Damian Lazarus — Lazpod 31
Indie House · 2h 04m · analysed 21 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, Damian Lazarus.
In short
Lazpod 31 by Damian Lazarus scores 72/100 in the SOONOS analysis (solid). The strongest dimension is IDENTITY (85/100) and the weakest is SURPRISE (77/100).
This DJ understands that in techno, the space between the notes is as important as the kick drum.
Key takeaways
What worked
- Technical precision
- Groove continuity
- Atmospheric consistency
- Emotional storytelling
What weakened the set
- Long static loops (6×) — Sections sitting on the same loop without layering, EQ moves or tension shifts (5:00, 34:00, 40:00, 58:00, 62:00, 70:00). Long loops kill dancefloor momentum.
What to try next
- Focus on 'harmonic mixing' to bridge genre gaps; the 60:12 transition felt musically unrelated.
- Maintain percussive energy when switching from rock to R&B to avoid a 'sag' in the dancefloor energy.
- Use shorter transitions for high-contrast genre shifts to make them feel like 'power cuts' rather than slow, awkward blends.
- Introduce more melodic elements to provide emotional contrast.
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.
Perfectly executed transition from spoken intro to the main groove.
Very smooth transition, the groove remains uninterrupted as the new bassline takes over.
Effective use of filtering to bring in the new track's lead elements.
Smooth low handover on the downbeat, no flam, energy preserved.
About this indie house set
Lazpod 31 is a indie house DJ set by Damian Lazarus, running 2h 04m. It was measured by the SOONOS multi-agent engine and scores 72/100 overall — solid.
The engine files this performance as “Promising”.
This DJ understands that in techno, the space between the notes is as important as the kick drum.
How the score breaks down
SOONOS scores this set across 5 weighted categories. The strongest is IDENTITY at 85/100; the weakest is SURPRISE at 77/100.
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 45 points, moving between 20 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 558.2 grid breaks per hour, with a median beat offset of 21 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
This DJ understands that in techno, the space between the notes is as important as the kick drum.
The 8 dimensions
Not available — the 8-dimension profile was not measured for this set.
Score breakdown
TECHNIQUE
How well did you execute?
25%84
TECHNIQUE
How well did you execute?
STORYTELLING
Did the mix go somewhere?
25%80
STORYTELLING
Did the mix go somewhere?
ENERGY
Did you control the room?
20%80
ENERGY
Did you control the room?
SURPRISE
Did you remain unpredictable?
15%77
SURPRISE
Did you remain unpredictable?
IDENTITY
Did the mix sound like you?
15%85
IDENTITY
Did the mix sound like you?
Energy arc
Transitions analysed (4)
Atmospheric crossfade into percussive loop
Perfectly executed transition from spoken intro to the main groove.
Long EQ blend with low-end swap
Very smooth transition, the groove remains uninterrupted as the new bassline takes over.
Mid-range layering and filter sweep
Effective use of filtering to bring in the new track's lead elements.
32-bar EQ blend, low swap on phrase
Smooth low handover on the downbeat, no flam, energy preserved.
Technical subscores
84
EQ balance
90
Beat alignment
80
Energy control
90
Tempo stability
82
Loudness control
84
Musical coherence
82
Harmonic compatibility
87
Transition cleanliness
Measured (DSP)
21 ms
Beat-grid drift (median)
Typical gap between the two tracks' beats while they play together. Lower is tighter.
154 ms
Drift p95
Worst-case drift: 95% of the set stays tighter than this value.
7,431
Boundaries detected
Points where the audio changes track or section.
32
Hard cuts / h
Abrupt track changes per hour, with no blend between the two records.
54
Clipping events
Moments where the recording hits the ceiling and distorts.
844
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 (5:00, 34:00, 40:00, 58:00, 62:00, 70:00). Long loops kill dancefloor momentum.
What SOONOS heard
- Technical precision
- Groove continuity
- Atmospheric consistency
- Emotional storytelling
- Atmospheric consistency
- Patient pacing
Timeline
- 0:45Narrative Hook
- 7:20Groove Lock
Coaching notes
- Focus on 'harmonic mixing' to bridge genre gaps; the 60:12 transition felt musically unrelated.
- Maintain percussive energy when switching from rock to R&B to avoid a 'sag' in the dancefloor energy.
- Use shorter transitions for high-contrast genre shifts to make them feel like 'power cuts' rather than slow, awkward blends.
- Introduce more melodic elements to provide emotional contrast.
- Experiment with more creative transition techniques like looping.
- Use filter sweeps more effectively to signal energy shifts.
- Ensure energy levels don't peak too early in the overall mix.
- Use subtle delay throws on percussion to add spatial depth.
- Experiment with brief, dramatic filter cuts to create 'fake' drops.
- Incorporate more varied mid-range textures to prevent frequency fatigue.
- Introduce more melodic variation to break up long percussive segments.
- Experiment with high-pass filter sweeps to build more tension before drops.
- Incorporate occasional 'surprise' elements to keep the listener on their toes.
- Use dramatic breakdowns to emphasize the emotional vocal elements.
- Continue to leverage long, transparent blends as a signature technique.
- Explore adding subtle live percussion elements to further enhance the organic feel.
- Maintain the current balance between vocal and instrumental tracks.
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 Damian Lazarus's Lazpod 31 get?
- Lazpod 31 scores 72/100 in the SOONOS Mix Database (solid), based on audio measurement plus multi-agent listening.
- How long is Lazpod 31?
- The analysed recording runs 2h 04m.
- Which part of the mix scores best?
- IDENTITY is the strongest category at 85/100, while SURPRISE is the lowest at 77/100.
- 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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