Nicole Moudaber — InTheMood - Episode 637 - Live from Academy, Los Angeles (Part 2)

    Techno · 58 min · analysed 22 Jul 2026 · source: soundcloud.com

    Overall score
    87/100
    Level
    festival ready
    Duration
    58 min
    Genre
    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, Nicole Moudaber.

    In short

    InTheMood - Episode 637 - Live from Academy, Los Angeles (Part 2) by Nicole Moudaber scores 87/100 in the SOONOS analysis (festival ready). The strongest dimension is STORYTELLING (90/100) and the weakest is TECHNIQUE (82/100).

    The engine files this performance as “Promising”.

    Key takeaways

    What weakened the set

    • Long static loops (3×) — Sections sitting on the same loop without layering, EQ moves or tension shifts (20:00, 35:00, 42:00). Long loops kill dancefloor momentum.

    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.

    • 03:45Measured (DSP)92/100Listen at 03:45

      Seamless transition that maintains the groove while shifting the tonal focus.

    • 09:45Measured (DSP)88/100Listen at 09:45

      Effective use of filtering to introduce the next track's energy.

    • 12:45Measured (DSP)92/100Listen at 12:45

      Perfectly executed handover. The incoming bassline takes over without any audible dip in pressure.

    • 15:32Measured (DSP)88/100Listen at 15:32

      Smooth introduction of the next track's vocal elements while maintaining the previous track's groove.

    • 18:15Measured (DSP)94/100Listen at 18:15

      Excellent timing. The transition uses the 'I Am Ready' vocal to reset the energy and launch into a higher gear.

    • 29:45Measured (DSP)82/100Listen at 29:45

      Strategic disruption

    About this techno set

    InTheMood - Episode 637 - Live from Academy, Los Angeles (Part 2) is a techno DJ set by Nicole Moudaber, running 58 min. It was measured by the SOONOS multi-agent engine and scores 87/100 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 STORYTELLING at 90/100; the weakest is TECHNIQUE at 82/100.

    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 24 points, moving between 10 and 95 on the SOONOS energy scale — a wide dynamic range across the set.

    7 transitions were detected and scored individually.

    Beat-grid measurement (DSP) reports 383.5 grid breaks per hour, with a median beat offset of 24 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

    TECSELNRGCRWHRMGRVCRESIG
    0

    The 8 dimensions

    Technical precision92
    Track selection89
    Energy arc90
    Crowd control93
    Harmonic depth70
    Groove patience89
    Creativity76
    Signature90

    Score breakdown

    TECHNIQUE

    How well did you execute?

    25%82
    Beat Alignmentmeasured76
    Transition Qualitymeasured67
    Phrase Accuracymeasured94
    EQ & Frequency Controlmeasured82
    Tempo Stabilitymeasured95
    Technical Cleanlinessmeasured80

    STORYTELLING

    Did the mix go somewhere?

    25%90
    Narrative ArcAI92
    ProgressionAI90
    Tension & ReleaseAI85
    Section AwarenessAI92
    Flow ContinuityAI95
    PayoffAI88

    ENERGY

    Did you control the room?

    20%88
    Energy Curvemeasured85
    PacingAI90
    MomentumAI95
    Peak ManagementAI85
    Energy HandoffAI88
    Fatigue ControlAI85

    SURPRISE

    Did you remain unpredictable?

    15%84
    Unexpected MomentsAI82
    ContrastAI80
    Transition Varietymeasured92
    Selection RiskAI85
    FreshnessAI80
    Surprise ControlAI82

    IDENTITY

    Did the mix sound like you?

    15%90
    Selection QualityAI85
    Identity ConsistencyAI98
    IntentionalityAI92
    ConfidenceAI92
    RestraintAI80
    SignatureAI88

    Energy arc

    00:0058:00

    Transitions analysed (7)

    03:45

    Long EQ blend with low-end swap

    Seamless transition that maintains the groove while shifting the tonal focus.

    92
    09:45

    Percussive layering and high-pass filter sweep

    Effective use of filtering to introduce the next track's energy.

    88
    12:45

    32-bar EQ blend, low swap on phrase

    Perfectly executed handover. The incoming bassline takes over without any audible dip in pressure.

    92
    15:32

    Mid-range layering with vocal tease

    Smooth introduction of the next track's vocal elements while maintaining the previous track's groove.

    88
    18:15

    High-impact drop swap

    Excellent timing. The transition uses the 'I Am Ready' vocal to reset the energy and launch into a higher gear.

    94
    25:30

    Perfect Blend

    100
    29:45

    Minor EQ Overlap

    strategic_disruption

    82

    Technical subscores

    82

    EQ balance

    92

    Beat alignment

    90

    Energy control

    95

    Tempo stability

    84

    Loudness control

    92

    Musical coherence

    88

    Harmonic compatibility

    85

    Transition cleanliness

    Measured (DSP)

    24 ms

    Beat-grid drift (median)

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

    199 ms

    Drift p95

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

    3,465

    Boundaries detected

    Points where the audio changes track or section.

    106

    Hard cuts / h

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

    0

    Clipping events

    Moments where the recording hits the ceiling and distorts.

    116

    Level jumps

    Sudden volume steps between tracks.

    Measurement confidence 100% · analysed on the full audio.

    Recurring patterns

    • Long static loops (3×)

      Sections sitting on the same loop without layering, EQ moves or tension shifts (20:00, 35:00, 42:00). Long loops kill dancefloor momentum.

    Timeline

    • 03:45The Pivot
    • 07:15Groove Saturation
    • 18:22Energy Peak
    • 21:45Groove Lock
    • 18:15Risky pressure or vibe dip
    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 Nicole Moudaber's InTheMood - Episode 637 - Live from Academy, Los Angeles (Part 2) get?
    InTheMood - Episode 637 - Live from Academy, Los Angeles (Part 2) scores 87/100 in the SOONOS Mix Database (festival ready), based on audio measurement plus multi-agent listening.
    How long is InTheMood - Episode 637 - Live from Academy, Los Angeles (Part 2)?
    The analysed recording runs 58 min.
    Which part of the mix scores best?
    STORYTELLING is the strongest category at 90/100, while TECHNIQUE is the lowest at 82/100.
    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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