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--- |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: label |
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dtype: |
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class_label: |
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names: |
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'0': afro |
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'1': classical |
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'2': country |
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'3': disco |
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'4': electro |
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'5': jazz |
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'6': latin |
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'7': metal |
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'8': pop |
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'9': rap |
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'10': reggae |
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'11': rock |
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splits: |
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- name: train |
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num_bytes: 128963338.5857826 |
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num_examples: 1697 |
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- name: test |
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num_bytes: 14256351.565217393 |
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num_examples: 189 |
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download_size: 143291941 |
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dataset_size: 143219690.151 |
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license: mit |
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task_categories: |
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- image-classification |
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tags: |
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- music |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Dataset Card |
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The egtzan_plus dataset is an GTZAN like dataset for musical genre classification in the vision domain. |
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In egtzan_plus, new classes such as Electro and Afro have been added to the original GTZAN dataset. Each audio track (30s) is transformed into a Mel-frequency spectrogram using Librosa: |
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```python |
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# Mel-frequency spectrogram generation |
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y, sr = librosa.load(audio_file) |
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ms = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128, fmax=8000) |
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log_ms = librosa.power_to_db(ms, ref=np.max) |
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librosa.display.specshow(log_ms) |
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``` |
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The dataset contains the following classes: |
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- Afro |
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- Classical |
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- Country |
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- Disco |
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- Electro |
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- Jazz |
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- Latin |
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- Metal |
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- Pop |
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- Rap |
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- Reggae |
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- Rock |
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The dataset is split into train and test sets as follows: |
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- Train: 1697 examples |
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- Test: 189 examples |
