Making waves


For one of my personal projects, I tried create a new way to design sounds: by representing them in a programming language, and manipulating them directly in an in-browser programming environment called Tsunami. In this report, I will discuss some of the engineering decisions that I had to make while developing Tsunami; most notably, the representation of the signals themselves. I will also demonstrate the effectiveness of Tsunami by showing how some common signal processing algorithms might be implemented in this framework.



A signal is a function over time. This is the signal that microphones try to capture and speakers try to recreate; the signal that encodings like MP3, AAC, and Vorbis try to represent in a compressed format. Physically, this signal might represent the displacement of a microphone diaphragm, a speaker cone, or an eardrum. Continuous-time signals are like mathematical functions in that they are defined for every value of time and can be infinitely detailed, while discrete-time signals are series of values measured at a particular sampling rate. While signals are continuous in reality (ignoring quantum mechanics), one must usually convert them to discrete signals in order to manipulate them on a computer.


Closures, or lexically scoped first-class functions, first appeared in Scheme in the 1970s and, as with all good language features, have been ignored in mainstream programming until a few years ago.1 First-class means that the function can be passed around, stored in a variable, and returned like any other value; lexically scoped means that the function can capture values from the environment in which it was defined even if it is passed outside that environment. These two properties together are very powerful; for example, they make it possible to define a function like quadratic(a, b, c) that itself returns a function f(x) that calculates the quadratic $( ax^2 + bx + c )$.

The vast majority of languages that support closures are garbage collected.2 This is understandable, since lexically scoped closures make it difficult to tell when memory can be freed. One can no longer simply pop off a stack frame when a function returns, since that stack frame might contain variables that are referenced by a closure that the function returns.


Most of the code examples in this article are written in TypeScript, Microsoft's layer of gradual typing on top of JavaScript. TypeScript's type definitions and annotations allow us to precisely specify the data structures we are using. While JavaScript has its warts, it also has good parts: it supports closures, its syntax has been getting better with each new version, and many smart people have worked very hard to make it run really fast right in your browser.

Figure: JavaScript's good parts.

Design goals

Our goal is to find a way to express these signals, as well as transformations on these signals, naturally in a programming environment.

An ideal means of representing signals, for the purposes of Tsuanami, should satisfy the following properties.

  • It should be flexible. One should be able to apply a wide variety of signal processing techniques using a single interface.
  • It should be performant. It should be possible to play sounds in real time without waiting for them to render.
  • It should be easy to manipulate in a language that runs in your web browser. This goal is more vain than technical; applications that run directly in the browser have a low barrier to entry, so making Tsunami a web app increases the chance that it becomes popular some day.

MP3 is an example of something that doesn't satisfy these properties. It is opaque, and must be decoded to get access to any meaningful information in the signal. It is inflexible; there are no obvious transformations you can do to an MP3 file other than decoding it.

Representing signals

Continuous signals

As you may have figured out, one way to represent a signal is to directly use a first-class function.

type Signal = (t: number) => number;

That is, a Signal is a function that takes a number and returns a number.

This is a very elegant way to treat signals. But this representation doesn't capture all the information that we might want. Most notably, it's impossible to tell how long a signal lasts — we must treat all signals as if they have infinite duration! This is inefficient, so let's put the function and its duration together in an interface (TypeScript's name for "the shape of an object").

interface Signal {
  f: (t: number) => number;
  dur: number;

This, I claim, is enough to do an impressive amount. A signal can be created simply by describing it as a calculation of time, and annotating it with its actual duration. For example, here is a pure tone at A440:

const sine_440: Signal = {
  f: t => Math.sin(2 * Math.PI * 440 * t),
  dur: Infinity,

Discrete signals

However, it isn't enough to be able to work with continuous signals. While they are a useful and flexible way to think about signals, we are ultimately working on a computer, which has to eventually output digital samples to an output device — these digital samples are only later converted to the analog signal that is played back. Also, many efficient digital signal processing algorithms are efficient only on discrete signals, which are sampled at a constant rate over time. Therefore, we need a way to represent these discrete signals. One natural way is with a flat array of samples:

type DiscreteSignal = Float32Array

Since arrays already carry their length, we don't need to store the size of the array separately, so we don't need to annotate this array with a duration like we did the continuous signal. However, the array itself doesn't have enough information to play back the signal — we also need to know how much time each sample represents, the sample rate. So, we will similarly wrap this array in an object as follows:

interface DiscreteSignal {
  samples: Float32Array;
  sample_rate: number;

This is now fully enough information to reproduce the signal.


At this point, we have two flexible and simple representations of signals. But they aren't interchangeable. For example, suppose we wanted to mix together two signals to produce a third signal that is their "sum" — there is no natural way to add together a function and an array. How do we bridge this gap? As it turns out, there are very natural ways to convert between the two.

If we have continuous signal, converting it to a discrete signal is only a matter of sampling it:

function discretize(s: Signal, sample_rate: number) {
  const n = Math.floor(s.dur * sample_rate);
  const samples = new Float32Array(n);
  for (let i = 0; i < n; i++) {
    samples[i] = s.f(i / sample_rate);
  return { samples, sample_rate } as DiscreteSignal;

One might worry that this discards information, and indeed it does, but as the Sampling Theorem states, only information above the Nyquist frequency of sample_rate / 2 is lost. So if we pick a standard sample rate like 44100 Hz, all information in frequencies below 22050 Hz will be preserved, encompassing the entire human hearing range.

What if we have a discrete signal and want to make it continuous? This is a bit trickier, and there are several approaches we can try here. The most "proper" way is to sample the Discrete-time Fourier Transform (DTFT). This interpolates the discrete samples "perfectly" in that if the original continuous signal had no information above the Nyquist frequency, then the DTFT lets us recover the original signal perfectly. However, it is computationally expensive. Since we'll be converting between discrete and continuous signals all the time, we can use the zero-order hold: simply holding the signal at the sampled level for each sampling period.

function zero_order_hold(s: DiscreteSignal): Signal {
  return {
    f: t => s.samples[Math.round(t * s.sample_rate)] || 0,
    dur: s.samples.length / s.sample_rate,

We now have a representation for signals that mostly satisfies the three properties we previously mentioned. It is performant: it is possible to get the value of the signal at any time using only a function call, and we used techniques such as the dur field in Signal and the zero order hold to keep things efficient. And it is easy to manipulate in JavaScript, so it can run directly in your browser. The criterion that this solution doesn't fully satisfy is flexibility: rather than having a single interface that can be used to apply any signal processing technique, the user is forced to separate continuous and discrete signals.

There are a couple ways to achieve the more uniform interface that we originally desired. One way is to model every discrete signal as a continuous signal by immediately converting discrete signals to continuous whenever they are created. Unfortunately, this makes it easy to accidentally do inefficient things, since algorithms that are efficient only on discrete signals can now take continuous signals as input. Another way is to use typeclasses, a language feature that allows us to treat both types of signals generically by specifying their common functionality. However, typeclasses haven't really become mainstream yet, and aren't available in most mainstream programming languages including TypeScript. In the end, I felt that sacrificing flexibility was the best trade off to make.

Two domains

In signal processing, we often jump back and forth between two domains in which to represent a signal: the time domain, and the frequency domain. So far, we have represented signals in the time domain, where signals are expressed as a function over time.

But every signal has an equivalent representation in the frequency domain, where it is instead expressed as a function of frequency. To do this, the signal is decomposed into the sum of a large number of sine waves at varying amplitudes and phase shifts. In fact, we can figure out a unique representation in the frequency domain for any signal using a procedure called the Fourier transform.

The following diagram demonstrates these two domains. (Note that in reality, the frequency domain is complex-valued so that it can encode both magnitude and phase information. For simplicity, we only plot the magnitude.)

Time domain

Frequency domain

The most important part is that if we have a signal in one domain, we can get the signal in the other domain. This means we can manipulate signals directly in the frequency domain, which opens up a wide range of audio effects.

Note that this is one reason we needed DiscreteSignal. On computers, the Fourier transform is usually implemented using an efficient algorithm called the FFT (Fast Fourier Transform). This allows conversion between the two domains in $(O(n \log n))$ time, but it doesn't work directly on continuous signals — it takes a discrete sampled signal as input, and produces one as output. Allowing Signals to be converted freely to and from DiscreteSignals gives us efficient access to the frequency domain.

Aside: The convolution theorem

A key result from Fourier analysis is the convolution theorem:

$$ \mathcal{F}\{f * g\} = \mathcal{F}\{f\} \cdot \mathcal{F}\{g\} \\ \mathcal{F}\{f \cdot g\} = \mathcal{F}\{f\} * \mathcal{F}\{g\} $$

where $(f)$ and $(g)$ are signals, $(*)$ is convolution, and $(\mathcal{F})$ is the Fourier transform.

This theorem is key to understanding many audio processing and synthesis techniques.

The formula can be a bit opaque, so here is a visual explaining what exactly it means.

Original signal


After pointwise-multiplying in time

Original signal

The most important thing to notice here is that pointwise multiplication in the time domain becomes convolution in the frequency domain. Note that this also works in the other way — if you pointwise multiply in the frequency domain (for example, to filter out unwanted frequencies), that implies you need to convolve in the time domain.

Audio processing techniques

In this section, we demonstrate the effectiveness of our design by implementing several audio processing techniques.

Amplitude modulation

Amplitude modulation describes a general class of techniques that involve multiplying the original signal with an envelope. Depending on the frequency content of the envelope, this can produce a variety of effects. Fascinatingly, the core of all amplitude modulation can be expressed in a very small amount of code. Amplitude modulation for two continuous signals can be implemented in our framework as follows:

function mul(a: Signal, b: Signal): Signal {
  return {
    f: t => a.f(t) * b.f(t),
    dur: Math.min(a.dur, b.dur),

Note that signals a and b are treated symmetrically, so we can actually consider either one of them to be the "original" signal or the "envelope". That is, pointwise multiplication is commutative.


At the macroscopic end of the scale, amplitude modulation can be used to apply envelopes to sounds to make them fade in, fade out, or enter with a sharp "attack". One common envelope shape is the ADSR, or the Attack-Decay-Sustain-Release, a piecewise linear function that provides an initial burst of energy before fading to a sustain level, that is held until the key is released.

// adsr: attack, decay, sustain, release
// tl: time, level
function adsr({ at, dt, sl, st, rt }): Signal {
  f: (t) => {
    /*    */ if (t < at) return t / at;
    t -= at; if (t < dt) return ((dt - t) + (t * sl)) / dt;
    t -= dt; if (t < st) return sl;
    t -= st; if (t < rt) return sl * (1 - (t / rt));
    return 0;
  dur: at + dt + st + rt,

For example, adsr({ at: 0.1, dt: 0.3, sl: 0.2, st: 0.5, rt: 0.2 }) looks like this:

And, when applied to a signal (we'll use a sawtooth wave), sounds like this:


If our envelope is instead a low-frequency oscillator with a DC offset near 1, then we get a musical effect called tremolo. It's a subtle way to add richness to a sound.

AM synthesis

If we take amplitude modulation to the limit and use another audible signal as our envelope, a new sound is produced that can sound radically different from the original. This is called AM synthesis, and it works because while low frequencies in the envelope merely change the loudness of a sound over time, high frequencies in the envelope can actually change the shape of the individual waves.

Understanding the result of this procedure only requires understanding the convolution theorem — since applying an envelope is pointwise multiplication in the time domain, the signals are convolved in the frequency domain, which introduces plenty of new frequencies to the signal.

Phase modulation

Taking a step back, let's take another look at our design for continuous signals:

interface Signal {
  f: (t: number) => number;
  dur: number;

A signal has a closure f that takes a number and returns a number. With amplitude modulation, we took two of the returned numbers and multiplied them together. What if, instead, we took one signal and used it as the input to another signal?

function phase_mod(a: Signal, b: Signal): Signal {
  return {
    f: t => b.f(a.f(t)),
    dur: Math.min(a.dur, b.dur),

This results in an exciting new technique: phase modulation. Signal a modulates the phase of signal b, essentially allowing us to play b faster or slower over time, or even to play b back in time. In fact, if we calculate a as the integral of a desired instantaneous frequency modifier, then phase modulation can be used to implement frequency modulation.


If you modulate the phase of a signal using the sum of a unit ramp and a sine wave, then you get the same signal out but with a smoothly oscillating pitch, a musical effect known as vibrato.

FM synthesis

Similarly to AM synthesis, we can modulate the phase of a signal using another audible signal to produce interesting effects. In particular, this can create a sound whose timbre varies over time, a common property of musical instruments.


Finally, suppose we start with white noise. By discretizing the noise applying a convolutional filter, we can filter the frequencies present in the noise to our desired range.


In this article, we discussed some of the design decisions that I made while working on Tsunami. We saw that while it would be ideal to have a single representation of signals, the existence of both continuous and discrete signals requires that we separate them for efficiency. We also demonstrated the viability of our final design by using it to implement several common signal processing techniques.

I hope, also, that this has been a convincing dive into the possibility for manipulating sound by programming. If you wish to explore further, I encourage you to play with Tsunami (though I have yet to write good documentation for it).



C++ got closures in C++11. Java got proper closures in version 8, released 2014. As usual, there is a 40-year gap between the invention of an incredibly useful language feature in academia and its appearance in the mainstream.


While it was once believed that garbage collection is necessary to have closures, the programming language Rust implements closures without a garbage collector by keeping track of references using its ownership system.


Although there are now typed arrays that can store integers and floats.