Kitchen Nightmares but just the important parts

I searched for 46 more or less emotional keywords. Everything from "shit", "disgusting" and "shocking" to "good", "great" and "awesome" (but most importantly "[__]", which is YouTubes way of decoding profanity). My original intent was to just show how adjectives are used in kitchen nightmares to drive the storytelling. It turned out however that they are used so frequently that the video encapsulates the whole plot. It is perfectly understandable what happens. But also some interesting things happen. For example, lots of adjectives are introduced by chef Ramsay and simply copied by the others. Also some dialogues function perfectly with just a touch of humor. Look out for Ramsay talking to the rastaurants owner Joe.

Bachs Prelude to the first Cello Suite, the first 69 Notes sorted by pitch

The greatness of this edit mostly consists in the fact that every cut has a fade in and a fade out!


Bach vs Bach vs me

I took Bachs Cello Suite No. 1 and paired it up with the Banderie from the Orchestra Suite BWV 1067. The catch is: I transposed the score so the key is a tritone above the key from the Cello suite. What that means is the impurities of the cuts lead to the most disgusting harmonics possible. But through this mess of ear-bleeding-ugliness, you can still hear the melody. Hopefully.

Cats...?

As a big fan of cats, I naturally wanted to test the AIs ability to detect them. For what is the world without cats? Less hairy, probably. I knew AI is under a lot of critique lately and so I chose the easiest source material I could think of: The famous Weimar Cat Documentary, specifically the first 10 Minutes of it. What should have been the output was about a good 5 minutes of nothing but cats, but instead I sat in front of my computer watching in horror as the algorithm not only failed to identify about 95% of all the cats, but also just straight up selected a dog. And those cats weren't difficult either. I could forgive a furball in the corner of a shot not being selected, but the AI just missed frame-filling close-ups. CONCLUSION: the software was trained by dog-persons. 3/10.

The Julian Mosbach String Quartet

I can't play the cello. But in this exercise I used computers to make it sound even worse. Ever since I got into classical music, I found Bach's "The Art Of The Fugue" to be just the most beguiling set of pieces out there. I always wanted to play it. However I only know how to play the piano and while there is a version for piano (which sadly is incredibly difficult to play and thus even the most skilled pianists play it way too slow - not for me) the piece just shines played by a string quartet. I, however can't play any string instruments, not do I own any. But my sister does. She used to play the cello when she was younger and showed me how to produce notes. So I sat down with that half-scale childrens cello and recorded every note in chromatical order for our algorithm to make me a cello quartet. And this is where the trouble begins. I did not record to a metronome, but instead thought I could just record long notes, which PD1. Public Domain 2. in the context of the university PD sometimes stands for Product Design 3. Police Department could then chop up. It does not. It finds the note that is closest in length and plays that in its entirety. (by the way: the video of me playing the cello is eleven minutes long, which means I sat through hours of just listening to it again and again then the sorted notes and that for every instrument to find that mistake). So I needed to give the algorithm perfect note lengths. I did that by putting the original audio in a DAWDigital audio workstation – combination of hardware and software to create music and sound digitally based on 115BPM (the pieces tempo) and cut each note in semi, quarter and eigth notes. I got very lucky here because the piece mostly consists of these. I ran the analysis again, to find that the detection of the notes themselves is very challenging, since the cello has so many overtones that it just confuses sigmund. At first I tried to EQ the first and second overtones (octave and fifth above), which again is 148 notes that each get their own treatment. But that again didn't work out. So in a last final-straw attempt I took a synth with a simple sine wave and manually reproduced the original audio with quantized notes. 148 midi notes placed by hand. Needless to say decisions were regretted at this point. BUT THEN. IT WORKED. Kind of. Each instrument sounded roughly how it was supposed to, but when I put them together nothing worked. This was because the piece contained linked and dotted notes. And the algorithm looked for absolute lengths. So instead of putting even more notes in the original audio, I edited the midi files to remove any links and dots. So a dotted quarter note became a quarter and then an eigth of the same pitch. Arguably at this point its not Bach anymore, its Mosbach-Bach (say it fast). Now, finally after all this editing it was ready to work. I was emotionally wrecked but excited to see what PD1. Public Domain 2. in the context of the university PD sometimes stands for Product Design 3. Police Department had cooked for me. And something happened that I didn't expect. For some reason, the algorithm had pitched each instrument 3 semitones down. Except for the cello-voice. This took me hours to find, because I checked the cellos pitch again and again and it was correct. But then finally, after pitching the cello down to match the other instruments, I was ready to press play... - just to find out, that our algorithm does not take into account pauses. So whenever there should be a pause, it simply played the next note without hesitation. At this point I am wondering whether I should have simply cut everything by hand. That might have saved me A LOT of time. But I kept on working and edited each pause by hand, using the other instruments as reference.

Its sounds...accetable? Maybe? There is one very prominent issue still prevalent which I wasn't able to solve. The instruments alone work perfectly, but played together, they keep going out of sync. I think there is two reasons for that: Firstly any error in detection leads to a slightly changed note length which multiplies when more notes are played. The violin 1- voice for example has the most eigth notes and seems to always drag behind the others (constant error multiplied). And secondly I think that sigmund detects high notes faster than low notes, which makes sense physically, because it should need at least one full cycle of the wave before detecting a note. And that just takes twice as much time per octave you go down. To give that combined error a number: the pieces original tempo was 115 BPMBeats per minute but due to lag between the notes and inconsistent detection the PD1. Public Domain 2. in the context of the university PD sometimes stands for Product Design 3. Police Department-Version is just 108BPM. In this case, manually editing the pauses really saved a lot, because it meant that the instruments were always synced up again every 30 seconds or so.

This might have been it, but I decided to take one last step. I am a firm believer in "the product", meaning that a "good" process in my eyes shouldn't make up for a bad product. Or in other words: nobody is going to applaud your for putting lots of effort in something if the end product is bad. So, as this is mostly a video editing class, I took the original sound files again and cut it up to build a Kontakt-Sample-Instrument. (If you don't know what that means: Sampling takes a bunch of notes and maps them to midi notes, which gives you the ability to play something using the notes you recorded in real life). I completed it with a legato-script, reverb and a binaural mix for complete immersion. After that I took the same video and just married them together: