Blogs on Staring Contest

Unresolved Speech?

Fri, 17 Sep 2021 22:56:03 +0000

On the latest episode of the Reconcilable Differences podcast, John Siracusa and Merlin Mann open the episode by discussing the “sing-song” or “uptalk” openings of some YouTube videos. John mentions the LockPickingLawyer introduction as an example of this; he feels as though the end feels like it’s hanging or unresolved.

I was so intrigued by this that I stopped listening to the episode and started investigating. I went to the most recent episode of the LockPickingLawyer channel and grabbed the first couple seconds of audio.

lockpickinglawyer.m4a

You can certainly hear what John is talking about. I tried figuring out what the closest pitches were to each word, but my ears are not very well trained. So, I turned to technology instead. I found that there’s a pretty cool Python package called crepe that uses a trained neural network model to estimate the fundamental frequencies of a given sound sample (as well as its confidence in that estimate). After quick installation of the package and its dependencies, I was able to predict the pitch from the clip.

Ranking the Butterfield Diet

Mon, 09 Mar 2020 23:00:07 +0000

The Brian Butterfield diet is a very funny sketch from The Peter Serafinowicz Show. I wanted to know what the objectively funniest options for Treat Day were, so I fired up Survey Monkey and asked the members of the Do By Friday Discord to rank all the items from the video. For some reason, thirteen people filled out the survey.

The results have been…increbidle

I averaged the results to get an average rank for each item (circles in the plot below). I’ve also shown individual responses (small squares). Note that better ranks are toward the right.

Results of an extremely scientific Butterfield Diet survey.

I’ve also grouped the items into arbitrary tiers based on their average rank. Let’s go through them together.

C-Tier

C-Tier results

Double-click to edit…

Most people ranked these items in the bottom half, but there’s not a whole lot of difference between them.

A Translated Guide to The Taken King Changes

Wed, 26 Aug 2015 06:03:04 +0000

Steve Lubitz of the Isometric podcast complained in their most recent episode that all the articles about the new Destiny expansion, The Taken King, were written in fairly-incomprehensible Destiny-lingo (Destinese?). I figured I’d take a shot at explaining what’s new in the expansion in terms that a general gamer could readily understand.

I am by no means a hardcore Destiny player. Since I don’t have a regular group that also plays (and little energy/time to try to find one), I haven’t done the serious endgame content like raids or most of the other content that doesn’t automatically match you with other players. I do have a character at the highest possible level, but that’s about it.

I’m going to start with a summary of what Destiny is to lay the groundwork for understanding what is changing with the expansion. This will try to hit the highlights; it is not intended to be comprehensive.

What is Destiny?

Destiny is a first-person shooter that has some elements similar to massively-multiplayer online games (MMOs, like World of Warcraft. It can be played by yourself, cooperatively with a few other players, or competitively with teams of players facing each other. When you play by yourself or cooperatively, you are completing missions or scenarios against computer opponents (called player-versus-environment modes, or PvE). Competitive modes are called player-versus-player, or PvP.

Parallel Treks

Thu, 18 Dec 2014 18:24:00 +0000

Around the time the podcast Random Trek completed its first 30 episodes, a quantum fissure mysteriously appeared. Naturally, we investigated it using a subspace differential pulse and — to our surprise — we discovered 10,000 parallel timelines which all had their own versions of Random Trek.

After recalibrating the sensor array (as you do), we were able to determine which episodes have been watched so far in these parallel universes. We used these data to address some of the pressing questions from the Random Trek fanbase.

First, did our timeline’s Random Trek really have an unusual number of Voyager episodes as it was starting out? For reference, ours had two of its first four episodes from Voyager, six out of its first 20, and seven out of its first 30. We can look at the distributions of the episode selections across all the timelines to see how unusual this was.

As Random Trek’s host Scott McNulty might say, spoiler alert: it’s not very unusual. About a quarter of the other timelines (2,420 to be precise) had two or more Voyager episodes in their first four episodes. Below, you can see the distributions of the first 20 episodes and first 30 episodes (our timeline’s values are marked with red triangles). Our timeline is sitting close to the middle of the Voyager distributions. Perhaps the most unusual thing about ours here appears to be the relatively low number of Deep Space Nine episodes, as only 891 of the other 10,000 timelines had two or fewer DS9 episodes in the first 20, and 923 had four or fewer in the first 30 episodes.

Graphing The Incomparable

Fri, 10 Jan 2014 05:01:09 +0000

It turns out I’m probably going to have good reasons to need to learn Python in the near future, so I’m finding little projects to practice writing scripts. The Incomparable recently wrapped up the year with a retrospective episode in which some people talked about being on more than in the past, while others mentioned they were on less. I was inspired to see if there was a way to visualize who has been on episodes together and how that has changed over time.

I collected data from the RSS feed and the web site to figure out which people were on which episodes. I could then put that into a matrix with episodes as the rows and people as the columns (with 1’s or 0’s depending on whether they were on the episode or not). Multiplying the transpose of that matrix by itself gives a new matrix that shows how many episodes people had in common. (This is just like what Kieran Healy did in his Paul Revere metadata post.)

Through the Eyes of the Jackals

Tue, 26 Feb 2013 02:18:28 +0000

Example cloud generated from title submissions for The Incomparable episode 108.

With all the great podcasting content produced these days, I often end up wishing for easier ways of finding past episodes. Memory does not always serve well enough, and clicking through episode descriptions can be tedious (and unreliable for podcasts that span many topics in a single show).

Clearly it would be great to have some way of searching or browsing podcast episodes, but it’s equally clear that it’s hard to do without accurate transcriptions.

As I’ve mentioned before (in my only other post so far), live listeners of shows on the 5by5 network can submit title suggestions via Showbot, which was created by Jeremy Mack. Suggestions are almost always quotes from the shows, and during popular shows the submissions are so frequent it seems almost like a live transcript. Of course it’s not—submissions are unsurprisingly biased toward humorous or silly quotes—but I wondered if they could be used in some way.

Show Titles

Fri, 27 Jul 2012 14:24:32 +0000

One of the fun parts of listening to 5by5 shows is helping pick show titles. Jeremy Mack created the Showbot, which lets members of the 5by5 chat room suggest titles as the shows run live (it also lets anyone vote for their favorite suggestions).

Of course, the hosts have the final say over what title gets used, and, as you can imagine, different hosts have different preferences for show titles. For example, John Siracusa, host of Hypercritical, only will consider things that are actually said on his show as possible titles. Inspired by Kieran Healy’s recent analysis of show durations, I decided to look at naming trends across several 5by5 shows and see how they have changed over time.

The main variable I looked at is the show title’s “originality.” That’s in scare quotes because I’m defining originality very narrowly – here, it is just a reflection of the number of Google hits for the exact title phrase. Since the data span many orders of magnitude, I log-transform the number of hits (adding 0.5 to deal with zeros) and call it an “originality index.” Note that numerically smaller indices are more original, so I have reversed the y-axis on the graphs to put the indices that reflect more originality at the top.