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Emotion Recognition with librosa, MFCC, and MLP Objective To build a model to recognize emotion from speech using the librosa and sklearn libraries and the RAVDESS dataset. What is Speech Emotion Recognition? Speech Emotion Recognition, abbreviated as SER, is the act of attempting to recognize human emotion and affective states from speech. This is capitalizing on the fact that voice often reflects underlying emotion through tone and pitch. This is also the phenomenon that animals like dogs and horses employ to be able to understand human emotion. SER is tough because emotions are subjective and annotating audio is challenging. The Dataset You will use the RAVDESS dataset; this is the Ryerson Audio-Visual Database of Emotional Speech and Song dataset, and is free to download. This dataset has 7356 files rated by 247 individuals 10 times on emotional validity, intensity, and genuineness. Download the dataset from this link: Hints 1. Use the libraries librosa, soundfile, and sklearn to build a model using an Mult Layer Perception (MI.P) classifier. 2. You need to load the data, extract features from it using MFCC, then split the dataset into training and testing sets