88 lines
3.0 KiB
JSON
88 lines
3.0 KiB
JSON
{
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"name": "mood aggressive",
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"type": "multi-class classifier",
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"link": "https://essentia.upf.edu/models/classifiers/mood_aggressive/mood_aggressive-musicnn-mtt-2.pb",
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"version": "1",
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"description": "classification of music by mood (aggressive/non-aggressive)",
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"author": "Pablo Alonso",
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"email": "pablo.alonso@upf.edu",
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"release_date": "2020-07-07",
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"framework": "tensorflow",
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"framework_version": "1.15.0",
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"classes": [
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"aggressive",
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"not_aggressive"
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],
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"model_types": [
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"frozen_model"
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],
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"dataset": {
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"name": "In-house MTG collection ",
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"citation": "@inproceedings{laurier2009music,\n title={Music mood annotator design and integration},\n author={Laurier, Cyril and Meyers, Owen and Serra, Joan and Blech, Martin and Herrera, Perfecto},\n booktitle={2009 Seventh International Workshop on Content-Based Multimedia Indexing},\n pages={156--161},\n year={2009},\n organization={IEEE}\n}",
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"size": "280 full tracks + excerpts, 133/147 per class",
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"metrics": {
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"5-fold_cross_validation_normalized_accuracy": 0.96
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}
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},
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"schema": {
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"inputs": [
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{
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"name": "model/Placeholder",
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"type": "float",
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"shape": [
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187,
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96
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]
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}
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],
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"outputs": [
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{
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"name": "model/Sigmoid",
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"type": "float",
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"shape": [
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1,
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2
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],
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"op": "Sigmoid",
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"output_purpose": "predictions"
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},
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{
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"name": "model/dense_2/BiasAdd",
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"type": "float",
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"shape": [
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1,
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2
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],
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"op": "fully connected",
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"description": "logits",
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"output_purpose": ""
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},
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{
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"name": "model/dense_1/BiasAdd",
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"type": "float",
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"shape": [
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1,
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100
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],
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"op": "fully connected",
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"description": "penultimate layer",
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"output_purpose": ""
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},
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{
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"name": "model/dense/BiasAdd",
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"type": "float",
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"shape": [
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1,
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200
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],
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"op": "fully connected",
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"output_purpose": "embeddings"
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}
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]
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},
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"citation": "@inproceedings{alonso2020tensorflow,\n title={Tensorflow Audio Models in Essentia},\n author={Alonso-Jim{\\'e}nez, Pablo and Bogdanov, Dmitry and Pons, Jordi and Serra, Xavier},\n booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},\n year={2020}\n}",
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"inference": {
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"sample_rate": 16000,
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"algorithm": "TensorflowPredictMusiCNN"
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}
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} |