{
    "name": "mood sad",
    "type": "multi-class classifier",
    "link": "https://essentia.upf.edu/models/classifiers/mood_sad/mood_sad-musicnn-msd-2.pb",
    "version": "1",
    "description": "classification of music by mood (sad/non-sad)",
    "author": "Pablo Alonso",
    "email": "pablo.alonso@upf.edu",
    "release_date": "2020-07-07",
    "framework": "tensorflow",
    "framework_version": "1.15.0",
    "classes": [
        "non_sad",
        "sad"
    ],
    "model_types": [
        "frozen_model"
    ],
    "dataset": {
        "name": "In-house MTG collection",
        "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}",
        "size": "230 full tracks + excerpts, 96/134 per class",
        "metrics": {
            "5-fold_cross_validation_normalized_accuracy": 0.86
        }
    },
    "schema": {
        "inputs": [
            {
                "name": "model/Placeholder",
                "type": "float",
                "shape": [
                    187,
                    96
                ]
            }
        ],
        "outputs": [
            {
                "name": "model/Sigmoid",
                "type": "float",
                "shape": [
                    1,
                    2
                ],
                "op": "Sigmoid",
                "output_purpose": "predictions"
            },
            {
                "name": "model/dense_2/BiasAdd",
                "type": "float",
                "shape": [
                    1,
                    2
                ],
                "op": "fully connected",
                "description": "logits",
                "output_purpose": ""
            },
            {
                "name": "model/dense_1/BiasAdd",
                "type": "float",
                "shape": [
                    1,
                    100
                ],
                "op": "fully connected",
                "description": "penultimate layer",
                "output_purpose": ""
            },
            {
                "name": "model/dense/BiasAdd",
                "type": "float",
                "shape": [
                    1,
                    200
                ],
                "op": "fully connected",
                "output_purpose": "embeddings"
            }
        ]
    },
    "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}",
    "inference": {
        "sample_rate": 16000,
        "algorithm": "TensorflowPredictMusiCNN"
    }
}