Rogue Scholar Beiträge

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Veröffentlicht in rOpenSci - open tools for open science
Autor Jeroen Ooms

A few weeks ago, prof Matt Crump wrote a blog post in which he explores tools to handle MIDI data in R, in preparation for a cognition experiment that involves creating musical stimuli. In the article he ends up using a mix of external command line tools ffmpeg and fluidsynth and a python module.

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Earlier this year I set up a bot on Mastodon. The bot, AlbumsX3, posts an album suggestion twice-a-day. Performance has been good. It has only missed a few posts due – I think – to server glitches. However, I have made a couple of tweaks to upgrade the bot since my last post, so I thought I would detail them here. Preventing duplicate posts In the last post I wrote: Well, it wasn’t long before I needed to revisit this issue.

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I have long admired albums2hear, a Twitter bot that posts albums. You can read a bit more about it here. There was no mastodon equivalent and so I decided to build one. You can follow the bot – currently called Albums Albums Albums (or AlbumsX3) – here. Idea behind the bot The idea is to periodically post an album.

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Another post about my time on Twitter. I will post the code in a separate post so that the R-bloggers don’t syndicate this one, which is about music . In my time on Twitter I occasionally posted about what I was listening to. I did this with a #NowPlaying hashtag. I wanted to preserve these tweets – and they can all be found below.

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This website is all about niche tech tips and this post is no exception. I run a Plex media server. This allows me to stream my music collection when I am out and about. Plex pass owners get the nifty plexamp app for listening to music, which I really like. The databasing for the movie and TV show side of Plex works great, but the music side has its peculiarities. The problem I have a large music library.

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Niche tech tips are the cornerstone of this website, and here is another. How to upgrade an iTunes/Music library whilst maintaining the database. I know everyone streams music these days – hence this is likely a very niche tech tip – but I still maintain a large music database on a server. The data in my library is precious (to me) since it tracks my listening habits over 17 years.

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In early December, Spotify users received a list, known as Spotify Wrapped, of the tracks they listened to most in 2021. As a committed non-streamer, I was a bit envious of these lists; so I decided to assemble my own list using my Music database. This was possible by comparing two snapshots of my Music database in xml format* that were taken in 2020 and yesterday.