Blawan — N SKA @ Smolna: Blawan | WarmUp Set
Hard Techno · 2h 29m · analysed 20 Aug 2026 · source: soundcloud.com
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, Blawan.
In short
N SKA @ Smolna: Blawan | WarmUp Set by Blawan scores 80/100 in the SOONOS analysis (club ready). The strongest dimension is IDENTITY (88/100) and the weakest is SURPRISE (78/100).
The DJ's ability to maintain a deep, hypnotic groove while subtly evolving the energy is a hallmark of professional mastery.
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, 7:30, 13:30, 19:00, 25:00, 29:00). Long loops kill dancefloor momentum.
What to try next
- Maintain this level of technical precision; it is your strongest asset.
- Consider adding a 'wildcard' track that breaks the industrial mold slightly to surprise the listener.
- Use the 15-20 minute window to experiment with more aggressive filter work to build energy faster.
- Ensure the high-end frequencies don't become too piercing during long overlaps.
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.
A masterfully executed transition where the incoming track's rhythmic noise perfectly replaces the outgoing textures.
A textbook techno blend. The incoming track's sub-bass is introduced so subtly it feels like a natural evolution of the first track.
Smooth integration of industrial textures. The DJ manages the busy mid-range well, avoiding clutter.
About this hard techno set
N SKA @ Smolna: Blawan | WarmUp Set is a hard techno DJ set by Blawan, running 2h 29m. It was measured by the SOONOS multi-agent engine and scores 80/100 overall — club ready.
The engine files this performance as “Promising”.
The DJ's ability to maintain a deep, hypnotic groove while subtly evolving the energy is a hallmark of professional mastery.
How the score breaks down
SOONOS scores this set across 5 weighted categories. The strongest is IDENTITY at 88/100; the weakest is SURPRISE at 78/100.
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 79 points, moving between 30 and 88 on the SOONOS energy scale — a wide dynamic range across the set.
5 transitions were detected and scored individually.
Beat-grid measurement (DSP) reports 1209.3 grid breaks per hour, with a median beat offset of 22 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.
Full SOONOS analysis
The DJ's ability to maintain a deep, hypnotic groove while subtly evolving the energy is a hallmark of professional mastery.
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
TECHNIQUE
How well did you execute?
STORYTELLING
Did the mix go somewhere?
25%85
STORYTELLING
Did the mix go somewhere?
ENERGY
Did you control the room?
20%81
ENERGY
Did you control the room?
SURPRISE
Did you remain unpredictable?
15%78
SURPRISE
Did you remain unpredictable?
IDENTITY
Did the mix sound like you?
15%88
IDENTITY
Did the mix sound like you?
Energy arc
Transitions analysed (5)
Long textural blend with gradual EQ handover
A masterfully executed transition where the incoming track's rhythmic noise perfectly replaces the outgoing textures.
Long EQ blend with low-end swap
A textbook techno blend. The incoming track's sub-bass is introduced so subtly it feels like a natural evolution of the first track.
Mid-range layering
Smooth integration of industrial textures. The DJ manages the busy mid-range well, avoiding clutter.
Perfect Blend
Surgical EQ Swap
Technical subscores
88
EQ balance
93
Beat alignment
86
Energy control
93
Tempo stability
86
Loudness control
89
Musical coherence
87
Harmonic compatibility
90
Transition cleanliness
Measured (DSP)
22 ms
Beat-grid drift (median)
Typical gap between the two tracks' beats while they play together. Lower is tighter.
221 ms
Drift p95
Worst-case drift: 95% of the set stays tighter than this value.
8,923
Boundaries detected
Points where the audio changes track or section.
204
Hard cuts / h
Abrupt track changes per hour, with no blend between the two records.
219
Clipping events
Moments where the recording hits the ceiling and distorts.
1350
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, 7:30, 13:30, 19:00, 25:00, 29:00). Long loops kill dancefloor momentum.
Timeline
- 0:00Atmospheric Foundation
- 8:45Groove Consolidation
- 14:10Groove Lock
- 20:45Atmospheric Shift
Coaching notes
- Maintain this level of technical precision; it is your strongest asset.
- Consider adding a 'wildcard' track that breaks the industrial mold slightly to surprise the listener.
- Use the 15-20 minute window to experiment with more aggressive filter work to build energy faster.
- Ensure the high-end frequencies don't become too piercing during long overlaps.
- Experiment with more dramatic breakdowns to create higher peaks of tension.
- Incorporate subtle melodic elements to add depth and emotional variety.
- Maintain the current level of technical precision in transitions.
- Consider the overall arc of the full set to ensure this segment fits perfectly.
- Introduce subtle melodic elements to provide contrast to the industrial weight.
- Experiment with brief moments of silence to reset tension and amplify drops.
- Use filter sweeps more frequently to signal upcoming rhythmic shifts.
- Experiment with more aggressive filter sweeps to create artificial tension peaks.
- Introduce subtle melodic motifs to add emotional depth to the industrial textures.
- Try layering field recordings or ambient textures to enhance the atmospheric immersion.
- Continue exploring extended blends to maintain the hypnotic state.
- Introduce subtle melodic elements to provide brief moments of emotional relief.
- Experiment with more aggressive EQ cuts to create rhythmic variation within long loops.
- Experiment with more creative transition techniques.
- Incorporate more melodic elements to enhance narrative depth.
- Use more pronounced breakdowns to create greater contrast.
- Ensure consistent energy levels throughout the entire set.
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 Blawan's N SKA @ Smolna: Blawan | WarmUp Set get?
- N SKA @ Smolna: Blawan | WarmUp Set scores 80/100 in the SOONOS Mix Database (club ready), based on audio measurement plus multi-agent listening.
- How long is N SKA @ Smolna: Blawan | WarmUp Set?
- The analysed recording runs 2h 29m.
- Which part of the mix scores best?
- IDENTITY is the strongest category at 88/100, while SURPRISE is the lowest at 78/100.
- 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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