Adriana Lopez — Asylum Podcast (BAIHUI Radio) - Adriana Lopez

    Industrial Techno · 1h 02m · analysed 21 Aug 2026 · source: soundcloud.com

    Overall score
    85/100
    Level
    festival ready
    Duration
    1h 02m
    Genre
    Industrial Techno

    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, Adriana Lopez.

    In short

    Asylum Podcast (BAIHUI Radio) - Adriana Lopez by Adriana Lopez scores 85/100 in the SOONOS analysis (festival ready). The strongest dimension is IDENTITY (95/100) and the weakest is SURPRISE (81/100).

    The engine files this performance as “Promising”.

    Key takeaways

    What weakened the set

    • Long static loops (6×) — Sections sitting on the same loop without layering, EQ moves or tension shifts (21:00, 29:00, 41:00, 47:00, 52:00, 56:00). Long loops kill dancefloor momentum.

    What to try next

    • Experiment with more dramatic filter sweeps to create larger tension releases.
    • Consider adding a subtle vocal or melodic element to increase emotional depth.
    • Maintain the current level of technical precision; it is world-class.

    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.

    • 5:22Measured (DSP)95/100Listen at 5:22

      Masterful transition. The incoming kick replaces the outgoing one so smoothly it feels like a single track evolution.

    • 9:05Measured (DSP)92/100Listen at 9:05

      Excellent use of layering to bridge two tracks with similar percussive DNA.

    • 20:10Measured (DSP)82/100Listen at 20:10

      Intentional reset

    • 23:45Measured (DSP)92/100Listen at 23:45

      The transition is almost imperceptible; the new bassline takes over without any phase issues.

    • 28:20Measured (DSP)95/100Listen at 28:20

      Masterful use of the second track's percussion to build energy before removing the first track's core.

    About this industrial techno set

    Asylum Podcast (BAIHUI Radio) - Adriana Lopez is a industrial techno DJ set by Adriana Lopez, running 1h 02m. It was measured by the SOONOS multi-agent engine and scores 85/100 overall — festival ready.

    The engine files this performance as “Promising”.

    How the score breaks down

    SOONOS scores this set across 5 weighted categories. The strongest is IDENTITY at 95/100; the weakest is SURPRISE at 81/100.

    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 18 points, moving between 70 and 88 on the SOONOS energy scale — a narrow dynamic range across the set.

    7 transitions were detected and scored individually.

    Beat-grid measurement (DSP) reports 811.3 grid breaks per hour, with a median beat offset of 3 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

    The 8 dimensions

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

    Score breakdown

    TECHNIQUE

    How well did you execute?

    25%92
    Beat Alignmentmeasured98
    Transition Qualitymeasured87
    Phrase Accuracymeasured94
    EQ & Frequency Control (estimated)measured91
    Tempo Stability (estimated)measured96
    Technical Cleanlinessmeasured80

    STORYTELLING

    Did the mix go somewhere?

    25%90
    Narrative ArcAI94
    ProgressionAI90
    Tension & ReleaseAI85
    Section AwarenessAI90
    Flow ContinuityAI95
    PayoffAI82

    ENERGY

    Did you control the room?

    20%82
    Energy Curvemeasured78
    PacingAI93
    MomentumAI92
    Peak ManagementAI87
    Energy HandoffAI88
    Fatigue ControlAI41

    SURPRISE

    Did you remain unpredictable?

    15%81
    Unexpected MomentsAI82
    ContrastAI83
    Transition Varietymeasured96
    Selection RiskAI86
    FreshnessAI39
    Surprise ControlAI82

    IDENTITY

    Did the mix sound like you?

    15%95
    Selection QualityAI94
    Identity ConsistencyAI98
    IntentionalityAI95
    ConfidenceAI95
    RestraintAI94
    SignatureAI94

    Energy arc

    21:0061:50

    Transitions analysed (7)

    5:22

    Long-form EQ blend with low-end handover

    Masterful transition. The incoming kick replaces the outgoing one so smoothly it feels like a single track evolution.

    95
    9:05

    Mid-frequency layering and filter fade

    Excellent use of layering to bridge two tracks with similar percussive DNA.

    92
    11:30

    Perfect Blend

    100
    16:25

    Surgical EQ Swap

    100
    20:10

    Minor Phase Drift

    intentional_reset

    82
    23:45

    Long EQ blend with low-end swap

    The transition is almost imperceptible; the new bassline takes over without any phase issues.

    92
    28:20

    Additive layering

    Masterful use of the second track's percussion to build energy before removing the first track's core.

    95

    Technical subscores

    91

    EQ balance

    96

    Beat alignment

    89

    Energy control

    96

    Tempo stability

    90

    Loudness control

    93

    Musical coherence

    90

    Harmonic compatibility

    93

    Transition cleanliness

    Measured (DSP)

    3 ms

    Beat-grid drift (median)

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

    141 ms

    Drift p95

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

    3,693

    Boundaries detected

    Points where the audio changes track or section.

    45

    Hard cuts / h

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

    187

    Clipping events

    Moments where the recording hits the ceiling and distorts.

    65

    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 (21:00, 29:00, 41:00, 47:00, 52:00, 56:00). Long loops kill dancefloor momentum.

    Coaching notes

    • Experiment with more dramatic filter sweeps to create larger tension releases.
    • Consider adding a subtle vocal or melodic element to increase emotional depth.
    • Maintain the current level of technical precision; it is world-class.
    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 Adriana Lopez's Asylum Podcast (BAIHUI Radio) - Adriana Lopez get?
    Asylum Podcast (BAIHUI Radio) - Adriana Lopez scores 85/100 in the SOONOS Mix Database (festival ready), based on audio measurement plus multi-agent listening.
    How long is Asylum Podcast (BAIHUI Radio) - Adriana Lopez?
    The analysed recording runs 1h 02m.
    Which part of the mix scores best?
    IDENTITY is the strongest category at 95/100, 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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