Searching music offside of the mainstream can be tedious. Recently i fell for a particular jazz piano genre, called “Harlem Stride Piano” while listening to a radio broadcast. Stride piano developed in the 1920s and 1930s in New York as an advancement from Ragtime. It is characterized by a rhythmic left hand play, where the pianist alternates a bass note or octave on the first and third beat with chords on the second and fourth beat, while the right hand plays the melody line. This causes the left hand to leap great distances on the keyboard, often at neck-break speed. Back then, pianists like Fats Waller, James P. Johnson or Eubie Blake were famous stride virtuosos.
Louis Mazetier introduces harlem stride piano
Today, only a few pianists are capable to play stride, and I was curious to find out about contemporary ”Harlem Stride Piano” interpreters and recordings.
The textual search for “Harlem Stride Piano” in iTunes led to zero results. Even in the advanced search of iTunes, you can only search for artists and interpreters, title- or track names, but not for genres. A search just for “stride piano” brought up one album, fortunately carrying both terms in its title. Similar, Spotify´s search for “Harlem Stride Piano” did not match anything, whereas a search for “stride piano” returned a few albums because of the use of the terms “piano” and “stride” in their titles or tracks.
Still unsatisfied, i continued the search for contemporary stride players in Google, YouTube and Wikipedia to find out about artists like Louis Mazetier, Günther Straub or Bernd Lhotzky. Knowing their names finally helped me to find the desired tunes in iTunes and Spotify.
This little research clearly depicts the limits of text based music search. It´s results depend largely on the coincidental presence of the chosen search terms in the title or artist name. If you have nothing but a tune, search is often impossible. What´s missing is search for music based on the sounds of a sample track.
While chasing contemporary “Harlem Stride Piano” records through Spectralmind´s audio intelligence platform, I certainly would have used Fats Wallers “Ain´t Misbehavin“. For sure, a sound-similarity search would have brought up more and better results in far less time.