As part of my research into women composers, I have been playing around with first names – partly as a way of identifying genders among general lists of composers. The most common first name for female composers overall is Mary / Marie / Maria, followed by Anne, Florence, Alice, Dorothy, Elizabeth, Louise and Margaret.1
I thought it would be interesting to compare this with male composers, whose most popular first name is John / Johann / Johannes / Jean / Giovanni. Which are there more of, women, or Johns? Continue reading →
Franz Pazdírek was a Viennese music publisher who, in the first decade of the twentieth century, compiled a ‘Universal Handbook of Music Literature’ – a composite catalogue of all sheet music then in print, worldwide. This ambitious undertaking (which, perhaps not surprisingly, was never repeated) was published over six years, and resulted in nineteen 600-page volumes listing music publications by 1,400 publishers covering every continent except Antarctica. Continue reading →
Deduplication is an important, though often messy and time-consuming, part of many statistical investigations. It is usually required when data comes from several different sources, to identify all of the records that actually refer to the same thing. For example, I have recently been deduplicating the names appearing in the ‘women composers’ sources listed in this previous article. Deduplication may also be needed where several publications of the same work are described in different ways in a library catalogue. Continue reading →
I have recently been working on extracting data on women composers from the various sources listed in this previous article. The first source on that list is a scanned copy of a French translation of a book – Les femmes compositeurs de musique – compiled in 1910 by Otto Ebel. It is available at archive.org here. Although I’ve not had great success in the past in extracting usable data from scanned books, this appears to be a reasonably tidy scan of Ebel, which looks like a useful source on women composers, so I thought I would give it a go. Continue reading →
Triangulation is a research technique that involves looking at the same thing from two different perspectives. In surveying, it enables positions and distances to be calculated by measuring angles from two locations. In the social sciences, it can increase the reliability of conclusions if they are found by two (or more) different methods. And in statistical historical musicology, looking for the same works or composers in two or more datasets can tell us a lot about the characteristics of the datasets, and about the works’ patterns of survival or dissemination. Continue reading →
Following this previous article, a friend got in touch to thank me for disproving some astrological ‘nonsense’. I replied that I had not disproved anything – I had just failed to find supporting evidence – but it did get me wondering about the nature of the conclusions that can be drawn from this sort of analysis.
Suppose, for the sake of argument, that people born under Aquarius do show a significantly higher propensity to become composers than those born under Virgo. Consider these three possible explanations… Continue reading →
On the classical.net website there is a list of 715 composers and their dates of birth. It is straightforward to use this data to identify each composer’s star sign, which produces this interesting chart: Continue reading →
I have just taken delivery of a good ex-library copy of the weighty two-volume ‘International Encyclopedia of Women Composers’ by Aaron I Cohen,1 which will be useful for some research I am doing (as well as for writing some materials to accompany a series of concerts by the excellent Bristol Ensemble next year). The encyclopedia weights about 3½kg, has almost 1,200 pages, and lists 6,196 women composers spanning all continents and over four millennia. Each entry includes brief biographical details, lists of works, and references for further reading. Continue reading →
Often in statistical analysis we need to select things at random. For example, if it is impractical to work with a complete dataset, the only option might be to use a random sample. The science of statistics tells us how to analyse a sample in order to reach conclusions about the entire dataset, and gives us ways to calculate margins of error based on the size of the sample. But I digress.
The graph below illustrates the size of orchestra required to perform symphonies composed between 1750 and 1920. Each symphony is represented by two dots: the red dots and line represent woodwind instruments; blue relates to brass instruments. Continue reading →