🎵 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. 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

Audio Input (WAV/M4A/MP3)
    ↓
┌─────────────────────────────────────┐
│ Step 1: Stem Separation (HPSS)     │
│ - Isolate harmonic content          │
│ - Remove drums/percussion           │
│ - Noise gating                      │
└─────────────────────────────────────┘
    ↓
┌─────────────────────────────────────┐
│ Step 2: Pitch Detection             │
│ - Basic Pitch ML model (Spotify)    │
│ - Polyphonic note detection         │
│ - Onset/offset estimation           │
└─────────────────────────────────────┘
    ↓
┌─────────────────────────────────────┐
│ Step 3: Analysis                    │
│ - Pitch class distribution          │
│ - Key detection                     │
│ - Dominant note identification      │
└─────────────────────────────────────┘
    ↓
┌─────────────────────────────────────┐
│ Step 4: Quantization & Cleanup      │
│ - Timing grid snap                  │
│ - Key-aware pitch correction        │
│ - Octave pruning (harmonic removal) │
│ - Overlap-based pruning             │
│ - Note merging (legato)             │
│ - Velocity normalization            │
└─────────────────────────────────────┘
    ↓
MIDI Output (Standard MIDI File)

Setup

Prerequisites

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

Installation

Quick Install (Recommended):

cd /path/to/voice-note-to-midi
./setup.sh

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:

mkdir -p ~/melody-pipeline
cd ~/melody-pipeline
python3 -m venv venv-bp
source venv-bp/bin/activate
pip install basic-pitch librosa soundfile mido music21
chmod +x ~/melody-pipeline/hum2midi

5. Add to your PATH (optional):

echo 'export PATH="$HOME/melody-pipeline:$PATH"' >> ~/.bashrc
source ~/.bashrc

Verify Installation

cd ~/melody-pipeline
./hum2midi --help

Usage

Basic Usage

Convert a voice memo to MIDI:

./hum2midi my_humming.wav

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

Specify Output File

./hum2midi input.wav output.mid

Command-Line Options

Option	Description	Default
--grid <value>	Quantization grid: 1/4, 1/8, 1/16, 1/32	1/16
--min-note <ms>	Minimum note duration in milliseconds	50
--no-quantize	Skip quantization (output raw Basic Pitch MIDI)	disabled
--key-aware	Enable key-aware pitch correction	disabled
--no-analysis	Skip pitch analysis and key detection	disabled

Usage Examples

Quantize to eighth notes

./hum2midi melody.wav --grid 1/8

Key-aware quantization (recommended for tonal music)

./hum2midi song.wav --key-aware

Require longer minimum notes

./hum2midi humming.wav --min-note 100

Skip analysis for faster processing

./hum2midi quick.wav --no-analysis

Combine options

./hum2midi recording.wav output.mid --grid 1/8 --key-aware --min-note 80

Processing MIDI Input

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

./hum2midi input.mid output.mid --grid 1/16 --key-aware

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

Sample Output

═══════════════════════════════════════════════════════════════
  hum2midi - Melody-to-MIDI Pipeline (Basic Pitch Edition)
  [Key-Aware Mode Enabled]
═══════════════════════════════════════════════════════════════

Input:  my_humming.wav
Output: my_humming.mid

→ Step 1: Stem Separation (HPSS)
  Isolating melodic content...
  Loaded: 5.23s @ 44100Hz
  ✓ Melody stem extracted → 5.23s

→ Step 2: Audio-to-MIDI Conversion (Basic Pitch)
  Running Spotify's Basic Pitch ML model on melody stem...
  ✓ Raw MIDI generated (Basic Pitch)

→ Step 3: Pitch Analysis & Key Detection
  Notes detected: 42 total, 7 unique
  Note range: C3 - G4
  Pitch classes: C3, E3, G3, A3, C4, D4, G4
  Dominant note: G3 (23.8% of notes)
  Detected key: G major

→ Step 4: Quantization & Cleanup
  Octave pruning: removed 3 harmonic notes above 67 (median+12)
  Overlap pruning: removed 2 harmonic notes at overlapping positions
  Note merging: merged 5 staccato chunks into legato notes (gap<=60 ticks)
  Grid:   240 ticks (1/16)
  Notes:  38 notes
  Key:    G major
  Key-aware: 2 notes corrected to scale
  Tempo:  120 BPM
  ✓ Quantized MIDI saved

═══════════════════════════════════════════════════════════════
  ✓ Done! Output: my_humming.mid
═══════════════════════════════════════════════════════════════

📊 ANALYSIS SUMMARY
─────────────────────────────────────────────────────────────
  Detected Notes: C3, E3, G3, A3, C4, D4, G4
  Detected Key:   G major
  Quantization:   Key-aware mode (notes snapped to scale)

MIDI Info: 38 notes, 7 unique pitches, 120 BPM
Pitches: C3, E3, G3, A3, C4, D4, G4

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. 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.