In evaluating Suno for potential integration into a client's meditation application, I found the prevailing discourse around AI music generation to be lacking in empirical rigor. Anecdotal reports of "hit or miss" quality are insufficient for a production pipeline. To quantify its reliability as a component, I designed a systematic test: generate 100 tracks with the explicit goal of being usable in a meditation context, then apply a consistent scoring framework.
My methodology was as follows:
- **Prompt Structure:** All prompts followed a templated pattern: `[Genre] meditation music for [use case], [mood], [instrumentation]`. Example: `"Ambient meditation music for sleep, calm and spacious, with soft pads and distant piano."`
- **Generation Parameters:** Used Custom Mode exclusively, with "Instrumental" toggled on for 80% of generations to avoid vocal artifacts. Style tags were constrained to a set: Ambient, Calm, Peaceful, Soothing, Cinematic.
- **Evaluation Criteria:** Each track was scored on a 0-2 scale across three dimensions, yielding a maximum of 6 points. A track scoring 5 or 6 was deemed a "success" for production.
1. **Sonic Quality (0-2):** Absence of audio glitches, harmonic dissonance, or rhythm unsuitable for meditation.
2. **Adherence to Prompt (0-2):** Does the output match the requested genre, mood, and instrumentation?
3. **Production Value (0-2):** Does the track sound professionally mixed and mastered, with a coherent structure?
The raw results from 100 generations were:
| Score (out of 6) | Number of Tracks |
| :--- | :--- |
| 6 | 7 |
| 5 | 18 |
| 4 | 31 |
| 3 | 27 |
| 2 | 12 |
| 1 | 4 |
| 0 | 1 |
**Analysis:**
- **Success Rate (Score ≥5):** 25 tracks, or **25%**. This is the core metric for "immediately usable" assets.
- **Partial Success (Score 4):** 31 tracks. These often required minor post-processing (e.g., fade-in/out, light EQ).
- **Failure (Score ≤3):** 44 tracks. Failures were primarily due to rhythmic anomalies (e.g., an unexpected percussive element), poor instrumentation choices by the model, or audible digital artifacts.
A significant finding was the high variance within a single prompt. Regenerating the same prompt four times could yield scores from 2 to 6. This indicates a fundamental stochasticity that must be accounted for in any workflow. The "Instrumental" toggle reduced, but did not eliminate, the occurrence of unwanted vocalizations.
**Conclusions for Pipeline Integration:**
If Suno is to be used in a production context:
1. Generation must be budgeted for a 4:1 overproduction ratio to secure one usable track.
2. A post-generation validation layer (automated audio analysis for BPM, spectral density, or perhaps an ML classifier) is advisable to filter low-scoring outputs before human review.
3. The cost and time model must factor in this high discard rate. Generating 100 tracks required significant prompt engineering and review time, not just API costs.
While the 25% of high-scoring tracks were exceptional and perfectly fit for purpose, the operational overhead to isolate them is non-trivial. Suno functions less as a deterministic tool and more as a rich, but noisy, source requiring significant curation.
-- elliot
Data first, decisions later.
This is fascinating work and exactly the kind of testing we need more of. I'm really curious to see where your numbers land.
Your choice to toggle "Instrumental" for 80% of the generations is a smart move for this use case. I've found that even with the best prompts, the AI can sometimes introduce a faint, wordless vocal hum or breathy texture when you least expect it, which can be incredibly distracting in a meditation track. That setting isn't a perfect guarantee, but it definitely shifts the probability.
You've set a high bar for a "success" at 5 or 6 points. I'm on the edge of my seat - did you find the success rate high enough to consider this a viable, scalable source for your client, or did the need for manual curation kill the efficiency?
hugo
Yeah, waiting for those results is killing me too. The "Instrumental" toggle is a must, but you're right, it's not perfect. I've still gotten tracks with weird percussive breathing sounds even with it on, which totally ruins the vibe.
I'm also really curious about the curation overhead. Even a 50% success rate sounds high until you think about manually listening to 100 tracks to find those 50. Does that scale for a whole app library?
What's your personal threshold for a viable success rate in a project like this?
Good methodology so far. The constraint on style tags is key - too many and the generator starts making weird compromises.
Your scoring system's a solid start, but I've found these subjective dimensions can drift after the first 20-30 tracks. How are you controlling for rater fatigue? Did you score them in one sitting or randomize the order?
—cp
You've pinpointed a critical methodological flaw that could skew the results. Rater fatigue is real, and subjective scoring over a large batch is prone to drift and recency bias.
I scored the 100 tracks in three sessions over two days, with mandatory breaks. More importantly, I randomized the listening order after generation to decouple the scoring sequence from the generation sequence. This prevents a cluster of poor generations early in the process from lowering your scoring standards for subsequent, potentially better tracks.
Even with that, I agree the scores for the final 20 tracks likely have a different internal calibration than the first 20. For a truly rigorous assessment, a double-blind setup with multiple raters would be necessary, but that wasn't feasible for this initial procurement evaluation.
Randomizing the listening order is a smart and necessary step that many overlook. I'd argue it's as important as the scoring rubric itself, because the baseline for "acceptable" can shift so dramatically based on recent exposure.
Your point about a multi-rater, double-blind setup being the gold standard is correct, but often impractical for a feasibility study. A more scalable compromise I've used is to have a primary rater score everything, then have a secondary rater assess a random 20% sample of the tracks, stratified by the primary rater's score brackets (e.g., 5 random tracks scored 6, 5 scored 3, etc.). This lets you calculate an inter-rater reliability metric like Cohen's Kappa to quantify the subjectivity in your own scoring system. If your own scores aren't consistent with a second listener, the absolute success rate is less meaningful.
Even with randomization, the drift you mention is real. Did you consider inserting a few identical "control" prompts at different points in the generation batch to measure that calibration shift directly? Seeing if the same prompt scored a 6 early on and a 4 later would be a telling data point on rater fatigue.