gtzan
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Dataset terdiri dari 1000 trek audio yang masing-masing berdurasi 30 detik. Ini berisi 10 genre, masing-masing diwakili oleh 100 lagu. Treknya adalah semua file audio Mono 16-bit 22050Hz dalam format .wav.
Genre tersebut adalah:
Membelah | Contoh |
---|
'train' | 1.000 |
FeaturesDict({
'audio': Audio(shape=(None,), dtype=int64),
'audio/filename': Text(shape=(), dtype=string),
'label': ClassLabel(shape=(), dtype=int64, num_classes=10),
})
Fitur | Kelas | Membentuk | Dtype | Keterangan |
---|
| fiturDict | | | |
audio | Audio | (Tidak ada,) | int64 | |
audio/nama file | Teks | | rangkaian | |
label | LabelKelas | | int64 | |
@misc{tzanetakis_essl_cook_2001,
author = "Tzanetakis, George and Essl, Georg and Cook, Perry",
title = "Automatic Musical Genre Classification Of Audio Signals",
url = "http://ismir2001.ismir.net/pdf/tzanetakis.pdf",
publisher = "The International Society for Music Information Retrieval",
year = "2001"
}
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Terakhir diperbarui pada 2022-12-06 UTC.
[null,null,["Terakhir diperbarui pada 2022-12-06 UTC."],[],[],null,["# gtzan\n\n\u003cbr /\u003e\n\n- **Description**:\n\nThe dataset consists of 1000 audio tracks each 30 seconds long. It contains 10\ngenres, each represented by 100 tracks. The tracks are all 22050Hz Mono 16-bit\naudio files in .wav format.\n\nThe genres are:\n\n- blues\n- classical\n- country\n- disco\n- hiphop\n- jazz\n- metal\n- pop\n- reggae\n- rock\n\n- **Additional Documentation** :\n [Explore on Papers With Code\n north_east](https://paperswithcode.com/dataset/gtzan)\n\n- **Homepage** :\n \u003chttp://marsyas.info/index.html\u003e\n\n- **Source code** :\n [`tfds.audio.gtzan.GTZAN`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/audio/gtzan/gtzan.py)\n\n- **Versions**:\n\n - **`1.0.0`** (default): No release notes.\n- **Download size** : `1.14 GiB`\n\n- **Dataset size** : `3.71 GiB`\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n No\n\n- **Splits**:\n\n| Split | Examples |\n|-----------|----------|\n| `'train'` | 1,000 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'audio': Audio(shape=(None,), dtype=int64),\n 'audio/filename': Text(shape=(), dtype=string),\n 'label': ClassLabel(shape=(), dtype=int64, num_classes=10),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|----------------|--------------|---------|--------|-------------|\n| | FeaturesDict | | | |\n| audio | Audio | (None,) | int64 | |\n| audio/filename | Text | | string | |\n| label | ClassLabel | | int64 | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `('audio', 'label')`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n Not supported.\n\n- **Examples**\n ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\n- **Citation**:\n\n @misc{tzanetakis_essl_cook_2001,\n author = \"Tzanetakis, George and Essl, Georg and Cook, Perry\",\n title = \"Automatic Musical Genre Classification Of Audio Signals\",\n url = \"http://ismir2001.ismir.net/pdf/tzanetakis.pdf\",\n publisher = \"The International Society for Music Information Retrieval\",\n year = \"2001\"\n }"]]