The project involves audio feature extraction and sentiment classification using various ML models. Features (ZCR, Chroma_stft, MFCC, RMS, MelSpectrogram) are extracted from audio data. Data augmentation techniques (noise addition, stretching, pitching, shifting) increase data diversity.

Code prepares data by normalization and splitting. Models used: CNN, LSTM, BiLSTM, Wave2Vec. Models capture spatial patterns, temporal dynamics, long-range dependencies. Evaluation metrics assess performance.

Pipeline: audio feature extraction ➡️ data augmentation ➡️ sentiment classification. Final model choice depends on task, dataset, resources. #ML #AudioProcessing #SentimentAnalysis