"><A NAME="fig03.04"></A><A NAME="3609"></A>
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<TABLE>
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<CAPTION ALIGN="BOTTOM"><STRONG>Figure 3.4:</STRONG>
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Three ways to change a control stream into an audio signal: (a)
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as fast as possible; (b) delayed to the nearest sample; (c) with
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two-point interpolation for higher delay accuracy.</CAPTION>
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<TR><TD><IMG
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WIDTH="574" HEIGHT="534" BORDER="0"
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SRC="img321.png"
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ALT="\begin{figure}\psfig{file=figs/fig03.04.ps}\end{figure}"></TD></TR>
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</TABLE>
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</DIV>
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<P>
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Part (b) shows the result of nearest-sample conversion. Each new value of the
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control stream at a time <IMG
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WIDTH="9" HEIGHT="13" ALIGN="BOTTOM" BORDER="0"
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SRC="img82.png"
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ALT="$t$"> affects output samples starting from index
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<!-- MATH
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$\lfloor t \rfloor$
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-->
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<IMG
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WIDTH="23" HEIGHT="32" ALIGN="MIDDLE" BORDER="0"
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SRC="img322.png"
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ALT="$\lfloor t \rfloor$"> (the greatest integer not exceeding <IMG
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WIDTH="9" HEIGHT="13" ALIGN="BOTTOM" BORDER="0"
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SRC="img82.png"
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ALT="$t$">).
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This is equivalent to using fast-as-possible conversion at a block size of 1;
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in other words, nearest-sample conversion hides the effect of the larger block
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size. This is better than fast-as-possible conversion in cases where the
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control stream might change quickly.
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<P>
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Part (c) shows sporadic-to-audio conversion, again at the nearest sample,
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but now also using two-point interpolation to further increase the time
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accuracy. Conceptually we can describe this as follows. Suppose the value
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of the control stream was last equal to <IMG
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WIDTH="12" HEIGHT="13" ALIGN="BOTTOM" BORDER="0"
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SRC="img243.png"
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ALT="$x$">, and that the next point is
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<IMG
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WIDTH="68" HEIGHT="32" ALIGN="MIDDLE" BORDER="0"
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SRC="img323.png"
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ALT="$(n+f, y)$">, where <IMG
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WIDTH="13" HEIGHT="13" ALIGN="BOTTOM" BORDER="0"
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SRC="img75.png"
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ALT="$n$"> is an integer and <IMG
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WIDTH="13" HEIGHT="30" ALIGN="MIDDLE" BORDER="0"
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SRC="img112.png"
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ALT="$f$"> is the fractional part of the
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time value (so <IMG
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WIDTH="71" HEIGHT="30" ALIGN="MIDDLE" BORDER="0"
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SRC="img324.png"
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ALT="$0 \le f < 1$">). The first point affected in the audio
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output will be the sample at index <IMG
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WIDTH="13" HEIGHT="13" ALIGN="BOTTOM" BORDER="0"
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SRC="img75.png"
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ALT="$n$">. But instead of setting the output
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to <IMG
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WIDTH="11" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
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SRC="img106.png"
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ALT="$y$"> as before, we set it to
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<BR><P></P>
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<DIV ALIGN="CENTER">
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<!-- MATH
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\begin{displaymath}
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fx + (1-f)y,
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\end{displaymath}
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-->
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<IMG
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WIDTH="99" HEIGHT="28" BORDER="0"
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SRC="img325.png"
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ALT="\begin{displaymath}
|
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fx + (1-f)y,
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\end{displaymath}">
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</DIV>
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<BR CLEAR="ALL">
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<P></P>
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in other words, to a weighted average of the previous and the new value, whose
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weights favor the new value more if the time of the sporadic value is earlier,
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closer to <IMG
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WIDTH="13" HEIGHT="13" ALIGN="BOTTOM" BORDER="0"
|
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SRC="img75.png"
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ALT="$n$">. In the example shown, the transition from 0 to 1 at time 2
|
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gives
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<BR><P></P>
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<DIV ALIGN="CENTER">
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<!-- MATH
|
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|
\begin{displaymath}
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0 \cdot x + 1 \cdot y = 1,
|
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\end{displaymath}
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-->
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<IMG
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WIDTH="108" HEIGHT="27" BORDER="0"
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SRC="img326.png"
|
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ALT="\begin{displaymath}
|
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|
0 \cdot x + 1 \cdot y = 1,
|
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|
\end{displaymath}">
|
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|
</DIV>
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<BR CLEAR="ALL">
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|
<P></P>
|
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|
|
while the transition from 1 to 0 at time 4.75 gives:
|
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|
<BR><P></P>
|
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<DIV ALIGN="CENTER">
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|
<!-- MATH
|
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|
|
\begin{displaymath}
|
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|
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|
0.75 \cdot x + 0.25 \cdot y = 0.75.
|
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|
|
\end{displaymath}
|
|
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|
|
-->
|
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|
<IMG
|
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|
WIDTH="168" HEIGHT="27" BORDER="0"
|
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|
SRC="img327.png"
|
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|
|
ALT="\begin{displaymath}
|
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|
|
|
0.75 \cdot x + 0.25 \cdot y = 0.75.
|
|
|
|
|
\end{displaymath}">
|
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|
|
</DIV>
|
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|
|
<BR CLEAR="ALL">
|
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|
<P></P>
|
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|
|
This technique gives a still closer representation of the control signal
|
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|
|
(at least, the portion of it that lies below the Nyquist frequency), at
|
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|
the expense of more computation and slightly greater delay.
|
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<P>
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|
Numeric control streams may also be converted to audio signals using ramp
|
|
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|
|
functions to smooth discontinuities. This is often used when a control stream
|
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|
|
is used to control an amplitude, as described in Section <A HREF="node12.html#sect1.synth">1.5</A>. In
|
|
|
|
|
general there are three values to specify to set a ramp function in motion: a
|
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|
|
start time and target value (specified by the control stream) and a target
|
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|
|
time, often expressed as a delay after the start time.
|
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|
<P>
|
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|
In such situations it is almost always accurate enough to adjust the start and
|
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|
|
ending times to match the first audio sample computed at a later logical time,
|
|
|
|
|
a choice which corresponds to the fast-as-possible scenario above. Figure
|
|
|
|
|
<A HREF="#fig03.05">3.5</A> (part a) shows the effect of ramping from 0, starting at
|
|
|
|
|
time 3, to a value of 1 at time 9, immediately starting back toward 0 at
|
|
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|
|
time 15, with block size <IMG
|
|
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|
|
WIDTH="45" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
|
|
|
|
|
SRC="img312.png"
|
|
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|
|
ALT="$B=4$">. The times 3, 9, and 15 are truncated to
|
|
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|
|
0, 8, and 12, respectively.
|
|
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|
|
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|
|
<P>
|
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|
<DIV ALIGN="CENTER"><A NAME="fig03.05"></A><A NAME="3616"></A>
|
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|
|
<TABLE>
|
|
|
|
|
<CAPTION ALIGN="BOTTOM"><STRONG>Figure 3.5:</STRONG>
|
|
|
|
|
Line segment smoothing of numeric control streams: (a) aligned to block
|
|
|
|
|
boundaries; (b) aligned to nearest sample.</CAPTION>
|
|
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|
|
<TR><TD><IMG
|
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|
|
WIDTH="342" HEIGHT="196" BORDER="0"
|
|
|
|
|
SRC="img328.png"
|
|
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|
|
ALT="\begin{figure}\psfig{file=figs/fig03.05.ps}\end{figure}"></TD></TR>
|
|
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|
|
</TABLE>
|
|
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|
|
</DIV>
|
|
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|
<P>
|
|
|
|
|
In real situations the block size might be on the order of a millisecond, and
|
|
|
|
|
adjusting ramp endpoints to block boundaries works fine for controlling
|
|
|
|
|
amplitudes; reaching a target a fraction of a millisecond early or late rarely
|
|
|
|
|
makes an audible difference. However, other uses of ramps are more sensitive
|
|
|
|
|
to time quantization of endpoints. For example, if we wish to do something
|
|
|
|
|
repetitively every few milliseconds, the variation in segment lengths will make
|
|
|
|
|
for an audible aperiodicity.
|
|
|
|
|
|
|
|
|
|
<P>
|
|
|
|
|
For situations such as these, we can improve the ramp generation algorithm to
|
|
|
|
|
start and stop at arbitrary samples, as shown in Figure <A HREF="#fig03.05">3.5</A> (part b),
|
|
|
|
|
for example. Here the endpoints of the line segments line up exactly with
|
|
|
|
|
the requested samples 3, 9, and 15. We can go even further and adjust for
|
|
|
|
|
fractional samples, making the line segments touch the values 0 and 1 at
|
|
|
|
|
exactly specifiable points on a number line.
|
|
|
|
|
|
|
|
|
|
<P>
|
|
|
|
|
For example, suppose we want to repeat a recorded sound out of a wavetable 100
|
|
|
|
|
times per second, every 441 samples at the usual sample rate. Rounding errors
|
|
|
|
|
due to blocking at 64-sample boundaries could detune the playback by as
|
|
|
|
|
much as a whole tone in pitch; and even rounding to one-sample boundaries
|
|
|
|
|
could introduce variations up to about 0.2%, or three cents. This
|
|
|
|
|
situation would call for sub-sample accuracy in sporadic-to-audio conversion.
|
|
|
|
|
|
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|
|
<P>
|
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|
|
<HR>
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