Detecting Solo Phrases in Music Using Spectral and Pitch-related Descriptors. Journal of New Music Research, 38(4), 343-356.

Hence, contrary to what we state in our paper, we are not the first ones to have tackled solo activity detection. We might still be the first ones to have done it on popular music data, and I hope that we have added some valid contributions to the task.

Secondly, there is two sentences from the original submission that I failed to delete in the camera-ready version. They are “Note, however, that the precision ranking of the individual features is different from the vocal case, where the F feature was best and the {M} and R features showed very similar performance: the method using the R feature alone is now substantially better than that of the simple MFCC feature {M}, suggesting that using the isolated timbre of the solo melody is a decisive advantage.” These are not a valid interpretation of the data.

As anyone reading the paper may have spotted, it’s a relatively simple concept, and also similar to previous work by Sapp, Streicher and Foote. But I believe that using the change vectors at different time scales as feature vectors could result in genuinely new uses for existing audio features.

My colleague Marcus has helped me make the code much more beautiful than it was only a week ago (thanks). It consists of two C++ (.hpp) files (one of which you don’t need if you’re not working with Vamp plugins).

But let’s assume that you actually have a Vamp plugin with a vector valued output handy. Then you only need to include the two files at the top of your vamp implementation, like so:


#include "StructuralChange.hpp"
#include "VampFeaturesAccess.hpp"


assuming that the files are somewhere in your include path. Then, once your original feature is safely calculated, make a Structural Change output from your usual output like this:


fm::last::audio::StructuralChange sc(12);
fm::last::audio::EuclideanDivergencePolicy edp;
sc.calculate(output_feature_set[structural_change_index], output_feature_set[original_feature_index], edp);


That’s it. (Well, to be honest, you also need to define the Structural Change output in getOutPutDescriptors, but you know that if you know how to make a Vamp plugin.) I hope to be able to make some tests and more detailed example code soon. But definitely not before ISMIR!

Links:

my slides,

Audio Flowers,

NNLS Chroma,

Song Prompter.

We also got a nice overview from Elaine Chew about her work of the last decade or so, King’s resident artist Michael Takeo Magruder showed us his techy visualisation techniques, in which he tries to keep the data “pure”. The most junior presenter was Mats Küssner. In what was maybe the most interesting presentation (from my point of view) Mats reported his results of a experiment in which people were asked to draw pitch and loudness of a sound on a pad. Especially the differing behaviours of musicians vs. non-musicians came quite unexpected. Beautiful research.

There were other attendees from the Natural History Museum, and University College, hope I can confirm the names soon.

In general, well done to Dan Tidhar and Daniel Leech Wilkinson for organising this, I really enjoyed it!

@inproceedings{mauch:scm:2011,
Author = {Matthias Mauch and Mark Levy},
Booktitle = {Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011)},
Title = {Structural Change on Multiple Time Scales as a Correlate of Musical Complexity},
Year = {2011}}