Gai BaronePatterns 633

    Progressive · 1h 27m · analysed 23 Aug 2026

    Overall score
    77/100
    Level
    solid
    Duration
    1h 27m
    Genre
    Progressive

    About this progressive set

    Patterns 633 is a progressive DJ set by Gai Barone, running 1h 27m. It was measured by the SOONOS multi-agent engine and scores 77/100 overall — solid.

    The engine files this performance as “Promising”.

    This DJ understands that in 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 86/100; the weakest is SURPRISE at 74/100.

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

    6 transitions were detected and scored individually.

    Beat-grid measurement (DSP) reports 988.2 grid breaks per hour, with a median beat offset of 25 ms.

    Listen to the original set

    Full SOONOS analysis

    TECSELNRGCRWHRMGRVCRESIG
    0

    This DJ understands that in 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 Alignmentmeasured75
    Transition Qualitymeasured79
    Phrase Accuracymeasured88
    EQ & Frequency Control (estimated)measured86
    Tempo Stability (estimated)measured92
    Technical Cleanlinessmeasured85

    STORYTELLING

    Did the mix go somewhere?

    25%82
    Narrative ArcAI85
    ProgressionAI81
    Tension & ReleaseAI76
    Section AwarenessAI82
    Flow ContinuityAI88
    PayoffAI77

    ENERGY

    Did you control the room?

    20%76
    Energy Curvemeasured73
    PacingAI83
    MomentumAI87
    Peak ManagementAI78
    Energy HandoffAI81
    Fatigue ControlAI36

    SURPRISE

    Did you remain unpredictable?

    15%74
    Unexpected MomentsAI74
    ContrastAI74
    Transition Varietymeasured88
    Selection RiskAI75
    FreshnessAI40
    Surprise ControlAI74

    IDENTITY

    Did the mix sound like you?

    15%86
    Selection QualityAI83
    Identity ConsistencyAI93
    IntentionalityAI83
    ConfidenceAI86
    RestraintAI86
    SignatureAI79

    Final score capped by measured execution (-) — weighted average was 80.

    Energy arc

    0:0087:00

    Transitions analysed (6)

    4:12

    Long-form spectral blend

    A masterclass in patience. The transition is almost imperceptible as the low-end of the new track replaces the old one over 64 bars.

    95
    9:45

    EQ-based percussive swap

    Clean handover of the high-hat patterns. The groove remains perfectly stable.

    88
    12:15

    Long EQ blend with low-end swap

    Perfectly executed transition that maintains the hypnotic momentum.

    92
    17:22

    Mid-frequency layering

    Smooth introduction of new percussive elements without cluttering the mix.

    88
    23:12

    Long EQ blend with low-end swap

    Seamless transition that maintains the hypnotic flow perfectly.

    90
    29:45

    Percussive layering and mid-range swap

    Smooth integration of new rhythmic elements, building energy effectively.

    85

    Technical subscores

    86

    EQ balance

    90

    Beat alignment

    82

    Energy control

    92

    Tempo stability

    85

    Loudness control

    84

    Musical coherence

    85

    Harmonic compatibility

    88

    Transition cleanliness

    Measured (DSP)

    24.8 ms

    Beat-grid drift (median)

    257.8 ms

    Drift p95

    5,203

    Boundaries detected

    12

    Hard cuts / h

    58

    Clipping events

    85

    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 (18:45, 24:00, 30:00, 33:00, 38:00, 54:00). Long loops kill dancefloor momentum.

    What SOONOS heard

    • Technical precision in long blends
    • Exceptional groove continuity
    • Mature restraint in energy management

    Timeline

    • 0:45Atmospheric Hook
    • 6:15Groove Lock-in
    • 14:50Groove Lock Peak
    • 19:10Subtle Energy Sag
    • 25:30Groove Lock
    • 30:15Tension Peak

    Coaching notes

    • Introduce more frequent, subtle filter movements to keep the long plateaus dynamic.
    • Experiment with brief rhythmic silences (1-2 beats) to reset the listener's ear.
    • Incorporate more distinct melodic motifs to enhance the narrative arc.
    • Introduce a subtle melodic element to break the percussive monotony.
    • Experiment with brief rhythmic silences to reset the dancefloor's attention.
    • Use a wider stereo field for atmospheric elements to increase the sense of space.
    • Experiment with subtle melodic layering in the final track to add emotional depth.
    • Use a wider stereo image for atmospheric elements during the final 5 minutes.
    • Practice using short, sharp high-pass filter cuts to create micro-tensions within the long blends.
    Listen on SoundCloud

    Curious how your own set scores?

    Questions about this analysis

    What score does Gai Barone's Patterns 633 get?
    Patterns 633 scores 77/100 in the SOONOS Mix Database (solid), based on audio measurement plus multi-agent listening.
    How long is Patterns 633?
    The analysed recording runs 1h 27m.
    Which part of the mix scores best?
    IDENTITY is the strongest category at 86/100, while SURPRISE is the lowest at 74/100.
    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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