Digital Sound Processing with Real Time Tools

NDH (Neue Deutsche Härte) with Seeq


Ever thought about analyzing music with Seeq. Signal processing is one of my favorite topics. It is useful in many areas of science and engineering, and if you understand the fundamental ideas, it provides insight into many things we see in the world, and especially the things we hear. Of course first and foremost I love music.  Ever heard Rammstein? In „Du hast“ you can find deep philosophic insights. So let’s start:

First I needed to load the song onto our Seeq-Server. Of course it all starts with some engineering. But since Seeq has released „Seeq Data Labs“ I could do some preparing work like convolution and discrete Fourier Transform

Data Source and technical background

Song: „Du hast“

Genre: „Industrial Metal“

Artist: „Rammstein“

Duration: 3′ 54“

from the CD „Sehnsucht“ so the sampled framerate is 44,1kHz.

And the spectral decomposition looks like this.

In this signal the fundamental frequency has the largest amplitude, so it is also the dominant frequency. Normally the perceived pitch of a sound is determined by the fundamental frequency, even if it is not dominant. This is how good music is build.

 Seeq Data Labs

Preparing for Seeq Data Labs means above all converting the singal data into a pandas dataframe with some Seeq Datetime requirements.

Having 44.100 frames per second I decided to adjust every frame to one second in the Seeq Time Domain, starting from 2019-01-01.

Some Math

This means that the duration of 3:54 in the signal time domain will extend to 234*44.100 data points (frames) in the Seeq time domain = 239 day of 2019 (Audiosignal has 10 million data values).

Deep Insights

Du, du hast, du hast mich
Du, du hast, du hast mich
Du, du hast, du hast mich
Du, du hast, du hast mich

[Pre-Refrain]
Du, du hast, du hast mich, du hast mich
Du hast mich gefragt, du hast mich gefragt
Du hast mich gefragt und ich hab‘ nichts gesagt

[Refrain]
Willst du bis der Tod euch scheidet
Treu ihr sein für alle Tage?

Nein
Nein

Willst du bis zum Tod, der scheide
Sie lieben auch in schlechten Tagen?

Nein

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