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  "source_url": "https://www.kaggle.com/competitions/audio-speech-emotion-classification/data",
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      "content": "**TESS Dataset**\n\nThe first dataset chosen for this mini-project is the TESS (Toronto emotional speech set) dataset. It contains 2880 files. A set of 200 target words were spoken in the carrier phrase \"Say the word _____' by two actresses and the sets were recorded in seven different emotions (anger, disgust, fear, happiness, pleasant surprise, sadness, and neutral). Both actresses spoke English as their first language, were university educated, and had musical training. Audiometric testing indicated that both actresses had thresholds within the normal range.\n\n**Ravdess Dataset**\n\nThe second dataset chosen for this mini-project is Ravdess (The Ryerson Audio-Visual Database of Emotional Speech and Song). This dataset contains 1440 files: 60 trials per actor x 24 actors = 1440. The RAVDESS contains 24 professional actors (12 female, 12 male), vocalizing two lexically-matched statements in a neutral North American accent. Speech emotions includes calm, happy, sad, angry, fearful, surprise, and disgust expressions. Each expression is produced at two levels of emotional intensity (normal, strong), with an additional neutral expression.\n\nFile naming convention\n\nEach of the 1440 files has a unique filename. The file name consists of a 7-part numerical identifier (e.g., 03-01-06-01-02-01-12.wav). These identifiers define the stimulus characteristics:\n\nFilename identifiers\n\nModality (01 = full-AV, 02 = video-only, 03 = audio-only).\nVocal channel (01 = speech, 02 = song).\nEmotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).\nEmotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion.\nStatement (01 = \"Kids are talking by the door\", 02 = \"Dogs are sitting by the door\").\nRepetition (01 = 1st repetition, 02 = 2nd repetition).\nActor (01 to 24. Odd numbered actors are male, even numbered actors are female).\nFilename example: 03-01-06-01-02-01-12.wav\n\n- Audio-only - 03\n- Speech - 01\n- Fearful - 06\n- Normal intensity - 01\n- Statement \"dogs\" - 02\n- 1st Repetition - 01\n- 12th Actor - 12 Female, as the actor ID number is even.",
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      "name": "Description",
      "content": "**Problem Statement**\n\nBuild a model to recognize emotion from speech using Ensemble learning\n\nFollow the instructions given in the notebook, download the test data from this kaggle competition and submit your predictions in the form of a `submission.csv` file. Refer to the kaggle document provided, for the submission process.\n\nAfter submission of your predictions, the mean F-score will reflect on the leader board.\n\n**Learning Objectives**\n\n- Extract the features from audio data\n- Implement ML classification algorithms individually and as Ensembles, to classify emotions\n- Record the voice sample and test it with trained model",
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      "name": "Evaluation",
      "content": "Kaggle calculates the mean F-score based on comparison of your model predictions with the ground truth.\n\nThe evaluation metric for this competition is Mean F1-Score. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives tp to all predicted positives tp+fp. Recall is the ratio of true positives to all actual positives tp+fn. The F1 score is given by:\n\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]\nThe F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.",
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    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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