Analog Music Datasets for AI Training

100 Years of Analog Sound, Machine-Ready

Sonic Atlas provides legally cleared, hardware-verified analog music datasets designed for AI training, machine learning, and generative audio models.

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Two-Layer Dataset Architecture

Golden Sessions are Sonic Atlas’s core dataset units — hardware-verified analog music datasets sourced from fully owned master tape archives spanning over 100 years of recorded heritage.

Each Golden Session is structured as a complete, machine-ready dataset, delivering two layers derived from a single source recording.

Together, these layers provide both structural audio data and real-world signal behaviour for high-quality AI training.

Each Golden Session Delivers...


• 24 fully mastered, production-grade stems per track


• Analog variation processing through real hardware


• Full legal clearance and documented ownership



All stems are delivered fully mastered, ensuring consistency, usability, and real-world audio fidelity across training datasets.

The Problem We Solve

Most AI Music Datasets Are:

• Legally ambiguous — scraped data introduces litigation risk


• Digitally native — lacking real-world harmonic complexity and signal behaviour


• Poorly documented — no signal path metadata or reproducibility

AI models require training data that reflects real-world recordings. That Means Analog.

OUR Two-Layer System

Every Golden Session delivers two complementary data layers from a single source recording.

  • 24 individually mastered stems per track: drums, bass, guitars, vocals, keys, FX, and more.

    • Captured and aligned from Sonic Atlas analog master tapes

    • Full signal path metadata for every stem

    • Example chain: Studer A827 → Neve 1073 → Prism ADA → 96 kHz/24-bit WAV

    All stems are professionally mastered — not raw multitracks — ensuring consistent levels, tonal balance, and real-world production characteristics for reliable model training.

    What you get: Clean, high-resolution source material with complete hardware provenance.

  • Each Layer 1 stem is re-processed through analog hardware to produce unique tonal variants.

    Hardware palette includes:

    Tape Machines

    • Studer A80 (×2)

    • Studer B67

    • Studer A827

    Console & EQ

    • Neve 48-track console

    • BBC Labs parametric EQs (×10)

    • Neve 1073 preamps

    Compressors & Limiters

    • Teletronix LA-2A

    • Joe Meek compressors (×4)

    • Dolby compressors

    Synthesizers & Keyboards

    • Moog Model D, Minimoog, Moog Prodigy

    • Roland Juno-60, Juno-106, Jupiter-8

    • Korg MS-20, Poly-800, M1

    • Alesis Andromeda

    • Sequential Circuits Prophet-5

    • Oberheim OB-X

    • ARP Odyssey

    • Yamaha DX7, CS-80

    Reverbs & Effects

    • EMT 140 Plate Reverb

    • Spring reverbs

    • Eventide Harmonizers

    • SP-1200 resampling

    Captured characteristics:

    • Harmonic distortion profiles

    • Noise floor signatures

    • Modulation and saturation behaviour

    • Three playback speeds (−5%, normal, +5%) for time/pitch-learning variance

    This layer enables models to learn how audio behaves in real analog environments, not just how it is structured.

    What you get: The sonic fingerprints AI models need to learn analog behaviour—not approximate it.

Data Format & Deliverables

Component Specification

Audio96 kHz / 24-bit WAV

MIDI Aligned to audio

Tempo/Key BPM and key tags

Metadata JSON schema per asset

Score MusicXML symbolic notation

OUR LIBRARY Archive Scale

Metric Count

Master Tapes 700

Cleared Catalog (incl. Major Artists) 700

Label Partnerships Sony, warner, universal 4ad

Mastered Stems per Song 24

Two-Layer Files per Song 48

TOtal Stems 150,000+

Licensing Tiers

Golden Sessions are delivered through flexible licensing tiers designed to support different stages of AI model development and deployment.

  • Source stems from our fully owned masters, including hit songs from the 80s and 90s.

    • Source: 500+ fully owned songs

    • Licensing risk: None

    • Pricing: $15,000–$25,000/month retainer or $1,200/session

  • Cleared works from estate and dormant label contracts featuring major artists.

    • Source: Cleared catalog via label partnerships

    • Licensing risk: Low (post-clearance)

    • Pricing: $40,000–$60,000/month or $5,000–$8,000/session

  • Bespoke sessions and custom label clearances for current artists.

    • Source: Active roster via major label partnerships

    • Licensing risk: Managed

    • Pricing: $25,000–$40,000 per track (20% service fee)

  • Available for any tier. Each stem re-processed through our analog hardware collection to produce unique tonal variants.

    • Includes: Tape machine processing, console coloration, vintage compression, synthesizer layering, reverb and effects

    • Pricing: +35% premium on base tier pricing

Golden Sessions are delivered through flexible licensing tiers designed to support different stages of AI model development and deployment.

All tiers include fully owned, legally cleared datasets with documented provenance. Sonic Atlas provides contractual indemnity against third-party rights claims, ensuring legal certainty and protection for AI training and deployment.

Why Sonic Atlas

  • Fully owned analog master recordings with verified chain of control, eliminating licensing ambiguity and downstream rights risk.

  • Hardware-verified audio capturing real-world signal behaviour, including tape saturation, harmonic distortion, and analog modulation.

  • Fully mastered multitrack stems — not raw recordings — ensuring consistency, tonal balance, and production-grade inputs for AI model training.

  • Documented signal paths, hardware provenance, and structured metadata enabling reproducible and scalable model development.

  • Rights-cleared datasets with contractual indemnity against third-party claims, removing the legal risks associated with scraped or unverified data.

  • Scalable dataset architecture allowing multiple training datasets to be generated from the same source material through layered analog processing.

  • Every dataset traces back to verifiable recording equipment and documented analog lineage.

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