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.gitattributes CHANGED
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
 
 
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
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+ train.jsonl filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ license: cc-by-4.0
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+ task_categories:
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+ - text-generation
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+ - audio-classification
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+ language:
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+ - en
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+ tags:
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+ - music
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+ - midi
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+ - bass
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+ - bassline
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+ - generative-music
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+ - chord-conditioned
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+ - pianoroll
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+ - symbolic-music
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+ - audio
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+ - monophonic
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+ - music-generation
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+ - music-information-retrieval
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+ size_categories:
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+ - 10K<n<100K
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+ ---
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+
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+ # NeuralAcid: Chord-Conditioned Bassline Dataset
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+
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+ ## Overview
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+
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+ NeuralAcid is a dataset of **10,000 monophonic bassline sequences** with per-bar chord annotations and **19.2 hours of rendered audio**. Every sample is available in three aligned representations:
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+
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+ 1. **Token sequence** -- symbolic note events with chord labels
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+ 2. **Piano roll** -- (60, 64) velocity matrix
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+ 3. **Audio** -- 44.1 kHz stereo WAV
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+
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+ No existing public dataset provides monophonic bass MIDI paired with chord annotations at this scale. NeuralAcid fills that gap.
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+
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+ ## Why This Dataset Exists
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+
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+ Training a chord-conditioned bassline generator requires paired (chord progression, bassline) data. Existing symbolic music datasets either lack isolated bass tracks (MAESTRO, Groove MIDI), require manual extraction and chord labeling (Lakh, Slakh2100), or are too small (FiloBass: 48 jazz songs). Acid/electronic bass datasets simply don't exist in academic form.
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+
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+ NeuralAcid takes a different approach: a rule-based generative sequencer produces the data, with musical constraints baked in so the model only sees valid basslines from day one.
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+
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+ ## Dataset at a Glance
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+
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+ | | Train | Validation |
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+ |---|---|---|
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+ | Samples | 9,000 | 1,000 |
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+ | Token sequences | 16.1 MB | 1.8 MB |
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+ | Piano rolls | 131.8 MB | 14.6 MB |
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+ | Rendered audio | 20.3 GB | 2.4 GB |
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+ | Total audio duration | ~17.3 h | ~1.9 h |
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+ | Unique chord progressions | 636 | |
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+
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+ ## Musical Properties
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+
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+ Every sequence is 4 bars at 120 BPM, strictly monophonic, and scale-quantized.
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+
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+ ### Built-in Musical Constraints
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+
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+ - **Scale-chord consistency** -- Major chords only produce notes from major-family scales (ionian, lydian, major pentatonic). Minor chords only use minor-family scales (aeolian, dorian, phrygian, minor pentatonic). Dominant 7th chords can draw from mixolydian, phrygian, or octatonic. No cross-contamination.
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+ - **Downbeat rule** -- Every bar begins with a note. 70% of downbeats land on the chord root.
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+ - **Monophonic guarantee** -- Post-generation clamp ensures no note overlaps the next.
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+
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+ ### Chord Progressions
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+
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+ 53 templates transposed across all 12 keys, covering:
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+
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+ | Style | Examples |
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+ |---|---|
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+ | Minor | i - iv7 - bVII - V7, i - bIII - iv - V7 |
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+ | Major | I - IV - V7 - I, I - vi - IV - V7 |
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+ | Blues | I7 - I7 - IV7 - I7, V7 - IV7 - I7 - V7 |
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+ | House / Techno | one-chord vamps, i - bIII oscillations |
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+ | Acid | phrygian bII, tritone subs, neapolitan |
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+ | Jazz | ii - V7 - I, minor ii-V-i |
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+ | Passing | diminished, augmented, suspended |
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+
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+ 10 chord types: maj, maj7, min, min7, dom7, 7, dim, dim7, aug, sus4, 7sus4, min7b5
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+
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+ ### Scale Palette
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+
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+ 13 scales selected per-bar based on chord type:
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+
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+ | Chord Family | Compatible Scales |
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+ |---|---|
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+ | Major | ionian, lydian, major pentatonic |
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+ | Minor | aeolian, dorian, phrygian, minor pentatonic |
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+ | Dominant | mixolydian, phrygian, minor pentatonic, octatonic |
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+ | Diminished | locrian, octatonic, diminished 7th |
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+ | Augmented | whole tone, lydian |
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+
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+ ### Parameter Space
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+
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+ Each sample is generated with independently randomized parameters. The full variation space:
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+
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+ | Parameter | Values | What it controls |
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+ |---|---|---|
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+ | Mode | random, drunk | Pure random vs. random walk |
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+ | Chance | 0.45 -- 0.98 | Note density |
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+ | Deviation | 1 -- 8 | Pitch range, octave jumps, note length variation |
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+ | Step division | 1/16, 1/8 | Rhythmic grid |
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+ | Note length | 1/32 -- 1/4 beat | Gate time |
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+ | Octave range | 2, 3, 4 | Pitch register span |
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+ | Velocity | 10 dynamic presets | Soft to loud, narrow to wide |
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+ | Tie chance | 0 -- 0.3 | Legato |
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+ | Pitch drift | -0.08 -- 0.08 | Gradual pitch wander |
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+ | Pitch weights | 27 presets + random | Per-degree note probability |
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+ | Step probability | 27 presets + random | Rhythmic accent patterns |
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+ | Clock pattern | 18 presets | Swing, shuffle, polyrhythm |
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+
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+ ## Data Formats
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+
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+ ### Token Sequence (`train.jsonl`, `val.jsonl`)
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+
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+ JSONL, one sample per line:
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+
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+ ```json
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+ {
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+ "id": 42,
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+ "roll_idx": 42,
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+ "chord_progression": "Cm7 | F7 | Bbmaj7 | G7",
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+ "chords": [
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+ {"bar": 0, "root": "C", "type": "min7", "scale_used": "dorian"},
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+ {"bar": 1, "root": "F", "type": "dom7", "scale_used": "mixolydian"}
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+ ],
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+ "params": {"mode": "random", "chance": 0.82, "deviation": 4, ...},
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+ "tokens": ["<BOS>", "<BAR>", "CHORD_Cm7", "NOTE_36", "DUR_4", "VEL_f", ...],
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+ "token_ids": [1, 3, 8, 149, 201, 218, ...],
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+ "num_notes": 25,
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+ "bars": 4
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+ }
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+ ```
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+
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+ **Vocabulary** (220 tokens):
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+
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+ | Type | Count | Format |
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+ |---|---|---|
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+ | Special | 5 | `<PAD>`, `<BOS>`, `<EOS>`, `<BAR>`, `<REST>` |
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+ | Chord | 132 | `CHORD_{root}{type}` (12 roots x 11 types) |
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+ | Note | 61 | `NOTE_{midi}` (MIDI 24-84, C1-C6) |
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+ | Duration | 16 | `DUR_{n}` (1-16, in 16th notes) |
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+ | Velocity | 6 | `VEL_{pp,p,mp,mf,f,ff}` |
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+
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+ Average sequence length: 78 tokens.
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+
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+ ### Piano Roll (`train_pianorolls.npy`, `val_pianorolls.npy`)
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+
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+ NumPy float32 arrays:
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+
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+ - **Shape**: `(N, 60, 64)` -- N samples, 60 pitches, 64 time steps
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+ - **Pitch axis**: MIDI 24 (C1) to 83 (B5)
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+ - **Time axis**: 16th-note grid, 4 bars = 64 steps
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+ - **Values**: velocity normalized to [0, 1]. Zero = silence.
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+ - **Sparsity**: ~99% zeros (monophonic bass)
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+
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+ Indexed by `roll_idx` in the JSONL.
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+
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+ ### Audio (`train_audio/`, `val_audio/`)
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+
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+ - Format: WAV, 44.1 kHz, stereo, 32-bit float
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+ - Duration: ~7 seconds per clip (4 bars at 120 BPM + release tail)
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+ - Rendered with [Vita](https://github.com/unohee/Vita) (Vital synthesizer Python bindings), default init preset
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+ - Filename: `{sample_id:06d}.wav`
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+
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+ ## Intended Uses
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+
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+ **Chord-conditioned generation** -- Train a model that takes a chord progression as input and outputs a bassline. The token format supports autoregressive decoding with chord context tokens.
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+
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+ **CNN + Sequence hybrid** -- Use piano rolls as CNN encoder input to extract rhythmic/melodic features, combined with a sequence decoder for token generation.
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+
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+ **Audio-to-MIDI benchmarking** -- 10,000 paired (audio, MIDI ground truth) samples for evaluating monophonic transcription systems on synthesized bass.
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+
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+ **Bassline pattern analysis** -- Study the relationship between chord types and bass note choices across scales and styles.
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+
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+ ## Limitations
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+
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+ - All sequences are synthetically generated, not human-performed. They lack expressive timing and dynamics of real performances.
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+ - Fixed tempo (120 BPM) and time signature (4/4).
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+ - Single synth timbre (Vital init preset). Real-world bass timbres vary widely.
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+ - 4-bar fixed length. Real basslines have longer-form structure.
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+ - The rule-based generator, while musically constrained, does not capture idiomatic patterns of specific genres (e.g., Motown walking bass, reggae one-drop) beyond what emerges from parameter randomization.
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+
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+ ## Generation Source
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+
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+ All data is produced by [Electr-o-matic](https://github.com/unohee/NeuralAcid), a generative sequencer ported from a Max for Live device to Python. The original device was built for real-time generative performance in Ableton Live.
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+
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+ ## License
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+
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+ CC-BY-4.0
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+ "vocab": {
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+ "<BOS>": 1,
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+ "<BAR>": 3,
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+ },
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+ "pitch_min": 24,
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+ },
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+ "seed": 42
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+ }
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