xavriley

One reason is issues with property ownership. In the U.K. a company could do this but if the original customers were to see the house, they’d have no legal means of compelling the new owners to take up the arrangement. At least that’s what I understand.

That’s true but in those cases they are passing tones. For examples in a bebop scale you don’t tend to arpeggiate using both the consecutive tones.

Coincidentally there are no commonly used scales or modes with two consecutive semitones. The semitone gaps are always spaced out. With 11 notes (excluding the octave), that only leaves 4 possibilities for a 7 note scale if you remove rotations. These correspond to major, harmonic minor, melodic minor and harmonic major. It’s easy to prove with pencil and paper concentrating on c to c

It will work in Sonic Pi, but I’m looking at ways to make the voice leading of chords more intelligent. At the moment it will voice chords in root position unless you specify otherwise. I’m also looking at writing a parser so the chord symbols can be written naturally as a string

Edit: I’m on the Sonic Pi core team. I mean that I’m looking to add these features to sonic pi soon

Being able to “hear” a fundamental when it’s not there in a spectrogram but the upper harmonics are is known as the missing fundamental and has been known and studied for over 100 years. It seems likely that the auditory system is not only doing a spectral breakdown of sounds (like an FFT) but also something like an autocorrelation of the signal. That autocorrelation will recover the fundamental as long as enough harmonics are present

Well, I for one enjoyed it.

This problem has a name actually - there’s a solution called Bjorklund’s algorithm which was originally used in physics. It was picked up by a music researcher (Godfried Toussaint) who noticed that it generates satisfying rhythms too. https://en.m.wikipedia.org/wiki/Euclidean_rhythm I implemented it myself for Sonic Pi as the spread function.

Sound travels at ~1ft per ms in air so 10ms equivalent to being 10ft away. Sure, you notice the lag at that distance but it’s still possible. 30ms (or 30ft) is the point at which I would give up on trying to play rhythmic music well. That said 30ms latency is a lot of wiggle room where the speed of light is involved

In network science, it’s fairly common to take a graph of something and be able to apply techniques like neighbourhood analysis or clustering which then reveal something about the data. This comes up in all kinds of real world examples from social networks to music preferences etc. What the authors are saying (I think) is that they tried the same tricks on the Panama data and got nothing useful out of it. The standard graph techniques don’t explain anything useful about what’s going on. I would guess (from previous experience working on similar things at opencorporates.com a few years ago) that this is because these networks aren’t constructed according to the real world - instead they’re a response to specific tax rules. For example if you use a specific tax avoidance scheme like the Irish dog leg then the company structure will be defined by the laws of the countries involved. Another reason would be that intermediaries like company formation agents are likely to show connections where they don’t exist in reality. If I use the same company formation agent as say, Donald Trump, it doesn’t follow that we’re related financially. Finally the Panama papers were just about hiding assets in many cases, so you’ll have these chains of ownership going through as many countries as they could be bothered to work with. This is just to give law enforcement etc. a hard time. Again I wouldn’t expect graph analysis to make sense of that on its own. If anyone is interested, banks in the US are required to document their corporate structures so there’s good data available on their structures. It suffers from the same issues though - it’s hard to tell from looking at a graph of companies where the bad stuff is happening.