Joel Mull — Smoke Machine Podcast 130 Joel Mull
Peak Techno · 2h 07m · analysed 23 Aug 2026
About this peak techno set
Smoke Machine Podcast 130 Joel Mull is a peak techno DJ set by Joel Mull, running 2h 07m. It was measured by the SOONOS multi-agent engine and scores 85/100 overall — festival ready.
The engine files this performance as “Promising”.
Joel Mull proves that the most powerful transitions are the ones you never hear happening.
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 83/100.
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 20 and 88 on the SOONOS energy scale — a wide dynamic range across the set.
5 transitions were detected and scored individually.
Beat-grid measurement (DSP) reports 1041.1 grid breaks per hour, with a median beat offset of 26 ms.
Full SOONOS analysis
Joel Mull proves that the most powerful transitions are the ones you never hear happening.
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%86
ENERGY
Did you control the room?
SURPRISE
Did you remain unpredictable?
15%83
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 low-end handover
A masterfully executed transition that brings the first rhythmic element into the mix without breaking the atmospheric spell.
Long EQ blend with sub-bass handover
Perfectly executed transition; the two tracks become one for over 64 bars.
Textural layering and high-pass fade
Smooth transition focusing on the mid-range energy shift.
Long EQ blend with low-end swap
Perfectly executed handover of the sub-frequencies, maintaining the hypnotic drive.
32-bar percussive layering
Seamless integration of new rhythmic elements without disrupting the established groove.
Technical subscores
90
EQ balance
95
Beat alignment
89
Energy control
94
Tempo stability
89
Loudness control
92
Musical coherence
89
Harmonic compatibility
92
Transition cleanliness
Measured (DSP)
25.6 ms
Beat-grid drift (median)
203.1 ms
Drift p95
7,605
Boundaries detected
25
Hard cuts / h
61
Clipping events
336
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 (9:00, 14:30, 20:00, 25:00, 31:00, 46:00). Long loops kill dancefloor momentum.
What SOONOS heard
- Technical transparency in transitions.
- Exceptional groove continuity.
- Mature pacing and restraint.
- Technical transparency in mixing.
- Exceptional groove continuity.
- Professional restraint and pacing.
Timeline
- 15:45Groove Lock Mastery
- 21:20Atmospheric Peak
- 27:45Groove Lock Peak
Coaching notes
- Experiment with 'vacuum' filter drops to create artificial tension.
- Layer in more organic field recordings to contrast the industrial tones.
- Maintain the current blend lengths; they are a significant technical strength.
Curious how your own set scores?
Questions about this analysis
- What score does Joel Mull's Smoke Machine Podcast 130 Joel Mull get?
- Smoke Machine Podcast 130 Joel Mull scores 85/100 in the SOONOS Mix Database (festival ready), based on audio measurement plus multi-agent listening.
- How long is Smoke Machine Podcast 130 Joel Mull?
- The analysed recording runs 2h 07m.
- Which part of the mix scores best?
- IDENTITY is the strongest category at 93/100, while SURPRISE is the lowest at 83/100.
- 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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