Clara CuvéSPFDJ with Clara Cuvé - 08 October 2019

    Hard Techno · 2h 00m · analysed 22 Aug 2026

    Overall score
    85/100
    Level
    festival ready
    Duration
    2h 00m
    Genre
    Hard Techno

    About this hard techno set

    SPFDJ with Clara Cuvé - 08 October 2019 is a hard techno DJ set by Clara Cuvé, running 2h 00m. It was measured by the SOONOS multi-agent engine and scores 85/100 overall — festival ready.

    The engine files this performance as “Promising”.

    This mix demonstrates how to maintain extreme tension without losing the groove.

    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 81/100.

    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 70 and 95 on the SOONOS energy scale — a moderate dynamic range across the set.

    6 transitions were detected and scored individually.

    Beat-grid measurement (DSP) reports 784.4 grid breaks per hour, with a median beat offset of 20 ms.

    Listen to the original set

    Full SOONOS analysis

    TECSELNRGCRWHRMGRVCRESIG
    0

    This mix demonstrates how to maintain extreme tension without losing the groove.

    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%85
    Beat Alignmentmeasured81
    Transition Qualitymeasured82
    Phrase Accuracymeasured90
    EQ & Frequency Control (estimated)measured85
    Tempo Stability (estimated)measured93
    Technical Cleanlinessmeasured80

    STORYTELLING

    Did the mix go somewhere?

    25%90
    Narrative ArcAI91
    ProgressionAI91
    Tension & ReleaseAI84
    Section AwarenessAI89
    Flow ContinuityAI92
    PayoffAI93

    ENERGY

    Did you control the room?

    20%84
    Energy Curvemeasured82
    PacingAI89
    MomentumAI94
    Peak ManagementAI89
    Energy HandoffAI92
    Fatigue ControlAI48

    SURPRISE

    Did you remain unpredictable?

    15%81
    Unexpected MomentsAI82
    ContrastAI79
    Transition Varietymeasured93
    Selection RiskAI89
    FreshnessAI48
    Surprise ControlAI82

    IDENTITY

    Did the mix sound like you?

    15%93
    Selection QualityAI93
    Identity ConsistencyAI98
    IntentionalityAI94
    ConfidenceAI96
    RestraintAI82
    SignatureAI94

    Final score capped by measured execution (-) — weighted average was 87.

    Energy arc

    12:00120:00

    Transitions analysed (6)

    04:22

    Fast EQ swap on the 1

    Seamless transition between two high-energy percussive tracks.

    90
    08:45

    Long percussive blend

    Effective layering of two drum patterns to increase density.

    85
    13:15

    Long EQ blend

    Smooth transition maintaining the percussive drive.

    88
    19:50

    Drop swap

    High-impact transition that perfectly captures the peak energy.

    92
    25:12

    Fast EQ swap on the downbeat

    Perfectly timed transition that maintains the driving momentum without a dip in energy.

    92
    29:45

    Long percussive blend

    Smooth layering of two tracks, creating a complex rhythmic texture before the swap.

    84

    Technical subscores

    85

    EQ balance

    91

    Beat alignment

    92

    Energy control

    93

    Tempo stability

    87

    Loudness control

    90

    Musical coherence

    79

    Harmonic compatibility

    85

    Transition cleanliness

    Measured (DSP)

    20.3 ms

    Beat-grid drift (median)

    218.6 ms

    Drift p95

    7,184

    Boundaries detected

    35

    Hard cuts / h

    39

    Clipping events

    260

    Level jumps

    Measurement confidence 100% · analysed on the full audio.

    Recurring patterns

    • Recurring mid-frequency clash

      Mid-range buildup muddies the soundstage and washes out vocals/synths during transitions. Detected 2× (08:45, 29:45).

    • Long static loops (6×)

      Sections sitting on the same loop without layering, EQ moves or tension shifts (14:00, 20:00, 30:00, 36:00, 44:00, 72:00). Long loops kill dancefloor momentum.

    Timeline

    • 17:30Energy Plateau
    • 22:15Vocal Peak
    • 27:30Percussive Peak

    Coaching notes

    • Experiment with brief atmospheric breakdowns to create more dynamic range.
    • Incorporate more varied mid-range textures to further distinguish the narrative chapters.
    • Maintain the current level of technical precision; it is a significant strength.
    • Incorporate more dramatic breakdowns to enhance tension.
    • Experiment with subtle melodic elements to add depth.
    • Maintain the current level of technical precision and energy.
    • Experiment with longer, more atmospheric breakdowns to create more dramatic tension/release cycles.
    • Incorporate subtle melodic elements in the final tracks to add emotional depth to the closing sequence.
    • Refine mid-range EQ management during dense layering to ensure every element has its own space.
    Listen on SoundCloud

    Curious how your own set scores?

    Questions about this analysis

    What score does Clara Cuvé's SPFDJ with Clara Cuvé - 08 October 2019 get?
    SPFDJ with Clara Cuvé - 08 October 2019 scores 85/100 in the SOONOS Mix Database (festival ready), based on audio measurement plus multi-agent listening.
    How long is SPFDJ with Clara Cuvé - 08 October 2019?
    The analysed recording runs 2h 00m.
    Which part of the mix scores best?
    IDENTITY is the strongest category at 93/100, while SURPRISE is the lowest at 81/100.
    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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