instances/25_Speech_Emotion_Recognition_CNN_LSTM_RAVDESS_DL.json

{
    "name": "25_Speech_Emotion_Recognition_CNN_LSTM_RAVDESS_DL",
    "query": "I am seeking a speech emotion recognition project using a CNN-LSTM model with the RAVDESS dataset, which should be downloaded from Kaggle or [this Hugging Face link](https://huggingface.co/datasets/xbgoose/ravdess). The project should load the dataset and perform robust audio preprocessing (noise removal and normalization) and MFCC feature extraction, implemented in `src/data_loader.py`. The CNN-LSTM model should be implemented in `src/model.py`. Recognition accuracy should be saved in `results/metrics/recognition_accuracy.txt`, and a confusion matrix should be generated and saved as `results/figures/confusion_matrix.png`. Additionally, a user-friendly local API should be created using Flask to allow users to upload audio files and receive emotion recognition results, with the implementation included in `src/hci.py`.",
    "tags": [
        "Audio Processing",
        "Classification"
    ],
    "requirements": [
        {
            "requirement_id": 0,
            "prerequisites": [],
            "criteria": "The \"RAVDESS\" dataset is loaded in `src/data_loader.py`, which is downloaded from Kaggle or [this Hugging Face link](https://huggingface.co/datasets/xbgoose/ravdess).",
            "category": "Dataset or Environment",
            "satisfied": null
        },
        {
            "requirement_id": 1,
            "prerequisites": [
                0
            ],
            "criteria": "Audio preprocessing, including noise removal and normalization, is implemented in `src/data_loader.py`.",
            "category": "Data preprocessing and postprocessing",
            "satisfied": null
        },
        {
            "requirement_id": 2,
            "prerequisites": [
                0,
                1
            ],
            "criteria": "MFCC feature extraction is implemented in `src/data_loader.py`.",
            "category": "Data preprocessing and postprocessing",
            "satisfied": null
        },
        {
            "requirement_id": 3,
            "prerequisites": [],
            "criteria": "The \"CNN-LSTM\" model is implemented in `src/model.py`.",
            "category": "Machine Learning Method",
            "satisfied": null
        },
        {
            "requirement_id": 4,
            "prerequisites": [
                2,
                3
            ],
            "criteria": "Recognition accuracy is saved in `results/metrics/recognition_accuracy.txt`.",
            "category": "Performance Metrics",
            "satisfied": null
        },
        {
            "requirement_id": 5,
            "prerequisites": [
                2,
                3,
                4
            ],
            "criteria": "The confusion matrix is generated and saved as `results/figures/confusion_matrix.png`.",
            "category": "Visualization",
            "satisfied": null
        },
        {
            "requirement_id": 6,
            "prerequisites": [
                2,
                3
            ],
            "criteria": "A local API is created using \"Flask\" to allow users to upload audio files and receive emotion recognition results. The implementation should be included in `src/hci.py`.",
            "category": "Human Computer Interaction",
            "satisfied": null
        }
    ],
    "preferences": [
        {
            "preference_id": 0,
            "criteria": "The audio preprocessing step should be robust, effectively reducing noise while preserving the integrity of the speech signals.",
            "satisfied": null
        },
        {
            "preference_id": 1,
            "criteria": "The local API should be user-friendly, with clear instructions for uploading files and interpreting results.",
            "satisfied": null
        }
    ],
    "is_kaggle_api_needed": true,
    "is_training_needed": true,
    "is_web_navigation_needed": true
}