For the 2019 edition of MIREX, the Music Information Retrieval Evaluation eXchange, we at the Centre for Digital Music once again submitted a set of Vamp audio analysis plugins for evaluation. This is the seventh year in a row in which we’ve done so, and the fourth in which no completely new plugin has been added to the lineup. Although these methods are therefore getting more and more out-of-date, they do provide a potentially useful baseline for other submissions, a sanity check on the evaluation itself, and some historical colour.

Every year I write up the outcomes in a blog post. Like last year, I’m rather late writing this one. That’s partly because the official results page is still lacking a couple of categories, and says “More results are coming” at the top — I’m beginning to think they might not be, and decided not to wait any longer. (MIREX is volunteer-run, so this is just a remark, not a complaint.)

You can find my writeups of past years here: 2018, 2017, 2016, 2015, 2014, and 2013.

Structural Segmentation

Again no results have been published for this task. Last year I speculated that ours might have been the only entry, and since we submit the same one every year, there’s no point in re-running it if nobody else enters. Pity, this ought to be an interesting category.

Multiple Fundamental Frequency Estimation and Tracking

A rebound! Two years ago there were 14 entries here, last year only three: this year we’re back up to 12, including our two (both consisting of the Silvet plugin, in “live” and standard modes).

This category is famously difficult and I think still invites interesting approaches. An impressive submission from Anton Runov (linked abstract is worth reading) uses an approach based on visual object detection using the spectrogram as an image. Treating a spectrogram as an image is typical enough, but this particular method is new to me (having little exposure to rapid object detection algorithms). The code for this has been published, in C++ under the AGPL — I tried it, it seems like good code, builds cleanly, worked for me. Nice job.

Another interesting set of submissions achieving similar performance is that from Steiner, Jalalvand, and Birkholz (abstract also well worth a read) using “echo state networks”. An ESN appears to be like a recurrent neural network in which only the output weights are trained, input and internal weights remaining random.

Our own submissions are some way behind these methods, but there’s plenty of room for improvement ahead of them as well: I think the best submissions from 2017’s bumper crop still performed a little better than any from this year, and perfection is still well out of reach. (At least among labs that submit things to MIREX. Who knows what Google are up to by now.)

Results pages are here and here.

Audio Onset Detection

No results have (yet?) been published for this task.

Audio Beat Tracking

Another quiet year, with Sebastian Böck’s repeat submission still ahead. Results are here and here.

Audio Tempo Estimation

No results are yet available for this one either.

We made a tiny change to the submission protocol for our plugin this year (as foreshadowed in my post last year, I changed the calculation of the second estimate to be double instead of half of the first, in cases where the first estimate was below an arbitrary 100bpm) and I was curious what difference it made. I’ll update this if I notice any results having been published.

Audio Key Detection

We actually submitted a “new” plugin for this category: a version of the QM Key Detector containing a fix to chromagram initialisation provided by Daniel Schürmann, working in the Mixxx project. We submitted both “old” (same as last year) and “new” (with fix) versions, and saw significantly better results from the fixed version in all five test sets. So thank you, Daniel.

The most interesting submission, from Jiang, Xia, and Carlton, actually seems to be a presentation of a new(ish?) crowd-annotated dataset, used to train a key detection CRNN. It gets good results, with the rather critical caveat that the crowd-sourced training dataset could overlap with the MIREX test data. It’s not clear from the abstract whether the dataset is publicly available — I think it may be accessible via a developer API from the company (Hooktheory) that put it together.

Results are here.

Audio Chord Estimation

Last year was busy, this year isn’t: it sees only one submission besides ours, a straightforward CNN from the MIR Lab at National Taiwan University, whose performance is roughly comparable to our own Chordino. Results here.

 

The 2018 edition of MIREX, the Music Information Retrieval Evaluation eXchange, was the sixth in a row for which we at the Centre for Digital Music submitted a set of Vamp audio analysis plugins for evaluation. For the third year in a row, the set of plugins we submitted was entirely unchanged — these are increasingly antique methods, but we have continued to submit them with the idea that they could provide a useful year-on-year baseline at least. It also gives me a good reason to take a look at the MIREX results and write this little summary post, although I’m a bit late with it this year, having missed the end of 2018 entirely!

For reference, the past five years’ posts can be found at: 2017, 2016, 2015, 2014, and 2013.

Structural Segmentation

No results appear to have been published for this task in 2018; I don’t know why. Last time around, ours was the only entry. Maybe it was the only entry again, and since it was unchanged, there was no point in running the task.

Multiple Fundamental Frequency Estimation and Tracking

After 2017’s feast with 14 entries, 2018 is a famine with only 3, two of which were ours and the third of which (which I can’t link to, because its abstract is missing) was restricted to a single subtask, in which it got reasonable results. Results pages are here and here.

Audio Onset Detection

Almost as many entries as last time, and a new convolutional network from Axel Röbel et al disrupts the tidy sweep of Sebastian Böck’s group at the top of the results table. Our simpler methods are squarely at the bottom this time around. Röbel’s submission has a nice informative abstract which casts more light on the detailed result sets and is well worth a read. Results here.

Audio Beat Tracking

Pure consolidation: all the 2018 entries are repeats from 2017, and all perform identically (with the methods from Böck et al doing better than our plugins). Every year I say that this doesn’t feel like a solved problem, and it still doesn’t — the results we’re seeing here still don’t seem all that close to human performance, but perhaps there are misleading properties to the evaluation. Results here, here, here.

Audio Tempo Estimation

This is a busier category, with a new dataset and a few new submissions. The new dataset is most intriguing: all of the submissions perform better with the new dataset than the older one, except for our QM Tempo Tracker plugin, which performs much, much worse with the new one than the old!

I believe the new dataset is of electronic dance music, so it’s likely that much of it is high tempo, perhaps tripping our plugin into half-tempo octave errors. We could probe this next time by tweaking the submission protocol a little. Submissions are asked to output two tempo estimates, and the results report whether either of them was correct. Because our plugin only produces one estimate, we lazily submit half of that estimate as our second estimate (with a much lower salience score). But if our single estimate was actually half of the “true” value, as is plausible for fast music, we would see better scores from submitting double instead of half as the second estimate.

Results are here and here.

Audio Key Detection

Some novelty here from a pair of template-based methods from the Universitat Autonoma de Barcelona, one attributed to Galin and Castells-Rufas and the other to Castells-Rufas and Galin. Their performance is not a million miles away from our own template-based key estimation plugin.

The strongest results appear to be from a neural network method from Korzeniowski et al at JKU, an updated version of one of last year’s better-performing submissions, an implementation of which can be found in the madmom library.

Results are here.

Audio Chord Estimation

A lively (or daunting) category. A team from Fudan University in Shanghai, whence came two of the previous year’s strongest submissions, is back with another new method, an even stronger set of results, and once again a very readable abstract; and the JKU team have an updated model, just as in the key detection category, which also performs extremely impressively. Meanwhile a separate submission from JKU, due to Stefan Gasser and Franz Strasser, would have been at the very top had it been submitted a year earlier, but is now a little way behind. Convolutional neural networks are involved in all of these.

Our Chordino submission can still be described as creditable. Results can be found here.