The 1001Tracklists Dataset

We obtained a collection of DJ mix metadata via direct personal communication with 1001Tracklists. Each entry of mixes contains a list of track, boundary timestamps and genre. It also contains web links to the audio files of the mixes and tracks. We downloaded the audio files separately from the linked media service websites on our own.

Summary Statistics

The table below summarizes statistics of the dataset. The original size of the dataset is denoted as ‘All’ and the size after filtering as ‘Matched’. Note that the number of played tracks is greater than the number of unique tracks as a track can be played in multiple mixes.

Summary statistic All Matched
The number of mixes 1,564 1,557
The number of unique tracks 15,068 13,728
The number of played tracks 26,776 24,202
The number of transitions 24,344 20,765
Total length of mixes (in hours) 1,577 1,570
Total length of unique tracks (in hours) 1,038 913
Average length of mixes (in minutes) 60.5 60.5
Average length of unique tracks (in minutes) 4.1 4.0
Average number of played tracks in a mix 17.1 15.5
Average number of transitions in a mix 14.5 12.9

Genre Distribution

The dataset includes a variety of genres but mostly focuses on House and Trance music as shown below.

Mix Genre Distribution

mix genre counts

Track Genre Distribution

track genre counts

Mix-To-Track Subsequence Alignment

The objective of mix-to-track subsequence alignment is to find an optimal alignment path between a subsequence of a mix and a track used in the mix. We compute the alignment by applying subsequence DTW to beat synchronous features such as MFCC and chroma features.

Three examples below visualize the DTW-based mix-to-track subsequence alignment between a mix and the original tracks played in that mix. The colored solid lines show the warping paths of the alignment. Since we use beat synchronous representations for them, the warping paths become diagonal with a slope of one if a mix and a track are successfully aligned.

You can listen and see individual tracks used in the mixes if you go to the tracklist links.

The example below shows an successfully aligned example for the most of tracks and features where all warping paths have straight diagonal paths.

Alexander Popov · Interplay Radioshow 250 (01-07-19)

Well aligned example

This example is failing because sounds from crowds are also recorded in the mix. You can hear crowds sreaming if you listen to the mix below.

Badly aligned example

Since the proposed alignment method is key transposition invariant, the method works even though a DJ changes the key of tracks. This is an example which the key-invariant method distinctively works better than others as the DJ frequently uses key transposition on the mix. You can hear that some tracks are key changed if you go to the tracklist link and listen to the mix and the individual tracks!

Max Vangeli · Max Vangeli Presents: NoFace Radio - Episode 046

Key changed example

Cue Point Extraction

If you look closer to the warping paths from the subsequence DTW, you can also extract cue points which indicate where tracks start/end in mixes. The figure below is a zoomed-in view of a visualization of mix-to-track subsequence alignment explaining the three types of extracted cue points. The two alignment paths drift from the diagonal lines in the transition region (between 2310 and 2324 in mix beat) because the two tracks cross-fades. Based on this observation, we detect the cue-out/-in point of the previous/next track by finding the last/first beat where preceding/succeeding 32 beats have diagonal moves in the alignment path.

Musicological Analysis of DJ Mixes

We hypothesize that DJs share common practices in the creative process. Here, we validate the hypotheses mentioned in the summary above using the results from the mix-to-track subsequence alignment and the cue point extraction.

Tempo Adjustment Analysis

The figure below shows a histogram of percentage differences of tempo between the original track and the audio segment in the mix. 86.1% of the tempo are adjusted less than 5%, 94.5% are less than 10%, and 98.6% are less than 20%.

Key Change Analysis

The figure below shows a histogram of key change between the original track and the corresponding audio segment in the mix. Only 2.5% among the total 24,202 tracks are transposed and, among those transposed tracks, 94.3% of them are only one semitone transposed. This result indicates that DJs generally do not perform key transposition much and leave the “master tempo” function on DJ systems turned on in most cases.

Transition Length Analysis

The figure below shows a histogram of transition lengths in the number of beats. We annotated the dotted lines every 32 beat which is often considered as a phrase in the context of dance music. The histogram has peaks at every phrases. This indicates that DJs consider the repetitive structures in the dominant genres of music when they make transitions or set cue points.

Cue Point Agreement Among DJs

We collected all extracted cue points for each track and computed the statistics of deviations in cue-in points and cue-out points among DJs. From the results, 23.6% of the total cue point pairs have zero deviation. 40.4% of them were within one measure (4 beats), 73.6% were within 8 measures and 86.2% were within 16 measures. This indicates that there are some rules that DJs share in deciding the cue points.

You Can Do It Too! [GitHub Repo Link]

We published the code for mix-to-track subsequence alignment, cue point extraction, DTW visualization and the tempo analysis. The code uses the cool mix below by Palms Trax as an input data.

And you will get the visualization below after running the code! Palms Trax DTW Viz

A Preview of Our Next Move

We are working on more detailed explanations about DJ mixing techniques! The figure below is extracted EQ curves explaining how a DJ controlled EQ knobs on a DJ system. Please stay tuned for our next paper! More fun is coming 💕 EQ