Speech emotion detection using Catboost model trained by labelling LPC, MFCC, Pitch features of speech from a emotion classified speech dataset

I want to develop code to do emotion detection on speech by training a model by pre-processes the test voice signal, extract selective MFCC, LPC, pitch, and voice quality features, then classify the speech using Catboost. Step 1. Read each file with different sentiments and languages Step 2. Filter the voices. We removed blanks Step 3. Extract LPC and MFCC features. Calculate Pitch. Club them all as features Step 4. Features satisfying a threshold are retained Step 5. Assign labels to features as per the sentiment in the voice Step 6. Design a Catboost model having relevant parameters Step 7. Using features and labels train the model Step 8. To predict the sentiment Take a new voice for output Repeat steps 2 through 4 Run the model with the new features to predict the sentiment Use Catboost to extract the categorical features and to predict the sentiments from voices. A detailed description is there in the attached research paper. All these algortithms have to be implemented exactly as described in the research paper. Training datasets 1. RAVDESS dataset [login to view URL] 2. TESS dataset [login to view URL] 3. Emo-DB Dataset [login to view URL] Deliverables:- A simple web page should be created where:- 1. A new dataset can be uploaded and the Catboost model can be trained using that dataset. 2. A speech audio is uploaded and the model predicts the emotion. 3. Full detailed documentation explaining every line of code.