I Generated 47 Suno Tracks So You Don’t Have To — Here’s Everything I Learned

I Generated 47 Suno Tracks So You Don’t Have To — Here’s Everything I Learned

The problem that kept me up at night

I generated my first Suno track in October 2024. The instrumental was decent. But the vocal? It sounded like a GPS navigation system trying to sing Maroon 5.

Two comments crushed me:

• “Great instrumental — did you use text-to-speech for the vocal?”
• “Robot.”

https://erwinwijayanto.gumroad.com/l/the-suno-vocal-fix

I didn’t sleep that night. Not because I was offended, but because they were right.

47 tracks. 3 months. 1 breakthrough.

Over the next three months, I generated 47 tracks. Some were full songs. Some were just 15-second clips testing one specific parameter.

I changed prompts. I ran stems through five different splitting tools. I EQ’d, compressed, saturated, de-essed, and reverb’d until my ears rang.

Slowly, track by track, the robot disappeared.

By Track #32, people stopped asking if it was AI. By Track #41, a label A&R messaged me asking who the session singer was.

https://erwinwijayanto.gumroad.com/l/the-suno-vocal-fix

What I discovered

The robotic sound isn’t a “talent” issue. It’s a frequency-balance issue.

Here are the three dead giveaways:

1. The 2kHz–4kHz plastic range. Suno over-emphasizes this band. It’s what makes vocals sound “forward” but also “fake.”

2. Lack of sub-200Hz body. Real chest voices have weight. Suno often cuts this out to avoid muddiness.

3. Static dynamics. A real singer gets quieter on sustained notes and louder on consonants. Suno holds everything at the same level.

The fix

I developed a prompt framework called CLARITY:

• Character: Who is singing?
• Location: Where are they singing?
• Action: How are they singing?
• Reference: Drop 1–2 specific artists
• Intensity: 1–10 on the energy scale
• Technique: Vocal runs, slides, or vibrato
• Yield: Desired output format

https://erwinwijayanto.gumroad.com/l/the-suno-vocal-fix

I also discovered a 3-band EQ trick that fixes 60% of the robot sound:

• The Mud Cut (200–350Hz): Cut 3–4dB to remove boxiness
• The Presence Cut (2.5–4.5kHz): Cut 1.5–2dB to remove the plastic sheen
• The Air Lift (8–12kHz): Boost 9.5kHz by 2dB to restore breath

Your next step

I wrote everything I learned into a 31-page ebook called “The Suno Vocal Fix.”

It includes:

• 5 genre-specific prompt templates
• Stem-splitting workflows
• The 3-band EQ trick
• Compression, saturation, and de-essing strategies
• Reverb, delay, and humanizing edits
• Mastering for Spotify, Apple Music, and YouTube

If you’re using Suno AI and struggling with robotic vocals, this is the guide I wish I had on Track #1.

https://erwinwijayanto.gumroad.com/l/the-suno-vocal-fix

📖 Available now for $5.99

🔗 https://erwinwijayanto.gumroad.com/l/the-suno-vocal-fix