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Richard Zhu commited on
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8b30617
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Parent(s): d4afb7f
fix folder structure
Browse files- chord_harp/Dockerfile → Dockerfile +0 -0
- chord_harp/app.py → app.py +0 -0
- chord_harp/.DS_Store +0 -0
- chord_harp/ChordEstimation +0 -1
- chord_harp/ChordRecognitionMIDITrainedExtractor +0 -1
- chord_harp/README.md +0 -21
- chord_harp/packages.txt → packages.txt +0 -0
- chord_harp/patch_madmom.py → patch_madmom.py +0 -0
- chord_harp/requirements.txt → requirements.txt +0 -0
chord_harp/Dockerfile → Dockerfile
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chord_harp/app.py → app.py
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chord_harp/.DS_Store
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chord_harp/ChordEstimation
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Subproject commit bdd05b2236130f01ca2875a30297f3f368fbf470
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chord_harp/ChordRecognitionMIDITrainedExtractor
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Subproject commit 19b81f694dae7782111b1be0e39e66f42fa1615f
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chord_harp/README.md
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---
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title: Automatic Chord Recognition
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emoji: 🎵
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colorFrom: blue
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colorTo: purple
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sdk: docker
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app_port: 7860
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---
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# Automatic Chord Recognition (PyHARP)
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This is a [PyHARP](https://github.com/TEAMuP-dev/pyharp) app that estimates chord progressions from audio using the model proposed in:
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> Wu, Y., & Li, W. (2019). Automatic Audio Chord Recognition With MIDI-Trained Deep Feature and BLSTM-CRF Sequence Decoding Model. *IEEE/ACM Transactions on Audio, Speech, and Language Processing*, 27(2), 355–366.
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## How it works
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1. Upload an audio file in HARP
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2. The model extracts a Harmonic CQT spectrogram
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3. A CNN extracts pitch class features (trained on MIDI data)
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4. A BLSTM-CRF decodes the chord label sequence
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5. Time-stamped chord labels are returned and displayed in HARP
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chord_harp/packages.txt → packages.txt
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chord_harp/patch_madmom.py → patch_madmom.py
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chord_harp/requirements.txt → requirements.txt
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