wAFF — NATURECAST 003: Lorenzo De Blanck

    House · 60 min · analysed 25 Aug 2026 · source: soundcloud.com

    Overall score
    75/100
    Level
    solid
    Duration
    60 min
    Genre
    House

    Public mix analysis — no affiliation or endorsement. This report measures a publicly available recording hosted on soundcloud.com. The scores are produced by SOONOS and do not imply any partnership with, or approval by, wAFF.

    In short

    NATURECAST 003: Lorenzo De Blanck by wAFF scores 75/100 in the SOONOS analysis (solid). The strongest dimension is IDENTITY (83/100) and the weakest is SURPRISE (70/100).

    Lorenzo De Blanck's strength lies in his ability to create a seamless, hypnotic flow that feels both effortless and highly controlled.

    Key takeaways

    What weakened the set

    • Long static loops (6×) — Sections sitting on the same loop without layering, EQ moves or tension shifts (0:00, 10:00, 36:00, 43:00, 50:00, 58:00). Long loops kill dancefloor momentum.

    What to try next

    • Continue to prioritize long, smooth blends to maintain the hypnotic flow.
    • Introduce a few more 'peak' moments to provide clear energy releases.
    • Experiment with more diverse mid-range elements to add texture to the mix.

    Timestamped insights

    Each moment below is tagged with how it was produced: “measured” comes from the DSP pass on the audio, “listening” comes from the multi-agent listening pass.

    • 03:52Measured (DSP)90/100Listen at 03:52

      A textbook tech-house transition. The incoming track's percussion is introduced subtly, and the low-end swap is perfectly timed on the phrase.

    • 07:45Measured (DSP)85/100Listen at 07:45

      Effective use of the mid-range to bridge the two tracks. The vocal snippet from the incoming track adds interest before the full drop.

    • 26:12Measured (DSP)92/100Listen at 26:12

      Excellent phrasing and smooth integration of the incoming percussion loop.

    About this house set

    NATURECAST 003: Lorenzo De Blanck is a house DJ set by wAFF, running 60 min. It was measured by the SOONOS multi-agent engine and scores 75/100 overall — solid.

    The engine files this performance as “Promising”.

    Lorenzo De Blanck's strength lies in his ability to create a seamless, hypnotic flow that feels both effortless and highly controlled.

    How the score breaks down

    SOONOS scores this set across 5 weighted categories. The strongest is IDENTITY at 83/100; the weakest is SURPRISE at 70/100.

    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 19 points, moving between 65 and 85 on the SOONOS energy scale — a moderate dynamic range across the set.

    6 transitions were detected and scored individually.

    Beat-grid measurement (DSP) reports 654.4 grid breaks per hour, with a median beat offset of 18 ms.

    How this score is built

    Measured layer (DSP): the audio file is analysed directly — beat grid, tempo stability, loudness and energy. These values are measurements, not opinions.

    Listening layer: a multi-agent pass reads musical intent — programming, storytelling, coherence and crowd effect. This layer interprets, it does not measure.

    Fusion and ceilings: the two layers are merged, and the measured layer always wins on execution. When the DSP hears an execution problem, it caps the final score even if the listening layer is positive.

    Measured coverage for this file is 100% of the set.

    Listen to the original set

    Full SOONOS analysis

    0

    Lorenzo De Blanck's strength lies in his ability to create a seamless, hypnotic flow that feels both effortless and highly controlled.

    The 8 dimensions

    Not available — the 8-dimension profile was not measured for this set.

    Score breakdown

    TECHNIQUE

    How well did you execute?

    25%81
    Beat Alignmentmeasured84
    Transition Qualitymeasured57
    Phrase Accuracymeasured88
    EQ & Frequency Control (estimated)measured87
    Tempo Stability (estimated)measured91
    Technical Cleanlinessmeasured85

    STORYTELLING

    Did the mix go somewhere?

    25%79
    Narrative ArcAI83
    ProgressionAI78
    Tension & ReleaseAI72
    Section AwarenessAI79
    Flow ContinuityAI86
    PayoffAI79

    ENERGY

    Did you control the room?

    20%73
    Energy Curvemeasured68
    PacingAI78
    MomentumAI87
    Peak ManagementAI76
    Energy HandoffAI79
    Fatigue ControlAI34

    SURPRISE

    Did you remain unpredictable?

    15%70
    Unexpected MomentsAI72
    ContrastAI71
    Transition Varietymeasured87
    Selection RiskAI62
    FreshnessAI37
    Surprise ControlAI72

    IDENTITY

    Did the mix sound like you?

    15%83
    Selection QualityAI79
    Identity ConsistencyAI93
    IntentionalityAI81
    ConfidenceAI85
    RestraintAI78
    SignatureAI74

    Energy arc

    00:0060:00

    Transitions analysed (6)

    03:52

    Long EQ blend with high-pass filtering

    A textbook tech-house transition. The incoming track's percussion is introduced subtly, and the low-end swap is perfectly timed on the phrase.

    90
    07:45

    Mid-range swap with vocal tease

    Effective use of the mid-range to bridge the two tracks. The vocal snippet from the incoming track adds interest before the full drop.

    85
    13:20

    Perfect Blend

    100
    18:40

    Minor EQ Overlap

    82
    23:00

    Clean Phrase Swap

    100
    26:12

    Long EQ blend

    Excellent phrasing and smooth integration of the incoming percussion loop.

    92

    Technical subscores

    87

    EQ balance

    92

    Beat alignment

    81

    Energy control

    91

    Tempo stability

    84

    Loudness control

    84

    Musical coherence

    84

    Harmonic compatibility

    88

    Transition cleanliness

    Measured (DSP)

    18 ms

    Beat-grid drift (median)

    Typical gap between the two tracks' beats while they play together. Lower is tighter.

    223 ms

    Drift p95

    Worst-case drift: 95% of the set stays tighter than this value.

    3,555

    Boundaries detected

    Points where the audio changes track or section.

    170

    Hard cuts / h

    Abrupt track changes per hour, with no blend between the two records.

    973

    Clipping events

    Moments where the recording hits the ceiling and distorts.

    195

    Level jumps

    Sudden volume steps between tracks.

    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 (0:00, 10:00, 36:00, 43:00, 50:00, 58:00). Long loops kill dancefloor momentum.

    Timeline

    • 05:15Groove Lock
    • 11:20Energy Plateau
    • 28:15Groove Lock Peak

    Coaching notes

    • Continue to prioritize long, smooth blends to maintain the hypnotic flow.
    • Introduce a few more 'peak' moments to provide clear energy releases.
    • Experiment with more diverse mid-range elements to add texture to the mix.
    Listen on SoundCloud

    Curious how your own set scores?

    Methodology and limitations

    This report is produced automatically from the published recording. It measures what is present in the audio file: a bad source encoding, a partial upload or a heavily processed broadcast version can change the measured values. Nothing here reflects the room, the crowd or the night itself.

    Read the full SOONOS methodology →

    Questions about this analysis

    What score does wAFF's NATURECAST 003: Lorenzo De Blanck get?
    NATURECAST 003: Lorenzo De Blanck scores 75/100 in the SOONOS Mix Database (solid), based on audio measurement plus multi-agent listening.
    How long is NATURECAST 003: Lorenzo De Blanck?
    The analysed recording runs 60 min.
    Which part of the mix scores best?
    IDENTITY is the strongest category at 83/100, while SURPRISE is the lowest at 70/100.
    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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