Sevillanas. The Spanish punk
Update 11 January: Spotify data added.
According to the English Wikipedia page, «Generally speaking, a sevillana is very light hea[r]ted, happy music». There’s certainly some bland stuff around, but many sevillanas are explosive and raw. In fact, sevillanas are the punk of Spanish music.
I wanted to back this claim up by pointing to the length of the songs on the legendary Sevillanas de los Cuarenta album. It’s a known fact that punk is a genre with very short songs: on average 2:58 according to this analysis by blogger Dale Swanson. It’s the shortest of all the genres he analysed. Well, the average song length on the Sevillanas de los Cuarenta album is 2:44.
However, there may be some problems with this argument. First, some of the songs on the album have a haunting quality about them (for example, A flamenca no me ganas by Gracia de Triana), which makes you wonder if they haven’t been played too fast when they were recorded for CD. This may be an issue, but even if you correct for this the songs on Sevillanas de los Cuarenta would still be shorter than punk songs (for details see below, Method).
More problematic is the fact that short songs appear to have been normal in the 1940s. According to this analysis by Rhett Allain, average song lengths rarely exceeded 3 minutes until the end of the 1960s (see also the debate in the comments on possible explanations). So the shortness of the songs on the Sevillanas de los Cuarenta album isn’t that impressive. In fact, a (possibly non-representative) sample of 1970s sevillanas has an average song length of 3:22, which appears to be quite typical for the 1970s judging by Allain’s data.
The Musicbrainz database used by Allain doesn’t seem to contain many sevillanas. However, the Discogs website, which has data on millions of songs, does contain a few hundred sevillanas. Since posting the first version of the article, I realised metadata can also be obtained from Spotify. Spotify has over 2,500 songs with «sevillanas» in the title but only a few hundred songs per genre for other genres (probably the genre tags aren’t applied consistently). Below is the song length of a number of genres in the Discogs and Spotify databases.
For especially jazz and house, Spotify has other durations than Discogs. Other than that, median song durations are very similar. This is actually quite remarkable given the differences between the datasets. In both datasets, sevillanas tend to be somewhat longer than punk songs, but shorter than the other genres in the analysis.
An analysis by year might be interesting, but tricky: first because the release year in the Discogs data may refer to the year in which an album or song was re-released and second because the number of sevillanas tracks with sufficient information isn’t large enough for that level of precision. The Spotify dataset has no information on the release year of tracks (I guess if I really wanted I could have looked up the release date of the album each track is on).
All in all, the average sevillanas may be somewhat longer than a punk song. But you can still argue that a sevillanas song is in fact a series of even shorter songs, as illustrated by the plot of ¡Ay Sevilla! by Los de la Trocha shown above. The typical sevillanas is a series of short bursts of music that can be as abrupt as any punk song.
Scripts for the analyses are available here.
Songs on Sevillanas de los Cuarenta too fast?
Spotify has three versions of A flamenca no me ganas: the one from Sevillanas de los Cuarenta (2:29 on cd) and two others lasting 2:37 and 2:41. This suggests it’s possible that the «correct» version is up to 8% longer than the one on Sevillanas de los Cuarenta. Even if you assume all the songs on the album should last 8% longer, the average length would become 2:56, still less than for punk. On the other hand, it’s doubtful that all songs on Sevillanas de los Cuarenta are too short. For example, Sevillanas del Espartero by Concha Piquer lasts 2:57 on Sevillanas de los Cuarenta, but Spotify has versions lasting only between 2:27 and 2:35.
The sample of 1970s songs is from albums C, D and F of the HISPAVOX Sevillanas de Oro collection (cd versions), containing songs by los Marismeños, Amigos de Gines and others (not all Sevillanas de Oro albums contain the release year of the songs, but these do).
The Discogs data are available through an API and as monthly data dumps. I thought I’d spare myself the trouble of figuring out how the API works, so I opted for the data dump (the one for 1 December 2014). The downside is that the data is 2.8 GB zipped and 19.2 GB unzipped, so downloading and analysing the data takes a while.
The data dump is xml (the API should return json). I’m not really familiar with xml so I used some not very sophisticated, but effective, regex to sort it out. The data is organised in releases (e.g., albums) that have tags (e.g., for the year in which it was released and for genres and styles). The releases contain tracks that have their own tags, including duration. In order to filter out excessive track lengths I ignored any release containing the string
mix and tracks with a duration longer than one hour.
Discogs uses hundreds of genre and style tags including some quite specific ones like ranchera and rebetiko, but not sevillanas. I decided to include only tracks with
sevillanas in the title. This will exclude some legitimate sevillanas, but I reckon there probably won’t be too many false positives.
I accessed the Spotify data through their web api. As indicated in the article, genre searches resulted in only a few hundred results per genre, which suggests these tags are often omitted.
Plotting a waveform
Based on this discussion, plotting a waveform from a .wav music file using Python should be simple, but saving the plot turned out to be a problem (googling the error message
OverflowError: Allocated too many blocks taught me I’m not the only one having that problem but I didn’t find a solution that worked for me). Instead I turned to R and found that the tuneR package will let you read and plot .wav files without a problem.