Analysis of Suno AI Music Generation Quality Degradation

Prompt Edit here before copy/try — source content is unchanged

A detailed analysis of audio degradation in Suno AI's music generation, with measurements, charts, and suggestions for improvement. Key findings include:
1. Progressive loudness increase: The recording started around -20 dBFS and ended around -12 dBFS, an increase of about 8 dB over the course of the song.
2. Loss of high-frequency detail: The proportion of frequency energy above 3,500 Hz dropped from 10.45% to 0.94%, a reduction of about 91%.
3. Music turning into a continuous drone: Detectable rhythmic attacks dropped by about 21%, estimated sustained harmonic content increased from 57% to 68%, and overall loudness increased by about 7 dB.
The user also attempted to fix the issue using the Extend feature, but the problem persisted.
Recommendations for Suno AI to address:
- Progressive loudness buildup during long generations
- Loss of instrument separation and rhythmic definition
- Increasingly muddy, sustained distortion in later sections
- Whether long-generation processing is contributing to these changes
- Why regenerating sections using Extend can reproduce the same degradation
- Better quality consistency across an entire song

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song_length audio_quality_metrics

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Usage & requirements

Analyze audio quality degradation in Suno AI music generation and provide improvement suggestions

Tools: suno ai
Variables: song_length audio_quality_metrics
Source: r/SunoAI ↗

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