Identified Patient — Identified Patient & Gamma Intel at Dekmantel Selectors 2022
Electro Techno · 2h 01m · analysed 23 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, Identified Patient.
In short
Identified Patient & Gamma Intel at Dekmantel Selectors 2022 by Identified Patient scores 82/100 in the SOONOS analysis (club ready). The strongest dimension is IDENTITY (92/100) and the weakest is TECHNIQUE (76/100).
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 (7:00, 13:00, 19:00, 24:00, 29:00, 44:00). Long loops kill dancefloor momentum.
What to try next
- Continue to explore the intersection of dark ambient and industrial techno.
- Experiment with more drastic EQ cuts to create rhythmic 'holes' in the mix.
- Consider introducing a vocal element to provide a focal point amidst the machine-like textures.
- Introduce a brief melodic or ambient break to provide contrast to the industrial grit.
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.
Masterful transition from atmosphere to rhythm.
Seamless transition between two percussive electro tracks. The low-end swap is perfectly timed on the phrase.
Smooth transition focusing on the mid-range textures. The energy remains constant throughout the blend.
Perfectly executed handover. The transition is used to evolve the texture rather than just change the song.
About this electro techno set
Identified Patient & Gamma Intel at Dekmantel Selectors 2022 is a electro techno DJ set by Identified Patient, running 2h 01m. It was measured by the SOONOS multi-agent engine and scores 82/100 overall — club 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 92/100; the weakest is TECHNIQUE at 76/100.
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 43 points, moving between 30 and 94 on the SOONOS energy scale — a wide dynamic range across the set.
4 transitions were detected and scored individually.
Beat-grid measurement (DSP) reports 895.7 grid breaks per hour, with a median beat offset of 25 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 8 dimensions
Not available — the 8-dimension profile was not measured for this set.
Score breakdown
TECHNIQUE
How well did you execute?
25%76
TECHNIQUE
How well did you execute?
STORYTELLING
Did the mix go somewhere?
25%89
STORYTELLING
Did the mix go somewhere?
ENERGY
Did you control the room?
20%83
ENERGY
Did you control the room?
SURPRISE
Did you remain unpredictable?
15%81
SURPRISE
Did you remain unpredictable?
IDENTITY
Did the mix sound like you?
15%92
IDENTITY
Did the mix sound like you?
Energy arc
Transitions analysed (4)
Long EQ blend, swapping low-end on the phrase.
Masterful transition from atmosphere to rhythm.
Long EQ blend with low-end swap
Seamless transition between two percussive electro tracks. The low-end swap is perfectly timed on the phrase.
Mid-range frequency handover
Smooth transition focusing on the mid-range textures. The energy remains constant throughout the blend.
Long EQ blend with sub-swap
Perfectly executed handover. The transition is used to evolve the texture rather than just change the song.
Technical subscores
83
EQ balance
89
Beat alignment
90
Energy control
91
Tempo stability
85
Loudness control
90
Musical coherence
81
Harmonic compatibility
85
Transition cleanliness
Measured (DSP)
25 ms
Beat-grid drift (median)
Typical gap between the two tracks' beats while they play together. Lower is tighter.
219 ms
Drift p95
Worst-case drift: 95% of the set stays tighter than this value.
7,244
Boundaries detected
Points where the audio changes track or section.
418
Hard cuts / h
Abrupt track changes per hour, with no blend between the two records.
61
Clipping events
Moments where the recording hits the ceiling and distorts.
1007
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 (7:00, 13:00, 19:00, 24:00, 29:00, 44:00). Long loops kill dancefloor momentum.
Timeline
- 4:12The Drop
- 8:45Percussive Peak
- 26:15Groove Lock Peak
Coaching notes
- Continue to explore the intersection of dark ambient and industrial techno.
- Experiment with more drastic EQ cuts to create rhythmic 'holes' in the mix.
- Consider introducing a vocal element to provide a focal point amidst the machine-like textures.
- Introduce a brief melodic or ambient break to provide contrast to the industrial grit.
- Experiment with more extreme filter resonance during transitions to enhance the mechanical feel.
- Incorporate more field recordings or non-musical samples to deepen the dystopian narrative.
- Experiment with brief, high-tension breakdowns to create more dynamic contrast.
- Incorporate more atmospheric, haunting elements to deepen the emotional impact.
- Monitor high-frequency layering to avoid potential EQ clashes.
- Maintain the exceptional energy and groove control demonstrated here.
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 Identified Patient's Identified Patient & Gamma Intel at Dekmantel Selectors 2022 get?
- Identified Patient & Gamma Intel at Dekmantel Selectors 2022 scores 82/100 in the SOONOS Mix Database (club ready), based on audio measurement plus multi-agent listening.
- How long is Identified Patient & Gamma Intel at Dekmantel Selectors 2022?
- The analysed recording runs 2h 01m.
- Which part of the mix scores best?
- IDENTITY is the strongest category at 92/100, while TECHNIQUE is the lowest at 76/100.
- 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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Test how a set like this one would land in a club, a beach club or a festival.
Get the same breakdown on your own set: transitions, energy arc and measured execution.
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