Also, there’s a Firefox issue that I will fix in the coming days.
The Autocanonizer. To make things at little easier to see, lets focus in on a single section. This is because the loudness scale is a logarithmic scale. The algorithm is not perfect. I did four things: Reveal more about the song structure, show the song sections, show, the canon graph and animate the transitions.You can see the steady build in volume over the course of the song. Note that breaking a song down into sections is still an open research problem – there’s no perfect algorithm for it yet, but The Echo Nest algorithm is a state-of-the-art algorithm that is probably as good as it gets. It uses The Echo Nest API to analyze a track and break them down into bars, beats, tatums, and then uses this data to create an auto-canon of the song.
Second, we get good behavior even for sections that have no strong complimentary section.
This visualizing shows the corresponding canon beat for each beat in the main stream.This has a number of good properties. Thus for each main stream section we find the most similar matching stream of beats for the canon stream.
:)Welcome to Music Machinery - the blog about the interface of music and technology written by Paul Lamere. The challenge, then, is to simply pick the best sequence for each section. Note, in the above visualization that the similar beats to section 3 span across section 5 and 6. We can make a simplifying rule that we will select a single sequence of beats for the canon stream to align with each section.
Search for a track: Search . See if you can tell which of the following songs is ‘A day in the life’ by The Beatles and which one is ‘Hey Jude’ by The Beatles.All in all I think the visualization is pretty neat now compared to where it was after the hack.
The following visualization shows the similar beat graph for a single section (section 3) in the song:You can see bundles of edges leaving section 3 bound for section 5 and 6. The Autocanonizer. We could use these bundles to find most similar sections and simply overlap these sections. Let’s look at each of these 3 functions:The first thing we can try is to make each beat in the canon stream be the most similar sounding beat to the corresponding beat in the main stream. Search for a track: Search . The Autocanonizer is a web app that turns any song into a canon.. The It showed the beats lined up in a row, colored by the timbral data. For instance, the second section is mostly self-similar, so this approach aligns the section with a copy of itself offset by a few beats leading to a very nice call and answer.That’s the core mechanism of the autocanonizer – for each section in the song, find the most commonly occurring distance to a sibling beat, and then generate the canon stream by assembling beats using that most commonly occurring distance. I know how to fix it, but it requires some subtle math (subtraction) that I’m sure I’ll mess up right before the hackathon demo if I attempt it now, so it will have to wait for another day.
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