Daniel Avery — Daniel Avery Essential Mix (March 2018)

    Indie House · 1h 58m · analysed 22 Aug 2026 · source: soundcloud.com

    Overall score
    86/100
    Level
    festival ready
    Duration
    1h 58m
    Genre
    Indie 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, Daniel Avery.

    In short

    Daniel Avery Essential Mix (March 2018) by Daniel Avery scores 86/100 in the SOONOS analysis (festival ready). The strongest dimension is IDENTITY (95/100) and the weakest is SURPRISE (85/100).

    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 (8:00, 21:00, 35:00, 39:00, 43:00, 59:00). Long loops kill dancefloor momentum.

    What to try next

    • Continue to use long-form layering to maintain the hypnotic state.
    • Introduce a subtle melodic motif in the next segment to provide a narrative anchor.
    • Maintain the current level of EQ precision during rhythmic transitions.
    • Continue to explore the intersection of industrial textures and hypnotic rhythms.

    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.

    • 5:45Measured (DSP)95/100Listen at 5:45

      Masterful transition from ambient noise to a structured beat.

    • 14:35Measured (DSP)92/100Listen at 14:35

      Seamless transition where the industrial drone of the first track becomes the atmosphere for the second.

    • 20:15Measured (DSP)88/100Listen at 20:15

      Smooth introduction of a new percussive loop, perfectly phase-aligned.

    • 26:15Measured (DSP)95/100Listen at 26:15

      Perfectly executed long blend; the tracks become one for over a minute.

    About this indie house set

    Daniel Avery Essential Mix (March 2018) is a indie house DJ set by Daniel Avery, running 1h 58m. It was measured by the SOONOS multi-agent engine and scores 86/100 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 85/100.

    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 26 points, moving between 10 and 88 on the SOONOS energy scale — a wide dynamic range across the set.

    4 transitions were detected and scored individually.

    Beat-grid measurement (DSP) reports 1092.1 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

    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%86
    Beat Alignmentmeasured77
    Transition Qualitymeasured83
    Phrase Accuracymeasured93
    EQ & Frequency Control (estimated)measured90
    Tempo Stability (estimated)measured95
    Technical Cleanlinessmeasured80

    STORYTELLING

    Did the mix go somewhere?

    25%92
    Narrative ArcAI95
    ProgressionAI91
    Tension & ReleaseAI90
    Section AwarenessAI93
    Flow ContinuityAI95
    PayoffAI84

    ENERGY

    Did you control the room?

    20%87
    Energy Curvemeasured88
    PacingAI94
    MomentumAI91
    Peak ManagementAI89
    Energy HandoffAI89
    Fatigue ControlAI58

    SURPRISE

    Did you remain unpredictable?

    15%85
    Unexpected MomentsAI85
    ContrastAI87
    Transition Varietymeasured94
    Selection RiskAI89
    FreshnessAI56
    Surprise ControlAI85

    IDENTITY

    Did the mix sound like you?

    15%95
    Selection QualityAI94
    Identity ConsistencyAI97
    IntentionalityAI96
    ConfidenceAI95
    RestraintAI94
    SignatureAI95

    Energy arc

    0:0071:00

    Transitions analysed (4)

    5:45

    Long-form textural blend

    Masterful transition from ambient noise to a structured beat.

    95
    14:35

    Long textural blend with low-end swap

    Seamless transition where the industrial drone of the first track becomes the atmosphere for the second.

    92
    20:15

    EQ-based layering

    Smooth introduction of a new percussive loop, perfectly phase-aligned.

    88
    26:15

    Long EQ blend with bass handover

    Perfectly executed long blend; the tracks become one for over a minute.

    95

    Technical subscores

    90

    EQ balance

    95

    Beat alignment

    91

    Energy control

    95

    Tempo stability

    89

    Loudness control

    94

    Musical coherence

    91

    Harmonic compatibility

    93

    Transition cleanliness

    Measured (DSP)

    24 ms

    Beat-grid drift (median)

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

    212 ms

    Drift p95

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

    7,062

    Boundaries detected

    Points where the audio changes track or section.

    31

    Hard cuts / h

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

    456

    Clipping events

    Moments where the recording hits the ceiling and distorts.

    1031

    Level jumps

    Sudden volume steps between tracks.

    Measurement confidence 99% · analysed on the full audio.

    Recurring patterns

    • Long static loops (6×)

      Sections sitting on the same loop without layering, EQ moves or tension shifts (8:00, 21:00, 35:00, 39:00, 43:00, 59:00). Long loops kill dancefloor momentum.

    Timeline

    • 5:45The Drop
    • 8:15Texture Shift
    • 15:45Groove Lock
    • 22:10Textural Peak

    Coaching notes

    • Continue to use long-form layering to maintain the hypnotic state.
    • Introduce a subtle melodic motif in the next segment to provide a narrative anchor.
    • Maintain the current level of EQ precision during rhythmic transitions.
    • Continue to explore the intersection of industrial textures and hypnotic rhythms.
    • Experiment with brief moments of silence to enhance the impact of your builds.
    • Maintain the exceptional level of restraint shown in your long blends.
    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 Daniel Avery's Daniel Avery Essential Mix (March 2018) get?
    Daniel Avery Essential Mix (March 2018) scores 86/100 in the SOONOS Mix Database (festival ready), based on audio measurement plus multi-agent listening.
    How long is Daniel Avery Essential Mix (March 2018)?
    The analysed recording runs 1h 58m.
    Which part of the mix scores best?
    IDENTITY is the strongest category at 95/100, while SURPRISE is the lowest at 85/100.
    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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