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GSoC 2021: Beat detection testing

Algorithmic beat detection

The first thing I need to implement music-syncing functionality in Pitivi is a piece of software capable of analysing a given audio track and returning some kind of information about its beat timing.

What exactly are we looking for? There’s two possible ways that came to my mind:

  • getting the BPM of the whole song plus the offset of the first beat, if the song has constant tempo - or an array of all detected sections with their BPMs and offsets, if the tempo changes throughout the song
  • detecting each ‘beat’ (usually where a full note in 4/4 signature begins) separately and getting a list of where they are placed throughout the song

The first option would, in theory, be the better one - we could then calculate the timing of each beat very precisely, making it possible to account for different time signatures and beat divisors needed to properly time our edits to the song.

In practice, none of the libraries I tested worked like this. The reason is simple - an algorithm precise and confident enough doesn’t really exist. While constant-tempo songs usually aren’t a problem, the moment the BPM changes for even a few beats, everything gets way harder. And, sometimes, it doesn’t have to really change at all.

From now on I’m going to focus on librosa, as it yielded the best results in my initial testing.

The constant tempo inconsistency

One thing, which I’ve discovered early on, is that librosa tends to be a bit overaggresive when detecting possible tempo changes - even if, in reality, there aren’t any.

It makes sense, since this behaviour allows the algorithm to detect real BPM changes if they actually happen, but has unpleasant side effects if the tempo just stays constant - see this example of ‘Marry You’ by Bruno Mars:

The audible clicks you hear, represented by circles on the timeline, indicate where librosa detected a beat in this song - I’ll talk more about that later on.

As you can notice, between beats 46 and 52 - when he sings ‘hey baby!’ - the algorithm can’t seem to decide whether it’s the vocals or the drums that are leading the beat. It tries to stay kinda inbetween, introducing a weird sounding beat skip.

Even though it quickly recovers (and stays consistent for the rest of the song), those very short moments of uncertainty happen often enough to throw possible section-wide BPM detection off the table, since we aren’t able to detect tempo precisely enough, even when it’s constant.

Visualizing the results

After analysing the song, librosa gives us the exact timestamps of detected beats (in seconds). I decided to use the editor of a popular rhythm game - osu! - to visualize the results on a timeline containing a waveform of the song, lines indicating where beats / half-beats should land (precisely timed by a human against a metronome), and circles representing where beats were detected by the algorithm, which make an audible click when passed on the timeline.

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an example visualisation of detected beats. the BPM shown in the upper part is the result of manual timing mentioned above, and the labels on the bottom can be ignored (leftovers of the original beatmap)

It was as simple as converting a list of the timestamps given by librosa into the format osu! expects and pasting it into a beatmap made and timed by a community member.

It is worth noting that the algorithm placed the beats ever-so-slightly late compared to where they should be, which coupled with the OS-added audio delay made them sound way too off-sync. The delay was constant, though, and applying an approx. -45ms offset placed them almost perfectly on time in most cases.

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original beat timestamps
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timestamps after applying offset

Detecting beats in songs of various genres and rhythm complexity

With all of that in mind, it’s time to test how beat detection works in various kinds of music.

Overall, in most cases it’s gonna be used for (constant-tempo songs with simple rhythm) it performs quite well, detecting beats on time - save for some really calm sections, where it just doesn’t have enough information to properly judge the rhythm. It also handles more advanced cases with… varying results. Here’s a couple examples:

Taylor Swift - Stay Stay Stay

A rather calm country pop song with constant BPM and a few calmer sections.

The rhythm is detected correctly during the intro, but the algorithm loses track right before the main beat begins and then ends up detecting beats on half-notes (red ticks), instead of full notes. As you can hear it makes sense rhythmically though, so no big deal.

In a later, calmer section of the song, it lets the vocals steer the detection for a few seconds, resulting in a few off-beat detections. Quickly recovers, this time correctly ending up on white lines (full notes). Otherwise, it stays consistent throughout the song, giving satisfying results.

One Republic - Counting Stars

A bit less calm pop song with a catchy beat. The main tricky section there is the intro, where there’s only vocals and a calm guitar background before the main beat starts. Let’s take a closer look at the first 30 seconds:

The algorithm starts with a slight delay and seems to mainly focus on the vocals, which yields mixed results - sometimes it lands on the beat, sometimes not really.

If we were to sync a video to the detected points in time, it would still be kind of in rhythm. In some cases, the vocal focus might even be preferable in calmer sections like this one, so overall - not a bad result.

During the rest of the song, beats are detected well and only slightly drift off-sync during the slow section, again letting the vocals take the lead for a short while. More than good enough for our use-case.

Bruno Mars - Marry You

Aside from the short hiccup mentioned earlier in the post, beat detection works perfectly fine throughout the whole song - here’s an example:

Stephen - Crossfire

This is an interesting one - it has short (one / two measures) pauses in the background rhythm throughout almost the whole song, as well as a few mostly-vocal sections and a sudden, energetic ending. Tempo remains constant, though.

The beginning is tricky - it doesn’t have much audio information to work with. The algorithm does quite well there, only drifting seriously off-beat once and quickly recovering as soon as a more solid beat appears.

Chorus looks much easier to handle, but still, the short pauses could throw librosa off. It handles those quite nicely, though, only sometimes offsetting a beat or two by a small, acceptable amount of time.

This slow section is probably the hardest one - there’s barely any audible ‘beat’ there, so the algorithm understandably has a hard time detecting it. However, all it needs to recover is the vocal line - as soon as it appears, beats start being detected correctly throughout the rest of the slow section and into the final, energetic chorus. Looks pretty impressive to me.

Rita - dorchadas

A relaxing song with an energetic chorus - the first one here with a variable tempo as well as a time signature different than 4/4.

Starting with the beginning of the song, the beat detection works fine, but might sound a little weird. As you might have noticed, beats alternate between full (white) and half (red) notes - that’s because this section has a time signature of 6/4, which the algorithm doesn’t take into account - detecting it would be a whole another story.

The first tempo change - 188BPM 6/4 into 120BPM 4/4 - is handled pretty much perfectly here.

Second tempo change - back into 188BPM 6/4 - also goes without issues. Overall, librosa handles this song really well.

MiddleIsland - Rose

Time for a real challenge - a non-percussive piano song that’s not timed to a metronome.

The BPM values visible above the timeline are a result of timing the song by a human, close to the actual tempo enough to be playable in a rhythm game.

For me, this is a really good result considering the constantly changing BPM.

A more vivid part of the song is also handled really well. Overall, the results are quite impressive to me - I honestly thought librosa was going to fail entirely before tested it on this song. Of course, it’s not perfect, but is easily good enough for us and our use-case.

Lady Gaga & Bradley Cooper - Shallow

Another non-constant tempo song, with piano + guitar background for the most of the song - only the final chorus has drums in it.

The intro is only vocals + guitar, but that looks like enough to detect rhythm in this case. Goes visibly off-beat during the short pauses, but that’s entirely understandable.

This part, where the piano starts playing, is definitely not ideal - the detection has issues with quieter moments and when vocal audibly overwhelms the underlying piano. Still, the result is acceptable for a non-metronome song.

The final buildup, on the other hand, isn’t an issue at all - it’s not a surprise, though, considering that drums make an appearance there.

Demetori - Mukau no Sato ~ Deep Mountain

A relatively calm, instrumental song performed by a Japanese metal band.

This song is 168BPM 4/4, but the algorithm detected beats as if they’ve had a different time signature - they don’t all land on the same kind of notes. It stays in sync with the song though, so that shouldn’t be a big deal, but sounds a little weird when directly overlayed onto the song.

Demetori - Seijouki no Pierrot ~ The MadPiero Laughs

A much more energetic, instrumental metal song. Strong drumline throughout the entire song, but the tempo changes a little in certain moments.

No issues whatsoever here, the slight BPM changes were handled perfectly.

Kobaryo - Bookmaker

Finally, time for some fast and loud tech / speedcore tracks.

Despite the constant BPM, the ‘noisiness’ of the song seems to confuse the algorithm in certain moments. It snaps to 1/2 notes during the slow section, correctly goes back to 1/1 when the buildup starts, only to be thrown onto 1/2 ticks by the same sound seconds after. Similar thing happens in the chorus later on.

sabi - true DJ MAG top ranker’s song Zenpen (katagiri Remix)

This is definitely the most noisy / rhythmically complex song here, despite having a constant 170 BPM. Volume warning I guess!

Once again, we see librosa doing pretty well, aside from the short moment of confusion, resulting in a jump onto 1/2 notes. It unfortunately stays that way until the chorus around 30 seconds later:

As you can hear, while 1/2 notes are perfectly where they are supposed to, they’re not exactly the beats we want to track - 1/1 notes, indicated by white lines, are much better moments to potentially sync an edit to.

Afterwards, it properly stays on 1/1 notes for a longer while, until this section:

Here the algorithm also jumps between white and red lines, this time understandably so, because of the irregularity of the beat and vocal samples. Seems to go back to 1/1 notes after this section and stays on them until the end of the song.


From what we’re seeing in the results above, I think librosa will be a good choice to begin working on the beat-synced video editing functionality in Pitivi.

It’s not ideal, but in most cases it’ll be well within the ‘good enough’ range of results for people looking to quickly synchronise their edits to a catchy pop song, for example.

Thank you for reading this (rather lengthy) post and I hope it was useful in showcasing the possibilites of a modern beat-tracking library like librosa. See you next time!