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Initial dataset upload: HTMOneShotLoopClassification v1.0.0
- Sample dataset CSV (100 rows)
- Complete metadata schema

CITATION.bib

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+@dataset{hemimy2025htmoneshotloop,
+  title={HTMOneShotLoopClassification: A Dataset of House, Tech House, and Minimal Techno Samples for Stem Category Classification},
+  author={Hemimy, Youssef},
+  year={2025},
+  publisher={Zenodo},
+  doi={10.5281/zenodo.17872191},
+  url={https://doi.org/10.5281/zenodo.17872191},
+  note={Dataset of 5,561 electronic music samples (one-shots and loops) with 55 extracted audio features, labeled for classification into 8 stem categories. Focus on house, tech house, and minimal techno genres.},
+  keywords={audio classification, electronic music, machine learning, music information retrieval, stem classification}
+}
+

DATASET_CARD.md

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+# Dataset Card for HTMOneShotLoopClassification
+
+## Dataset Description
+
+- **Homepage**: https://github.com/itsuzef/HTMOneShotLoopClassification
+- **Repository**: https://github.com/itsuzef/HTMOneShotLoopClassification
+- **Paper**: [To be added - link to paper when published]
+- **Point of Contact**: https://github.com/itsuzef
+- **DOI**: [10.5281/zenodo.17872191](https://doi.org/10.5281/zenodo.17872191)
+
+### Dataset Summary
+
+HTMOneShotLoopClassification is a dataset of 5,561 electronic music samples (one-shots and loops) from house, tech house, and minimal techno genres. Each sample is labeled with one of 8 stem categories and includes 55 extracted audio features for machine learning applications.
+
+The dataset is designed for:
+- Audio classification tasks
+- Stem category prediction
+- Feature engineering research
+- Electronic music analysis
+
+### Supported Tasks and Leaderboards
+
+- **Audio Classification**: Multi-class classification into 8 stem categories
+- **Baseline Performance**: 85.41% accuracy using SVM with RBF kernel
+
+### Languages
+
+- Audio samples (no spoken language)
+- Metadata in English
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance contains:
+- **Filename**: Original audio file name
+- **Stem Label**: One of 8 categories (kick, snare, hihat, bass, synth, vocal, percussion, fx)
+- **Category**: Sample type (one-shots or loops)
+- **55 Audio Features**: Extracted using librosa
+
+### Data Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| filename | string | Original audio filename |
+| stem | categorical | Target label (8 categories) |
+| category | categorical | one-shots or loops |
+| duration | float | Audio duration in seconds |
+| sample_rate | integer | Sample rate (typically 44100 Hz) |
+| rms | float | Root mean square energy |
+| zcr | float | Zero crossing rate |
+| spectral_centroid | float | Spectral centroid in Hz |
+| ... | ... | (See metadata_schema.json for all 55 features) |
+
+### Data Splits
+
+Recommended splits:
+- **Train**: 80% (4,450 samples)
+- **Validation**: 10% (556 samples)
+- **Test**: 10% (556 samples)
+
+Note: Good class balance (FX: 561 samples, Hihat: 907 samples). All categories within 80-130% of average.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was created to address the lack of curated electronic music sample datasets with:
+- High-quality professional samples
+- Consistent feature extraction
+- Genre-specific focus (house/tech house/minimal techno)
+- Both one-shots and loops
+
+### Source Data
+
+- Professional electronic music sample packs
+- Curated from house, tech house, and minimal techno collections
+- Quality-controlled and deduplicated
+
+**Category Notes**:
+- **Hihat** (907 samples): Most represented category because it includes multiple cymbal types (closed hihats, open hihats, ride cymbals, crash cymbals) essential for rhythmic patterns
+- **Bass & Synth** (100% key coverage): Key information is critical for these melodic categories to enable harmonic mixing and prevent dissonance
+- **Vocal** (45.1% key coverage): Samples without keys are primarily spoken word, vocal chops, and non-melodic elements; melodic vocals have key information
+
+### Annotations
+
+- **Annotation process**: Manual categorization by stem type
+- **Annotators**: Domain experts
+- **Annotation guidelines**: Based on standard electronic music production categories
+
+### Personal and Sensitive Information
+
+No personal or sensitive information is included. All samples are from commercial sample packs or original productions.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This dataset enables research in:
+- Automated music production tools
+- Audio classification systems
+- Electronic music analysis
+
+### Discussion of Biases
+
+- **Genre Bias**: Focused on house, tech house, and minimal techno (not all electronic music)
+- **Class Balance**: Good balance achieved (FX: 561, all categories within 80-130% of average)
+- **Key Coverage**: Not all categories have musical key information (only bass and synth have 100% coverage)
+
+### Other Known Limitations
+
+- Dataset size: 5,561 samples (moderate size)
+- Limited to specific sub-genres of electronic music
+- Some categories have low key coverage (important for harmonic mixing applications)
+
+## Additional Information
+
+### Dataset Curators
+
+- Youssef Hemimy
+
+### Licensing Information
+
+**Dataset License**: CC-BY-4.0 (Creative Commons Attribution 4.0 International)
+
+**License Scope**: This license applies to the CSV dataset containing extracted audio features and metadata, documentation, and scripts. You are free to use, share, and adapt the dataset for any purpose, including commercial use, provided you give appropriate credit.
+
+**Original Audio Files**: The original audio files used to generate this dataset are NOT included and are NOT covered by this license. The audio files were obtained from commercial sample packs under their respective licenses, which typically prohibit redistribution. Users must obtain the original audio files separately if needed.
+
+**What You Get**: This dataset provides extracted features (MFCCs, spectral features, etc.) and metadata that can be used for machine learning research without requiring access to the original audio files.
+
+See `LICENSING.md` for complete licensing details.
+
+### Citation Information
+
+```bibtex
+@dataset{hemimy2025htmoneshotloop,
+  title={HTMOneShotLoopClassification: A Dataset of House, Tech House, and Minimal Techno Samples for Stem Category Classification},
+  author={Hemimy, Youssef},
+  year={2025},
+  publisher={Zenodo},
+  doi={10.5281/zenodo.17872191}
+}
+```
+
+### Contributions
+
+Contributions and feedback are welcome. Please see the repository for contribution guidelines.
+

LICENSE.txt

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metadata_schema.json

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+{
+  "dataset_name": "HTMOneShotLoopClassification",
+  "version": "1.0.0",
+  "description": "Metadata schema for HTMOneShotLoopClassification dataset features",
+  "total_features": 55,
+  "features": [
+    {
+      "name": "filename",
+      "type": "string",
+      "description": "Original audio filename"
+    },
+    {
+      "name": "stem",
+      "type": "categorical",
+      "description": "Target classification category (kick, snare, hihat, bass, synth, vocal, percussion, fx)",
+      "values": ["kick", "snare", "hihat", "bass", "synth", "vocal", "percussion", "fx"]
+    },
+    {
+      "name": "category",
+      "type": "categorical",
+      "description": "Sample type: 'one-shots' or 'loops'",
+      "values": ["one-shots", "loops"]
+    },
+    {
+      "name": "subfolder",
+      "type": "string",
+      "description": "Subfolder path within category (optional)"
+    },
+    {
+      "name": "duration",
+      "type": "float",
+      "description": "Audio duration in seconds",
+      "range": [0.0, "varies"]
+    },
+    {
+      "name": "sample_rate",
+      "type": "integer",
+      "description": "Audio sample rate in Hz",
+      "typical_value": 44100
+    },
+    {
+      "name": "rms",
+      "type": "float",
+      "description": "Root Mean Square energy (loudness measure)",
+      "range": [0.0, 1.0]
+    },
+    {
+      "name": "zcr",
+      "type": "float",
+      "description": "Zero Crossing Rate (noisiness measure)",
+      "range": [0.0, 1.0]
+    },
+    {
+      "name": "spectral_centroid",
+      "type": "float",
+      "description": "Spectral centroid in Hz (brightness measure)",
+      "range": [0.0, "varies"]
+    },
+    {
+      "name": "spectral_rolloff",
+      "type": "float",
+      "description": "Spectral rolloff frequency in Hz (high-frequency content)",
+      "range": [0.0, "varies"]
+    },
+    {
+      "name": "spectral_bandwidth",
+      "type": "float",
+      "description": "Spectral bandwidth in Hz (frequency spread)",
+      "range": [0.0, "varies"]
+    },
+    {
+      "name": "tempo",
+      "type": "float",
+      "description": "Detected tempo in BPM (0.0 if not detected)",
+      "range": [0.0, "varies"]
+    },
+    {
+      "name": "onset_count",
+      "type": "integer",
+      "description": "Number of detected onsets (transient events)",
+      "range": [0, "varies"]
+    },
+    {
+      "name": "pitch_mean",
+      "type": "float",
+      "description": "Mean fundamental frequency in Hz (0.0 if not detected)",
+      "range": [0.0, "varies"]
+    },
+    {
+      "name": "harmonic_ratio",
+      "type": "float",
+      "description": "Ratio of harmonic to total energy (tonal vs noisy)",
+      "range": [0.0, 1.0]
+    },
+    {
+      "name": "percussive_ratio",
+      "type": "float",
+      "description": "Ratio of percussive to total energy",
+      "range": [0.0, 1.0]
+    },
+    {
+      "name": "freq_sub_bass",
+      "type": "float",
+      "description": "Energy in sub-bass frequency band (20-60 Hz)",
+      "range": [0.0, 1.0]
+    },
+    {
+      "name": "freq_bass",
+      "type": "float",
+      "description": "Energy in bass frequency band (60-250 Hz)",
+      "range": [0.0, 1.0]
+    },
+    {
+      "name": "freq_low_mid",
+      "type": "float",
+      "description": "Energy in low-mid frequency band (250-2000 Hz)",
+      "range": [0.0, 1.0]
+    },
+    {
+      "name": "freq_high_mid",
+      "type": "float",
+      "description": "Energy in high-mid frequency band (2000-6000 Hz)",
+      "range": [0.0, 1.0]
+    },
+    {
+      "name": "freq_high",
+      "type": "float",
+      "description": "Energy in high frequency band (6000+ Hz)",
+      "range": [0.0, 1.0]
+    },
+    {
+      "name": "filepath",
+      "type": "string",
+      "description": "Full file path to original audio file"
+    },
+    {
+      "name": "spectral_contrast_1",
+      "type": "float",
+      "description": "Spectral contrast in frequency band 1",
+      "range": ["varies"]
+    },
+    {
+      "name": "spectral_contrast_2",
+      "type": "float",
+      "description": "Spectral contrast in frequency band 2",
+      "range": ["varies"]
+    },
+    {
+      "name": "spectral_contrast_3",
+      "type": "float",
+      "description": "Spectral contrast in frequency band 3",
+      "range": ["varies"]
+    },
+    {
+      "name": "spectral_contrast_4",
+      "type": "float",
+      "description": "Spectral contrast in frequency band 4",
+      "range": ["varies"]
+    },
+    {
+      "name": "spectral_contrast_5",
+      "type": "float",
+      "description": "Spectral contrast in frequency band 5",
+      "range": ["varies"]
+    },
+    {
+      "name": "spectral_contrast_6",
+      "type": "float",
+      "description": "Spectral contrast in frequency band 6",
+      "range": ["varies"]
+    },
+    {
+      "name": "spectral_contrast_7",
+      "type": "float",
+      "description": "Spectral contrast in frequency band 7",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_mean_1",
+      "type": "float",
+      "description": "Mean of Mel-Frequency Cepstral Coefficient 1",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_mean_2",
+      "type": "float",
+      "description": "Mean of Mel-Frequency Cepstral Coefficient 2",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_mean_3",
+      "type": "float",
+      "description": "Mean of Mel-Frequency Cepstral Coefficient 3",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_mean_4",
+      "type": "float",
+      "description": "Mean of Mel-Frequency Cepstral Coefficient 4",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_mean_5",
+      "type": "float",
+      "description": "Mean of Mel-Frequency Cepstral Coefficient 5",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_mean_6",
+      "type": "float",
+      "description": "Mean of Mel-Frequency Cepstral Coefficient 6",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_mean_7",
+      "type": "float",
+      "description": "Mean of Mel-Frequency Cepstral Coefficient 7",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_mean_8",
+      "type": "float",
+      "description": "Mean of Mel-Frequency Cepstral Coefficient 8",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_mean_9",
+      "type": "float",
+      "description": "Mean of Mel-Frequency Cepstral Coefficient 9",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_mean_10",
+      "type": "float",
+      "description": "Mean of Mel-Frequency Cepstral Coefficient 10",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_mean_11",
+      "type": "float",
+      "description": "Mean of Mel-Frequency Cepstral Coefficient 11",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_mean_12",
+      "type": "float",
+      "description": "Mean of Mel-Frequency Cepstral Coefficient 12",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_mean_13",
+      "type": "float",
+      "description": "Mean of Mel-Frequency Cepstral Coefficient 13",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_std_1",
+      "type": "float",
+      "description": "Standard deviation of Mel-Frequency Cepstral Coefficient 1",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_std_2",
+      "type": "float",
+      "description": "Standard deviation of Mel-Frequency Cepstral Coefficient 2",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_std_3",
+      "type": "float",
+      "description": "Standard deviation of Mel-Frequency Cepstral Coefficient 3",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_std_4",
+      "type": "float",
+      "description": "Standard deviation of Mel-Frequency Cepstral Coefficient 4",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_std_5",
+      "type": "float",
+      "description": "Standard deviation of Mel-Frequency Cepstral Coefficient 5",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_std_6",
+      "type": "float",
+      "description": "Standard deviation of Mel-Frequency Cepstral Coefficient 6",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_std_7",
+      "type": "float",
+      "description": "Standard deviation of Mel-Frequency Cepstral Coefficient 7",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_std_8",
+      "type": "float",
+      "description": "Standard deviation of Mel-Frequency Cepstral Coefficient 8",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_std_9",
+      "type": "float",
+      "description": "Standard deviation of Mel-Frequency Cepstral Coefficient 9",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_std_10",
+      "type": "float",
+      "description": "Standard deviation of Mel-Frequency Cepstral Coefficient 10",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_std_11",
+      "type": "float",
+      "description": "Standard deviation of Mel-Frequency Cepstral Coefficient 11",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_std_12",
+      "type": "float",
+      "description": "Standard deviation of Mel-Frequency Cepstral Coefficient 12",
+      "range": ["varies"]
+    },
+    {
+      "name": "mfcc_std_13",
+      "type": "float",
+      "description": "Standard deviation of Mel-Frequency Cepstral Coefficient 13",
+      "range": ["varies"]
+    }
+  ],
+  "extraction_method": "librosa",
+  "notes": "Features extracted using librosa audio analysis library. MFCCs computed with 13 coefficients. Spectral features computed from magnitude spectrogram. Frequency bands sum to 1.0 (normalized energy distribution)."
+}
+