Gai BaronePatterns 693

    Progressive · 1h 25m · analysed 23 Aug 2026

    Overall score
    79/100
    Level
    club ready
    Duration
    1h 25m
    Genre
    Progressive

    About this progressive set

    Patterns 693 is a progressive DJ set by Gai Barone, running 1h 25m. It was measured by the SOONOS multi-agent engine and scores 79/100 overall — club ready.

    The engine files this performance as “Promising”.

    The DJ understands that in hypnotic techno, the space between the notes is as important as the notes themselves.

    How the score breaks down

    SOONOS scores this set across 5 weighted categories. The strongest is IDENTITY at 88/100; the weakest is SURPRISE at 78/100.

    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 37 points, moving between 20 and 85 on the SOONOS energy scale — a wide dynamic range across the set.

    7 transitions were detected and scored individually.

    Beat-grid measurement (DSP) reports 583.7 grid breaks per hour, with a median beat offset of 25 ms.

    Listen to the original set

    Full SOONOS analysis

    TECSELNRGCRWHRMGRVCRESIG
    0

    The DJ understands that in hypnotic techno, the space between the notes is as important as the notes themselves.

    The 8 dimensions

    Technical precision0
    Track selection0
    Energy arc0
    Crowd control0
    Harmonic depth0
    Groove patience0
    Creativity0
    Signature0

    Score breakdown

    TECHNIQUE

    How well did you execute?

    25%84
    Beat Alignmentmeasured76
    Transition Qualitymeasured80
    Phrase Accuracymeasured89
    EQ & Frequency Control (estimated)measured86
    Tempo Stability (estimated)measured92
    Technical Cleanlinessmeasured85

    STORYTELLING

    Did the mix go somewhere?

    25%84
    Narrative ArcAI87
    ProgressionAI83
    Tension & ReleaseAI79
    Section AwarenessAI84
    Flow ContinuityAI90
    PayoffAI78

    ENERGY

    Did you control the room?

    20%80
    Energy Curvemeasured75
    PacingAI87
    MomentumAI88
    Peak ManagementAI79
    Energy HandoffAI82
    Fatigue ControlAI65

    SURPRISE

    Did you remain unpredictable?

    15%78
    Unexpected MomentsAI75
    ContrastAI79
    Transition Varietymeasured90
    Selection RiskAI80
    FreshnessAI61
    Surprise ControlAI75

    IDENTITY

    Did the mix sound like you?

    15%88
    Selection QualityAI83
    Identity ConsistencyAI95
    IntentionalityAI87
    ConfidenceAI87
    RestraintAI89
    SignatureAI82

    Final score capped by measured execution (-) — weighted average was 83.

    Energy arc

    0:0085:00

    Transitions analysed (7)

    4:22

    Long EQ blend with low-end swap

    The transition from the atmospheric intro to the main beat is handled with extreme patience. The sub-bass of the incoming track is introduced so subtly it feels like a natural evolution of the intro.

    95
    9:15

    Harmonic layering

    A smooth transition between two tracks with very similar percussive signatures. The DJ uses the mid-range to bridge the gap, ensuring the groove never falters.

    90
    11:25

    Perfect Blend

    100
    15:42

    Seamless EQ Swap

    100
    19:40

    Minor Level Drift

    intentional_reset

    82
    22:45

    Long percussive blend with low-end swap

    Masterful layering where the two tracks become a new third entity. Perfect phrase alignment.

    92
    28:30

    EQ-focused transition with high-pass filtering

    Smooth introduction of atmospheric elements. The energy handoff is handled with professional restraint.

    84

    Technical subscores

    86

    EQ balance

    92

    Beat alignment

    84

    Energy control

    92

    Tempo stability

    84

    Loudness control

    89

    Musical coherence

    87

    Harmonic compatibility

    89

    Transition cleanliness

    Measured (DSP)

    24.5 ms

    Beat-grid drift (median)

    239.6 ms

    Drift p95

    5,089

    Boundaries detected

    20

    Hard cuts / h

    21

    Clipping events

    113

    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 (31:00, 53:00, 57:30, 64:00, 70:00, 74:00). Long loops kill dancefloor momentum.

    What SOONOS heard

    • Exceptional patience
    • Technical cleanliness
    • Stylistic focus
    • Technical precision in long blends.
    • Consistent and high-quality track selection.
    • Excellent management of low-end frequencies.

    Timeline

    • 1:15Atmospheric Hook
    • 5:45Groove Lock
    • 24:15Groove Lock

    Coaching notes

    • Maintain the current level of EQ precision; it is your strongest technical asset.
    • Experiment with subtle rhythmic 'glitches' or short loop repeats to add a layer of unpredictability.
    • Explore tracks with slightly more varied mid-range textures to enhance the narrative depth.
    • Introduce a subtle melodic element in the final 5 minutes to provide a sense of closure.
    • Experiment with brief (4-bar) bass cuts to create more dynamic tension/release moments.
    • Incorporate sparse vocal samples to add a human element to the industrial textures.
    Listen on SoundCloud

    Curious how your own set scores?

    Questions about this analysis

    What score does Gai Barone's Patterns 693 get?
    Patterns 693 scores 79/100 in the SOONOS Mix Database (club ready), based on audio measurement plus multi-agent listening.
    How long is Patterns 693?
    The analysed recording runs 1h 25m.
    Which part of the mix scores best?
    IDENTITY is the strongest category at 88/100, while SURPRISE is the lowest at 78/100.
    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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