Description
1. Purpose
The purpose of this lab is to: i) implement simple sound synthesis methods, and
ii) develop intuitions for the audio impact of different signal transformations.
2. Background
Sound effects, music, speech and many audio signals can be synthesized by
computer in a variety of ways. One approach is to generate a simple sound (e.g.
a tone or random noise) and then apply various types of transformations to it,
such as time scaling, amplitude scaling or filtering. Another approach is to start
with natural sounds and modify these. In either case, more complex sounds can
be generated by summing up multiple simple sounds, and these can be
concatenated to generate sequences of sounds. The lab will use tones and other
sounds with amplitude and time modification.
3. Basic Sound Synthesis
Signals can be periodic or aperiodic. In EE235, you learned that periodic signals
can be expressed with a Fourier series representation, and real-valued periodic
signals can be expressed as a sum of sinusoids. Since the human ear can’t hear
frequencies much above 20kHz, we can synthesize periodic sounds with a finite
number of sinusoids. A musical note is one example of a periodic signal, which
we will discuss further in Section 3.1. Aperiodic sounds contain a continuum of
frequencies. To synthesize aperiodic sounds in the range of human hearing, we
often start with a noise signal and shape it in either the time or frequency domain,
as described in Section 3.2. Another approach to synthesizing sounds is modify,
mix and concatenate natural sounds (Section 3.3).
3.1 Synthesizing musical notes1
Each musical note can be simply represented by a sinusoid whose frequency
depends on the note pitch. There are seven natural notes: A, B, C, D, E, F and
G. After G, we begin again with A. Music is written on a “staff” consisting of five
lines with four spaces between the lines. The notes on the staff are written in
alphabetical order, the first line is E as shown in Figure 1. Notes can extend
above and below the staff. When they do, ledger lines are added.
1 The material related to music synthesis is based on a lab by Professor Virginia Stonick of
Oregon State University.
A B C D E F G A
Figure 1. Natural notes.
Musical notes are arranged in groups of twelve notes called octaves. The twelve
notes in each octave are logarithmically spaced in frequency, with each note
being of a frequency 1/12 2 times the frequency of the note of lower frequency.
Thus, a 1-octave pitch shift corresponds to a doubling of the frequencies of the
notes in the original octave. Table 1 shows the ordering of notes in the octave
from 220Hz to 440Hz, as well as the fundamental frequencies for these notes.
Table 1. Notes in the 220 – 440 Hz octave
Note Frequency
A 220
A#
, Bb 220´21/12
B 220´22/12
C 220´23/12
C#
, Db 220´24/12
D 220´25/12
D#
, Eb 220´26/12
E 220´27/12
F 220´28/12
F#
, Gb 220´29/12
G 220´210/12
G#
, Ab 220´211/12
A 440
The duration of each note burst is determined by whether the note is a whole
note, half note, quarter note, etc. (see Figure 2). Obviously, a half note has half
the duration of a full note. For this lab, use 0.5 seconds for a whole note. A
musical score is essentially a plot of notes on the vertical scale (specifying
frequencies), with different forms to indicate note duration, and using the
horizontal scale to indicate time ordering. When you have multiple notes lining up
vertically, that represents a chord, which is synthesized simply by adding the
signals associated with the individual notes.
whole note. it lasts 4 counts
half note. it lasts 2 counts
fourth note. it lasts 1 count
Figure 2. Types of notes.
In the simplest case, each note may be represented by a burst of samples of a
sinusoid followed by a shorter period of silence (a sequence of zeros, which are
a pause). The pauses allow us to distinguish between separate notes of the
same pitch. The short pause you use to follow each note should be of the same
duration regardless of the length of the note. Longer periods of silence that are
part of the musical score are indicated by one of more rest symbols. (For more
information on music symbols, see
https://en.wikipedia.org/wiki/List_of_musical_symbols
3.2 Synthesizing aperiodic sounds
Sounds can be aperiodic simply by being finite in duration, but all computerbased signals are finite-duration. What matters for human perception is whether
there is a repeating pattern within a sufficiently long time window – even a few
seconds is long enough for something to sound periodic. Very short duration
sounds, such as clicks and pops, can be synthesized with pulses of various
shapes. A longer duration aperiodic sound can be synthesized by first
generating a noise signal, optionally filtering it to adjust the frequency content,
and then modifying it with amplitude operations. You can generate the basic
noise signal using the randn command in MATLAB. We will use amplitude
operations in this lab; you will learn more about filtering later in the course.
3.3 Modifying natural sounds
More natural sounds are often created by starting with actual recorded sounds.
For example, a clarinet synthesizer might be based on a few recorded clarinet
notes, which are modified through time scaling, amplitude operations, and
filtering to create additional notes or even different instruments. Similarly, you
could record the pop of a balloon and modify it to produce other sound effects. In
Matlab, you can concatenate signals to combine them sequentially (with zero
padding to get delays) or add things that you want to mix together.
4. Amplitude Operations
Amplitude scaling is a general concept that is used to improve the perceived
naturalness of a synthesized note, but is useful for sound effects as well. The
idea is simple: multiply one signal x[n] by another m[n] to get the modified signal
y[n]=x[n]m[n]. In Matlab, where signals are vectors, you can do an element by
element multiply using y=x.*m.
One example where this is useful is to approximate the gradual increase and
decay in energy of a note produced by a musical instrument. Typically, when a
note is played, the volume rises quickly from zero and then decays over time,
depending on how hard the key is struck and how long it is depressed. The
variation of the volume over time is divided into four segments: Attack, Decay,
Sustain, and Release (ADSR). For a given note, volume changes can be
achieved by multiplying a sinusoid by another function called a windowing
function. An example of such function is shown in Figure 3.
A D S R
volume
t
Figure 3. An ADSR Envelope.
Amplitude scaling can involve any function m[n]. An important example is
amplitude modulation, where m[n]=cos(2pf0n). In addition to its use in AM radio,
multiplying by a cosine is useful to create sound effects, as you will see here.
Another important amplitude operation is addition of signals. You can add
multiple harmonics to create a more natural sounding note, add multiple notes at
the same time to create a chord, or you can add sounds with a small overlap to
create other effects. For example, an improvement in perceived quality can be
achieved by overlapping some notes. As the volume of one note is decaying,
another note is played. Mathematically, this can be accomplished by allowing the
time regions occupied by subsequent sinusoids to overlap, hence removing the
pause. When combined with ADSR, this will yield a much smoother, less
staccato-sounding piece. You can combine many types of signals with addition,
including gradually decaying background music at the start of a news
announcement or the sum of several voices to create the sound of a party.
5. Time Scaling
A challenge with time scaling for discrete-time signals (compared to continuoustime signals) is that time is defined over the integers. For discrete-time signals,
time scaling involves throwing out or adding time samples, for speeding up or
slowing down a signal, respectively. (Recall that for periodic sounds, speeding
up gives you a higher pitch and slowing down gives a lower pitch, in addition to
being different in length.) Time scaling of the form y[n]=x[kn] where k is an
integer (speeding up) is simple: throw out samples. If y[n]=x[n/k] where k is an
integer (slowing down), you can insert k-1 samples between each original sample
and interpolate to get values for the added samples (e.g. using the Matlab
interp1 function).
For rational scaling factors, you can use a combination of up and down sampling.
In other words, if y[n]=x[3n/2], you would first slow down by a factor of 2 (referred
to as interpolation), and then speed up be a factor of 3 (referred to as
decimation). Alternatively, Matlab has a command that will do this for you:
resample. For example, resample(x,2,3) would give you the time-scaled signal
for a=3/2.
6. Group Assignment (for in-lab demo)
1. Synthesize an 8-note scale using simple tone bursts using four full notes
and four quarter notes (scale in Figure 1 with different note lengths). Use
an 8k sampling rate.
2. Improve the quality of the sound with a volume window function (amplitude
multiplication). Try concatenating different functions to model ADSR and
experiment with allowing a slight overlap in time. Demonstrate for your TA
the difference between this version and the unmodified tones in part 1.
3. It is said that when you put a seashell to your ear, you can hear the sound
of the ocean. You can create this effect by generating random noise using
the randn command in Matlab and multiplying the signal by a low
frequency cosine (or shifted cosine, e.g. 1+cos(2pfn)). Multiply the
resulting signal with an exponential decay function to get a sound that
fades out. Create such a signal and play it for your TA. You may need to
try different cosine frequencies to get an effect that is clearly audible.
4. Download “cat.wav” and “tiger.wav” from the course web page. Use timescale modification on the tiger to see if you get the effect of a cat or vice
versa. Play the resulting signal for the TA.
7. Individual Assignment
1. Synthesize a short segment (a few seconds) of a song you like using tone
bursts with your ADSR function and an 8k sampling rate. Look for “musical
scores for beginners” to get simple single note scores. (Each person
should have a different song.) Upload the file with your report.
2. Download or record an audio file with either music or someone talking and
modify it using the same function that you used for the cat/tiger signal.
(Each person should have a different signal.) Upload the original and the
modified file with your report.
Summary of Assignments:
• In-lab group demonstrations (due during the third week of class)
o Audio files generated in parts 1-4.
• Individual files to be turned in via Canvas (due the day before you lab in
week 3)
o wav files generated for the individual assignment
o M-files that were used to create these wav files
o Brief report (in pdf format) that describes what you implemented in
each step, specifics:
§ For (G1): an equation for the discrete-time cosine that
corresponds to continuous frequency fc given sampling
frequency fs.
§ For (G2): a plot of the ASDR window function that you
designed, an explanation of how it varies with the length of
the note (full vs. half vs. quarter, etc.), and the amount of
note overlap you used.
§ For (G3): the cosine frequency and decay factor that you
used and your observations about the perceptual effect of
varying those parameters.
§ For (G4): the time scaling factor(s) you used, and discussion
of the perceptual effect of time scaling the signal that you
uploaded.
§ For (I1): an image of the score of your song snippet
§ For (I2): a short description of the original sound, the source
of the sound, and what time scaling factor you used.
