Gai Barone — Patterns 686
Progressive · 1h 22m · analysed 23 Aug 2026
About this progressive set
Patterns 686 is a progressive DJ set by Gai Barone, running 1h 22m. It was measured by the SOONOS multi-agent engine and scores 77/100 overall — solid.
The engine files this performance as “Promising”.
This DJ excels at maintaining a relentless groove, making them a reliable choice for a peak-time techno set.
How the score breaks down
SOONOS scores this set across 5 weighted categories. The strongest is IDENTITY at 86/100; the weakest is SURPRISE at 76/100.
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 24 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 976.8 grid breaks per hour, with a median beat offset of 25 ms.
Full SOONOS analysis
This DJ excels at maintaining a relentless groove, making them a reliable choice for a peak-time techno set.
The 8 dimensions
Score breakdown
TECHNIQUE
How well did you execute?
25%84
TECHNIQUE
How well did you execute?
STORYTELLING
Did the mix go somewhere?
25%81
STORYTELLING
Did the mix go somewhere?
ENERGY
Did you control the room?
20%79
ENERGY
Did you control the room?
SURPRISE
Did you remain unpredictable?
15%76
SURPRISE
Did you remain unpredictable?
IDENTITY
Did the mix sound like you?
15%86
IDENTITY
Did the mix sound like you?
Final score capped by measured execution (-) — weighted average was 81.
Energy arc
Transitions analysed (5)
Long EQ blend
Seamless introduction of the rhythmic section.
Percussive layering
Smooth transition into a more complex rhythmic structure.
Long EQ blend with gradual low-end swap
Masterful control of the crossover point; the new kick emerges without any phase cancellation.
Mid-frequency layering and swap
Smooth transition of the rhythmic lead elements, maintaining the hypnotic flow.
Long EQ blend with low-end swap
Seamless integration of the incoming kick, maintaining the groove perfectly.
Technical subscores
86
EQ balance
92
Beat alignment
82
Energy control
94
Tempo stability
84
Loudness control
89
Musical coherence
85
Harmonic compatibility
89
Transition cleanliness
Measured (DSP)
24.7 ms
Beat-grid drift (median)
258.9 ms
Drift p95
4,932
Boundaries detected
19
Hard cuts / h
82
Clipping events
95
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 (47:00, 51:00, 55:00, 59:00, 71:00, 75:00). Long loops kill dancefloor momentum.
Timeline
- 1:15Vocal Hook
- 6:45Kick Entry
- 17:30Groove Lock Peak
- 22:15Energy Plateau
- 29:15Groove Lock Peak
Coaching notes
- Introduce a subtle melodic variation around the 15-minute mark to maintain interest.
- Experiment with brief, high-pass filter cuts to create small moments of tension release.
- Consider layering a field recording or ambient texture to further enhance the 'Patterns' concept.
- Experiment with more dramatic filter sweeps to build tension.
- Try incorporating some classic techno elements for a touch of nostalgia.
- Focus on creating more distinct 'chapters' within the set to enhance the narrative.
- Use more FX like delay and reverb to add depth to the percussive layers.
Curious how your own set scores?
Questions about this analysis
- What score does Gai Barone's Patterns 686 get?
- Patterns 686 scores 77/100 in the SOONOS Mix Database (solid), based on audio measurement plus multi-agent listening.
- How long is Patterns 686?
- The analysed recording runs 1h 22m.
- Which part of the mix scores best?
- IDENTITY is the strongest category at 86/100, while SURPRISE is the lowest at 76/100.
- 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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