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{
"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
} |