Today’s Lesson: An Luu – Pourquoi tu me fous plus des coups?


I have not been spending enough time on my blog lately, and it’s time for that to change with “Today’s Lesson:” a weekly blog segment in which I review a song or an album that I’ve recently been listening to and find myself interested in. Unlike a traditional review, I won’t be assigning scores or anything. Instead, I focus on writing about music that I think is worth writing about. In some tacit sense, you can assume that these are all recommendations. However, the real purpose of this segment is to use a song as a stepping off point to talk about whatever else is on my mind. We’re too quick to separate music out from other things in life, and this is my own small way of questioning the merit of doing that.


Just bask in the glory of this unabashedly 80s pop single by An Luu, a French actress. I don’t know how or why Spotify recommended this track to me, but I sure am grateful they did. I love it. There is absolutely no pretense, posturing, or even showing off. The song sets up a basic, spartan groove as An’s breathy voice floats over top. There is a certain innocence to the vocal delivery, too. A vulnerability. I’ve listened to the song maybe dozens of times over the past week or so. I’m utterly fascinated by it, partially because I challenged myself to understand the song without looking up a translation of the lyrics.

In writing this post, though, I decided to lift that curtain and track down a translation of the lyrics. Despite being molested by Google Translate, the song’s lyrics are immediately understandable: she’s asking a lover why he stopped beating her – maybe he doesn’t love her anymore? That’s heavy stuff, to put it mildly. The scenes are immediately evocative of Lou Reed’s Berlin concept album: domestic, bluntly laying out the cruelty, and also confronting the hard-to-understand inner workings of the abused that stay in the relationship. Though it might just be coincidence, the lover in An’s song is referred to as LouLou…

I won’t lie, I’m kind of bowled over right now. And this is the kind of musical experience I really cherish: drawn into a complicated beauty of a song. Listen to it. It’s just pretty. But it’s simultaneously so baldly ugly too. This is something special because it gives us a slice of life. A bad slice, but a slice none-the-less. Music can contain truths that are lost in mere words and, though disgusting as they may be, we mustn’t run from this. This is us, this is who we are. This is reality.

In our social media, we often find ourselves framing ourselves in a certain light, highlighting the good and erasing the bad. Hiding it away, and only putting your best self forward. But it’s a lie, isn’t it? And even when someone tries to show a more complete picture, we look down our noses at them for sharing drama or just being too sad. Something media scholars study at length is this phenomenon of how we present ourselves and how we act when (we think) someone is watching: we change. We pretend to be something else. An Luu strips away all of that with this song, and it’s arresting in its beauty.

Once More, with (Less) Feeling: artificialized vocals



This semester has been challenging and fun. One class, in particular, really pushed me. It’s a class on music information processing. In other words, it’s a class on how computers interpret and process music as audio. I’ll spare you a lot of the technical stuff, but generally speaking we were treating audio recordings are vectors with each value of the vector corresponding to the amplitude of a sample. This allowed us to do all sorts of silly and interesting things to the audio files.

The culmination of the class is an independent project that utilizes principles learned from the class. This presented a unique opportunity to design an effect that I’ve wanted but couldn’t find: a way to make my voice sound like a machine. Sure, there’s vocoders, pitch quantizers, ring modulators, choruses, and more… but they don’t quite do what I want. The vocoder gets awfully close, but having to speak the vocals and also perform the melody on a keyboard is no fun. iZotope’s VocalSynth actually gets very close to what I want, but even that is hard to blend the real and the artificial. There had to be something different!

And now there is. Before I can explain what I did, here’s a little primer on some stuff:

Every sound we hear can be broken down into a combination of sine waves. Each wave has 3 parameters: frequency (pitch), amplitude (loudness), and phase. You’ll note that phase doesn’t have an everyday analog like frequency does with pitch. That’s probably because our hearing isn’t sensitive to phase (with some exceptions not covered here). Below is a picture of a sine wave.


See how the wave starts at the horizontal line that bisects the wave? This sine wave has a phase of 0 degrees. If it started at the peak and went down, it would have a phase of 90 degrees. If it started in the middle and went down, it would have a phase of 180, and so forth.

As I said, we don’t really hear phase, but it’s a crucial part of a sound because multiple sine waves are added together to make complex sounds. Some of them reinforce each other, others cancel each other out. All in all, they have a very complex relationship to each other.

This notion of a complex wave represented by a series of sine waves comes from a guy named Fourier. (He’s French so it’s “Four-E-ay.”) There’s a lot of different flavors of the Fourier Transforms, but the type relevant here is the Finite (or Fast) Fourier Transform. This one only deals with finite numbers, which are very computer friendly.

There’s a subset of the FFT called the STFT (short-time Fourier Transform) that maintains phase information in such a way that it’s easier to play with. One of the simplest tricks is to set all of the phases to 0. This makes a monotone, robotic voice with a few parameters changed. Hm! That’s fun, but not very musical.

STFTs, as the name implies, analyze very short segments of audio then jump forward and analyze another short segment. Short, in this case, means something like 0.023 seconds (1024 samples at 44.1k) of audio at a time. Here’s where the robot voice comes in: instead of jumping ahead to the next unread segment, I’ll tell it to jump ahead, say, a quarter of the way and grab 0.023 seconds, then jump another quarter and so on. This imposes a sort of periodicity to the sound, and periodicity is pitch!

By manipulating the distance I am jumping ahead, I can impose different pitches on the audio. This is essentially what I did in my project. More specifically, I:

  1. Made a sample-accurate score of the desired pitches
  2. Made a bunch of vectors for start time, end time, and desired pitches (expressed as a ratio)
  3. Made a loop to step through these vectors
  4. Grabbed a chunk of sound from a WAV file
  5. Performed an STFT using the pitches I plugged in
  6. Did an inverse STFT to turn it back into a vector with just amplitube values for samples
  7. Turned that back into a WAV file

(See the end of the post for a copy of my code.)

Here’s what I ended up with!

And here’s what it started as:

Please be forgiving of the original version. It’s not great… I was trying to perform in such a way that would make this process easier. It did, but the trade off was a particularly weak vocal performance. Yeesh. My pitch, vowels, and timbre were all over the place!

Anyway, here’s the code. You’ll need R (or R Studio!) and TuneR. Oh, and the solo vocal track.


stft = function(y,H,N) {
 v = seq(from=0,by=2*pi/N,length=N) 
 win = (1 + cos(v-pi))/2
 cols = floor((length(y)-N)/H) + 1
 stft = matrix(0,N,cols)
 for (t in 1:cols) {
 range = (1+(t-1)*H): ((t-1)*H + N)
 chunk = y[range]
 stft[,t] = fft(chunk*win)

istft = function(Y,H,N) {
 v = seq(from=0,by=2*pi/N,length=N) 
 win = (1 + cos(v-pi))/2
 y = rep(0,N + H*ncol(Y))
 for (t in 1:ncol(Y)) {
 chunk = fft(Y[,t],inverse=T)/N
 range = (1+(t-1)*H): ((t-1)*H + N)
 y[range] = y[range] + win*Re(chunk)

spectrogram = function(y,N) {
 bright = seq(0,1,by=.01) 
 power = .2
 bright = seq(0,1,by=.01)^power
 grey = rgb(bright,bright,bright) # this will be our color palate --- all grey
 frames = floor(length(y)/N) # number of "frames" (like in movie)
 spect = matrix(0,frames,N/2) # initialize frames x N/2 spectrogram matrix to 0
 # N/2 is # of freqs we compute in fft (as usual)
 v = seq(from=0,by=2*pi/N,length=N) # N evenly spaced pts 0 -- 2*pi
 win = (1 + cos(v-pi))/2 # Our Hann window --- could use something else (or nothing)
 for (t in 1:frames) {
 chunk = y[(1+(t-1)*N):(t*N)] # the frame t of audio data
 Y = fft(chunk*win)
 # Y = fft(chunk)
 spect[t,] = Mod(Y[1:(N/2)]) 
 # spect[t,] = log(1+Mod(Y[1:(N/2)])/1000) # log(1 + x/1000) transformation just changes contrast
 image(spect,col=grey) # show the image using the color map given by "grey"

N = 1024
w = readWave("VoxRAW.wav")
y = w@left
full_length = length(y)

bits = 16
i = 1
# this is a vector containing all of the pitch change onsets, in samples
start = c(0,131076,141117,152552,241186,272557,292584,329239,402666,

#this is a vector containing all of the last samples necessary for pitch changes. in samples
end = c(131075,141116,152551,241185,272556,292583,329238,402665,459153,
 5944542,6068828,6086056,6223713,6250542,6275056, full_length)

#this ratio determines the pitch we hear by manipulating the window size
ratio = c(4.18128465,3.725101135,3.318687826,3.725101135,4.693333333,

w = readWave("VoxRAW.wav")
sr = w@samp.rate
y = w@left
ans = 0

for (i in 1:81) {
#the loop steps through each of the 3 above vectors 
frame = y[start[i]:end[i]] #take a bit of the wave from start to end
H = N/ratio[i] #make the window this size to change the perceived pitch
Y = stft(frame,H,N)

Y = matrix(complex(modulus = Mod(Y), argument = rep(0,length(Y))),nrow(Y),ncol(Y)) # robotization
ybar = istft(Y,H,N)
ans = c(ans,ybar) #concatinate all of the steps along the way

i = i + 1 #step through the loops

ans = (2^14)*ans/max(ans) #do some rounding to make sure it all fits
u = Wave(round(ans), samp.rate = sr, bit=bits) # make wave struct
#writeWave(u, "robotvox.wav") #save the robot version
o = readWave("VoxRAW.wav")
o = o@left
spectrogram(o, 1024) #what does the original recording look like?
r = readWave("robotvox.wav")
r = r@left
spectrogram(r, 1024) #what does the robot version look like?
#play(u) #listen to the robot version

Rothko on research


Mark Rothko, a favorite painter of mine, once put forth a manifesto with Adolph Gottlieb about the nature of art in 1943:

1. To us art is an adventure into an unknown world, which can be explored only by those willing to take the risks.

2. This world of imagination is fancy-free and violently opposed to common sense.

3. It is our function as artists to make the spectator see the world our way not his way.

4. We favor the simple expression of the complex thought. We are for the large shape because it has the impact of the unequivocal. We wish to reassert the picture plane. We are for flat forms because they destroy illusion and reveal truth.

5. It is a widely accepted notion among painters that it does not matter what one paints as long as it is well painted.

6. There is no such thing as a good painting about nothing.

7. We assert that the subject is crucial and only that subject matter is valid which is tragic and timeless. That is why we profess spiritual kinship with primitive and archaic art.


What’s so striking to me about these statements is that you can replace the various uses of art and painting with research and theory and the manifesto works just as well. Most points seem to translate directly. Rothko’s second point may be the hardest to defend as the rejection of common-sense may invoke a knee-jerk reaction from the pragmatist in us all, but I’d be willing to interpret it as a romanticized expression for the necessity of imaginative and original research questions. The fourth point is my favorite. It speaks to the excitement of finding a simple yet powerful theory that brings transparency to prior opaqueness. Lastly, the final point about the subject must be timeless and tragic: I’m not sure exactly what Rothko means by timeless, or rather how literally he meant it. Humanity is obviously not timeless, so I’m not sure how a piece of art – which by necessity reflects the human condition – could truly be timeless. Now, assuming he meant timeless to humanity, then I think it’s a fantastic point. The best answers to questions are not temporally locked, but true everywhere and as long as humanity is bumbling about.

(Yes, physics, I can see your smug look from here. You’ve got the “timeless” thing locked down. Congrats.)