🎵 Voice Note to MIDI

Transform your voice memos, humming, and melodic recordings into clean, quantized MIDI files ready for your DAW.

What It Does

This skill provides a complete audio-to-MIDI conversion pipeline that:

1. 1. Stem Separation - Uses HPSS (Harmonic-Percussive Source Separation) to isolate melodic content from drums, noise, and background sounds
2. ML-Powered Pitch Detection - Leverages Spotify's Basic Pitch model for accurate fundamental frequency extraction
3. Key Detection - Automatically detects the musical key of your recording using Krumhansl-Kessler key profiles
4. Intelligent Quantization - Snaps notes to a configurable timing grid with optional key-aware pitch correction
5. Post-Processing - Applies octave pruning, overlap-based harmonic removal, and legato note merging for clean output

Pipeline Architecture

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Setup

Prerequisites

• - Python 3.11+ (Python 3.14+ recommended)
• FFmpeg (for audio format support)
• pip

Installation

Quick Install (Recommended):

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This automated script will:

• - Check Python 3.11+ is installed
• Create the ~/melody-pipeline directory
• Set up the virtual environment
• Install all dependencies (basic-pitch, librosa, music21, etc.)
• Download and configure the hum2midi script
• Add melody-pipeline to your PATH

Manual Install:

If you prefer manual setup:

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1. 5. Add to your PATH (optional):

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Verify Installation

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Usage

Basic Usage

Convert a voice memo to MIDI:

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This creates my_humming.mid with 16th-note quantization.

Specify Output File

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Command-Line Options

Usage Examples

Quantize to eighth notes

Key-aware quantization (recommended for tonal music)

Require longer minimum notes

Skip analysis for faster processing

Combine options

Processing MIDI Input

You can also process existing MIDI files through the quantization pipeline:

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This skips the audio processing steps and goes directly to analysis and quantization.

Sample Output

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Notes & Limitations

Audio Quality Matters

• - Clear, loud melody produces the best results
• Background noise can cause false note detection
• Reverb and effects may confuse pitch detection
• Close-mic'd vocals work significantly better than room recordings

Musical Considerations

• - Monophonic sources work best (single melody line)
• Polyphonic audio (chords, multiple instruments) will produce messy results
• Vibrato and pitch bends may be quantized to stepped pitches
• Rapid note passages may be missed or merged

Technical Limitations

• - Tempo is fixed at 120 BPM in output (time positions are preserved, but tempo may need adjustment in your DAW)
• Note velocities are normalized but may need manual adjustment
• Very short notes (<50ms) may be filtered out by default
• Extreme pitch ranges may cause octave detection issues

Post-Processing Recommendations

After generating MIDI, you may want to:

1. 1. Import into your DAW and adjust tempo to match your original recording
2. Quantize further if stricter timing is needed
3. Adjust note velocities for dynamics
4. Apply swing/groove templates if the rigid grid sounds too mechanical
5. Edit individual notes that were misdetected (common with fast runs)

Supported Audio Formats

Input formats supported via FFmpeg:

• - WAV, AIFF, FLAC (uncompressed, best quality)
• MP3, M4A, AAC (compressed, acceptable)
• OGG, OPUS (open source formats)
• Most other formats FFmpeg supports

Troubleshooting

No notes detected

• - Check that input file isn't silent or corrupted
• Try increasing --min-note threshold
• Verify audio has clear melodic content (not just noise)

Too many notes / messy output

• - Enable octave pruning and overlap pruning (on by default)
• Use --key-aware to constrain to musical scale
• Check for background noise in source audio

Wrong key detected

• - Key detection works best with at least 8-10 measures of music
• Chromatic passages may confuse the detector
• Manually review and adjust in your DAW if needed

Notes in wrong octave

• - Basic Pitch sometimes detects harmonics instead of fundamentals
• The pipeline includes pruning, but some may slip through
• Use your DAW's transpose function for simple octave shifts

References

• - Basic Pitch - Spotify's polyphonic pitch detection model
• librosa HPSS - Harmonic-Percussive Source Separation
• Krumhansl-Kessler Key Profiles - Key detection algorithm

License

This skill integrates Basic Pitch by Spotify, which is licensed under Apache 2.0. The pipeline script and documentation are provided under MIT license.