Nina Kraviz — @ dommune tokyo 2013
Experimental Techno · 1h 48m · analysed 18 Aug 2026
About this experimental techno set
@ dommune tokyo 2013 is a experimental techno DJ set by Nina Kraviz, running 1h 48m. It was measured by the SOONOS multi-agent engine and scores 86/100 overall — festival ready.
The engine files this performance as “Promising”.
How the score breaks down
SOONOS scores this set across 5 weighted categories. The strongest is IDENTITY at 93/100; the weakest is SURPRISE at 82/100.
The final score is capped by measured -: the DSP layer overrides the listening layer whenever it measures an execution issue directly in the audio.
Analysis coverage for this file is 100% — the share of the set the engine could measure with full confidence.
Energy and structure
The energy curve was sampled at 38 points, moving between 65 and 95 on the SOONOS energy scale — a moderate dynamic range across the set.
5 transitions were detected and scored individually.
Beat-grid measurement (DSP) reports 722.2 grid breaks per hour, with a median beat offset of 20 ms.
Full SOONOS analysis
The 8 dimensions
Score breakdown
TECHNIQUE
How well did you execute?
25%85
TECHNIQUE
How well did you execute?
STORYTELLING
Did the mix go somewhere?
25%90
STORYTELLING
Did the mix go somewhere?
ENERGY
Did you control the room?
20%85
ENERGY
Did you control the room?
SURPRISE
Did you remain unpredictable?
15%82
SURPRISE
Did you remain unpredictable?
IDENTITY
Did the mix sound like you?
15%93
IDENTITY
Did the mix sound like you?
Final score capped by measured execution (-) — weighted average was 87.
Energy arc
Transitions analysed (5)
Long EQ blend with high-pass filtering
A textbook hypnotic techno blend; the incoming track emerges from the shadows perfectly.
Mid-frequency swap on the phrase
Effective transition that maintains energy, though the incoming track's mids were slightly prominent.
Fast percussive blend
Perfectly timed entry of new rhythmic elements.
EQ-led transition
Smooth handover of the low-end frequencies.
Long EQ blend with low-end swap
Masterful handover of the sub-frequencies, maintaining perfect groove continuity.
Technical subscores
87
EQ balance
91
Beat alignment
88
Energy control
92
Tempo stability
87
Loudness control
92
Musical coherence
85
Harmonic compatibility
89
Transition cleanliness
Measured (DSP)
20.3 ms
Beat-grid drift (median)
218.5 ms
Drift p95
6,476
Boundaries detected
41
Hard cuts / h
12
Clipping events
346
Level jumps
Measurement confidence 100% · analysed on the full audio.
Recurring patterns
Long static loops (6×)
Sections sitting on the same loop without layering, EQ moves or tension shifts (0:00, 20:00, 26:00, 30:00, 36:00, 42:00). Long loops kill dancefloor momentum.
Timeline
- 04:30Texture Shift
- 08:15Synth Introduction
- 18:30Rhythmic Peak
- 21:15Groove Shift
- 30:15Groove Lock
Coaching notes
- Experiment with longer filter sweeps to build even more tension.
- Introduce a subtle melodic element to add emotional depth.
- Ensure the master output remains clean during peak layering moments.
- Experiment with brief moments of silence or extreme filtering to create more dramatic tension resets.
- Introduce a subtle melodic element in the final third to provide an emotional payoff.
- Maintain this level of technical precision; it is your strongest asset.
- Continue exploring long-form layering to create unique sonic textures.
- Experiment with brief atmospheric breakdowns to provide contrast to the driving rhythm.
- Maintain the high level of EQ precision shown in this segment.
Curious how your own set scores?
Questions about this analysis
- What score does Nina Kraviz's @ dommune tokyo 2013 get?
- @ dommune tokyo 2013 scores 86/100 in the SOONOS Mix Database (festival ready), based on audio measurement plus multi-agent listening.
- How long is @ dommune tokyo 2013?
- The analysed recording runs 1h 48m.
- Which part of the mix scores best?
- IDENTITY is the strongest category at 93/100, while SURPRISE is the lowest at 82/100.
- How is this score calculated?
- SOONOS measures the audio with a DSP pass (beat grid, loudness, tempo, energy) and runs a multi-agent listening pass on top. Measured axes always override the listening layer, so an execution issue heard in the file caps the final score.
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