DosemDosem @ Womb Tokyo (Japan, 2023)

    Melodic Techno · 1h 59m · analysed 22 Aug 2026

    Overall score
    86/100
    Level
    festival ready
    Duration
    1h 59m
    Genre
    Melodic Techno

    About this melodic techno set

    Dosem @ Womb Tokyo (Japan, 2023) is a melodic techno DJ set by Dosem, running 1h 59m. It was measured by the SOONOS multi-agent engine and scores 86/100 overall — festival ready.

    The engine files this performance as “Promising”.

    This is the sound of a DJ who trusts their tracks enough to let them breathe, using transitions as bridges rather than interruptions.

    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 51 points, moving between 65 and 92 on the SOONOS energy scale — a moderate dynamic range across the set.

    6 transitions were detected and scored individually.

    Beat-grid measurement (DSP) reports 346.4 grid breaks per hour, with a median beat offset of 7 ms.

    Listen to the original set

    Full SOONOS analysis

    TECSELNRGCRWHRMGRVCRESIG
    0

    This is the sound of a DJ who trusts their tracks enough to let them breathe, using transitions as bridges rather than interruptions.

    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%88
    Beat Alignmentmeasured98
    Transition Qualitymeasured78
    Phrase Accuracymeasured94
    EQ & Frequency Control (estimated)measured91
    Tempo Stability (estimated)measured96
    Technical Cleanlinessmeasured62

    STORYTELLING

    Did the mix go somewhere?

    25%89
    Narrative ArcAI92
    ProgressionAI88
    Tension & ReleaseAI85
    Section AwarenessAI89
    Flow ContinuityAI93
    PayoffAI84

    ENERGY

    Did you control the room?

    20%85
    Energy Curvemeasured86
    PacingAI91
    MomentumAI91
    Peak ManagementAI87
    Energy HandoffAI88
    Fatigue ControlAI48

    SURPRISE

    Did you remain unpredictable?

    15%81
    Unexpected MomentsAI81
    ContrastAI84
    Transition Varietymeasured93
    Selection RiskAI85
    FreshnessAI49
    Surprise ControlAI81

    IDENTITY

    Did the mix sound like you?

    15%93
    Selection QualityAI91
    Identity ConsistencyAI96
    IntentionalityAI93
    ConfidenceAI93
    RestraintAI91
    SignatureAI91

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

    Energy arc

    12:00118:58

    Transitions analysed (6)

    4:12

    Extended EQ blend with low-end swap

    Perfectly executed long blend where the new bassline emerges organically from the previous atmosphere.

    94
    8:45

    Melodic layering and filter sweep

    Smooth integration of melodic elements; the DJ uses filters to mask the entry of the new lead synth.

    90
    13:12

    Long EQ blend with low-end swap

    A masterclass in transparency; the new bassline emerges without any phase cancellation.

    92
    18:45

    Melodic layering and high-pass filtering

    Smooth integration of melodic elements, using filters to manage the energy handoff.

    88
    25:45

    Long EQ blend with high-pass filtering

    Seamless introduction of the new percussion loop, perfectly phase-aligned.

    92
    30:12

    Harmonic layering and low-end swap

    A masterclass in melodic techno mixing; the keys align perfectly to create a new harmonic structure.

    95

    Technical subscores

    91

    EQ balance

    96

    Beat alignment

    89

    Energy control

    96

    Tempo stability

    89

    Loudness control

    92

    Musical coherence

    93

    Harmonic compatibility

    93

    Transition cleanliness

    Measured (DSP)

    7.4 ms

    Beat-grid drift (median)

    131.3 ms

    Drift p95

    7,120

    Boundaries detected

    85

    Hard cuts / h

    110

    Clipping events

    212

    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 (21:00, 26:00, 34:00, 36:00, 44:45, 55:00). Long loops kill dancefloor momentum.

    What SOONOS heard

    • Technical precision in EQ blending.
    • Exceptional pacing and patience.
    • Strong thematic consistency.

    Timeline

    • 0:00Atmospheric Opening
    • 6:45Melodic Introduction
    • 15:20Groove Lock
    • 22:30Tension Peak
    • 28:45Vocal Motif Entry

    Coaching notes

    • Experiment with shorter, more aggressive cuts to contrast the long blends.
    • Use vocal stems more prominently to anchor the melodic themes.
    • Slightly increase the mid-range EQ during peaks to add more 'bite' to the leads.
    • Experiment with brief, high-tension breakdowns to create more dramatic releases.
    • Incorporate more varied percussion textures to enhance the rhythmic complexity.
    • Try layering a subtle vocal element to add a human touch to the electronic landscape.
    Listen on SoundCloud

    Curious how your own set scores?

    Questions about this analysis

    What score does Dosem's Dosem @ Womb Tokyo (Japan, 2023) get?
    Dosem @ Womb Tokyo (Japan, 2023) scores 86/100 in the SOONOS Mix Database (festival ready), based on audio measurement plus multi-agent listening.
    How long is Dosem @ Womb Tokyo (Japan, 2023)?
    The analysed recording runs 1h 59m.
    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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