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Experimental Music

Creating experimental music with Suno AI involves navigating the platform's capabilities and limitations to produce unique and unconventional sounds. This often requires a blend of structured prompting, creative experimentation, and post-processing techniques.

Challenges in Generating Experimental Music​

Generating obscure and experimental styles with Suno AI can be challenging. The platform may sometimes default to more generic styles, such as pop, regardless of specific prompts. For example, users attempting to create Indian folk, ambient, or baccata mixes have reported receiving slow country music instead. This issue highlights the need for detailed and precise prompting to guide the AI towards less conventional outputs. The platform may also ignore the specified musical styles and genres, producing generic new pop songs regardless of the prompts.

Modular Songwriting Approach​

To overcome these challenges, a modular songwriting approach can be highly effective. This involves using a detailed template to organize song elements, balancing control and creativity. A template can include parameters such as:

  • Song Basics: Title, primary and secondary genres, tempo, key, time signature, and duration.
  • Emotional Tone: Primary and secondary emotions, and overall mood.
  • Lyrical Content: Theme, narrative style, rhyme scheme, metaphors, and hook/tagline.
  • Structure: Intro, verse, pre-chorus, chorus, bridge, and outro details.
  • Melodic Elements: Descriptions of verse, chorus, and bridge melodies.
  • Harmonic Elements: Chord progressions for verses, choruses, and bridges.
  • Rhythmic Elements: Rhythmic feel, drum pattern, and notable rhythmic features.
  • Instrumentation: Lead instrument, rhythm section, additional instruments, and production elements.
  • Dynamic Instructions: Verse and chorus dynamics, and any notable changes.
  • Special Instructions: Unique features, cultural references, target audience, and inspirations.
  • AI-Specific Guidelines: Lyrical style, rhyme density, metaphor usage, repetition, emotional progression, and language complexity.

By using such a template, users can systematically input song details into the custom instructions, aiming for a balance between control and creative flexibility.

Post-Processing Techniques​

Post-processing is crucial for enhancing the quality of experimental music generated by Suno AI. The raw output may contain issues such as background noise, glitches, and distortion. Advanced audio editing tools and techniques can help mitigate these problems:

  • Mastering Tools: Tools like Diktatorial Suite, CloudBounce, and Izotope Ozone can improve clarity and loudness.
  • Stem Separation: Separating tracks into stems using tools like Ultimate Vocal Remover allows for individual enhancement, enabling detailed control over the sound and reducing unwanted noise in specific parts of the track.
  • Audio Restoration Tools: Tools like Izotope RX and Unchirp can address specific audio issues like compression artifacts and chirping sounds.

Combining different processing tools and techniques can provide a more comprehensive improvement. Experimentation and patience are essential, as different tracks and genres may respond differently to various enhancements.

Addressing Specific Issues​

The Suno v3.5 update has introduced both improvements and challenges. While audio quality has generally improved, particularly for electronic and dubstep styles, users have reported longer generation times. Vocal quality remains an area of concern, with distortion and "demon choir" effects still present. Additionally, the prompt generator may occasionally fail to retain previous prompts, requiring users to add punctuation to correct the issue.

To achieve better results, users should:

  • Use Detailed Prompts: Include specific descriptions of genres, styles, and desired elements in the prompts, both in the style box and within brackets at the start of the track.
  • Dispute Incorrect Generations: If a generated song does not meet the expected quality or style, consider disputing the generation for a human review.
  • Experiment with Prompts: Try different prompt structures and include punctuation to improve accuracy.

By understanding these issues and employing the suggested techniques, users can better navigate the platform and create more compelling experimental music.