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I created the ultimate AI stem splitter tools cheatsheet, so you find the best one for you

6 min read Published By Christopher Wieduwilt
Dark teal infographic titled Find the Best AI Tools for Stem Separation with six numbered sections covering how it works, use cases, tools, splitting chart, source separation benchmark, and picking criteria

As a musician, I know the frustration of choosing the right stem splitting tool. With so many options available, it’s easy to feel overwhelmed.

But here’s the thing: picking the right stem splitter can help your music production workflow and unlock creative possibilities you never thought possible.

Download the cheatsheet here.

01 – What is stem separation actually?

Multicolored audio waveform splitting into four labeled stems: Vocals, Bass, Drums, and Other Instruments

Stem splitting is the process of separating a fully mixed audio track into its individual components, such as vocals, drums, bass, and other instruments. Think of it as having a magical audio surgeon that can precisely separate each layer of audio / music while maintaining pristine sound quality.

02 – 10 essential use cases for AI stem splitters:

AI stem splitting technology has changed various aspects of music production and content creation.

Here’s how different industries leverage this powerful tool:

Use caseExample application
Professional remixingCreating official remixes by isolating vocals from songs for modern reinterpretations
Live performance enhancementIsolating instrumental tracks for live karaoke or backing tracks during concerts
Karaoke productionRemoving vocals while maintaining high-quality instrumental tracks for karaoke applications
Film scoringExtracting specific elements from soundtracks to enhance particular scenes or create new arrangements
Sample creationIsolating unique sounds or instruments for use in new productions
Voice-over productionSeparating dialogue from background music in existing content for dubbing or translation
Audio restorationCleaning up and enhancing individual elements of historical recordings
Educational analysisStudying individual instrument parts for music education and transcription
Custom backing tracksCreating professional-quality accompaniment tracks for performers
Content creationExtracting specific elements for social media content, podcasts, or video productions

03 – AI stem separation tool overview:

Logo grid of 12 AI stem splitter tools — Audioshake, Izotope RX 11, Kits.AI, Fadr, Moises, Audiostrip, Lalal.ai, Bandlab Splitter, Logic Pro 11, Spectralayers 11, RipX DAW, and PEEL Stems — with category labels
Tool NameInterface TypePricing Model
AudioshakeWeb-basedFreemium
FadrPluginPremium
LALAL.AIWeb-basedFreemium
iZotope RX 11Plugin ModulePremium
Moises (Music.AI)Web-based, AppFreemium
BandLab SplitterWeb-based, AppFree
Kits.AIWeb-basedFree
AudioStripWeb-basedFreemium
Logic Pro 11StandalonePremium
SpectraLayers 11StandalonePremium
RipX DAWStandalonePremium
PEEL STEMSPluginPremium

Download the cheatsheet here.

04 – Stem splitting capabilities

Here’s a breakdown of AI stem separation tools and their splitting capabilities:

InstrumentsMusic.AILogic Pro 11Spectralayers 11RX 11 IzotopeLalal.aiFadrAudioshakeAudioStripBandLab SplitterPEEL STEMSRipX DAW
Vocals
Bass
Drums
Other
Guitar
Piano
Keys
Wind
Strings
Electric Guitar
Acoustic Guitar
Kick
Snare
Toms
Hi-hat
Cymbals
Cinematic Separation
Guitar Solo/Rhythm

05 – Source separation benchmarks:

Four-panel SDR boxplot benchmark on the MUSDB18HQ dataset comparing Music.ai, Logic Pro, Spectralayers, Izotope RX11, Lalal.ai, Demucs4, Fadr, Audioshake, and Kits.ai across Vocals, Bass, Drums, and Other stems

Signal-to-Distortion Ratio (SDR) is a crucial metric in evaluating the performance of AI stem separation tools. It quantifies how effectively an AI model can isolate individual instruments from a complete mix, with higher SDR values indicating cleaner separation.

A recent SDR benchmarking study (mentioned by Music.AI), such as those using the MUSDB18-HQ dataset, provided valuable insights into the capabilities of various models. This high-quality dataset, consisting of 150 uncompressed WAV files, offers a robust testing ground for AI separation algorithms.

However, it’s important to note that while SDR is a useful objective measure, it doesn’t always correlate directly with perceived audio quality. As an audio professional recently pointed out to me, the “best-sounding” model might not always have the highest SDR score.

That’s why I always recommend trying different tools yourself to find the one that best suits your specific needs and preferences. Each project may require a different approach, so hands-on experience with various models is invaluable.

06 – How to pick the right AI stem splitter

Four illustrated criteria icons for choosing an AI stem splitter — audio quality waveform, instrument guitar, dollar-sign budget, and piano keyboard interface preference

💰 Budget considerations:

  • Free Tier: BandLab Splitter provides unlimited splits with basic separation capabilities, making it an accessible entry point for beginners and casual users.
  • Mid-Range Solutions: LALAL.AI offers 10 minutes of free splitting with paid packages starting at $18 for 90 splitting minutes, providing professional-grade separation for vocals, instruments, drums, and bass.
  • Premium Services: Audioshake offers an Indie tier starting at $20 monthly for basic access (4 stems), with advanced features in higher-tier plans, while also providing custom enterprise solutions for larger clients

🖖🏻 Separation quality:

  • Test free tiers before purchasing to evaluate separation quality
  • Consider the number of stems needed – some tools offer basic vocal/instrumental separation while others provide up to 10 individual stems
  • Check supported file formats – most quality tools handle MP3, WAV, FLAC, and video formats

💻 Interface preferences:

  • Web-based platforms like Kits.AI and BandLab offer immediate access without installation1
  • Standalone software provides more robust features but requires local processing
  • Mobile apps like Moises enable on-the-go stem splitting

🔉 Audio quality requirements:

  • Check maximum file size limits (LALAL.AI supports up to 2GB)
  • Verify output format options – professional work may require WAV or FLAC
  • Consider processing speed needs – premium tiers often offer faster processing

Additional selection criteria

  • Evaluate batch processing capabilities for multiple files
  • Check integration options with your existing DAW or workflow
  • Consider community support and regular updates
  • Look for additional features like pitch/tempo adjustment that may add value

Remember, the most expensive option isn’t always the best choice – select a tool that matches your specific workflow needs and production requirements.

About the author

Photo of Christopher Wieduwilt

Christopher Wieduwilt

AI Music Educator & Journalist

Covering AI music tools, industry shifts, and news for music creators and professionals. Twice-weekly newsletter at aimusicpreneur.com.

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