Audio Data Feature Extraction for Speaker Diarization

In many applications, such as transcription, speaker identification, and audio indexing, speaker diarization—the process of dividing audio data into different speaker segments—is crucial. The speaker diarization algorithms for audio data utilising the Python Librosa module are the main topic of this research study. A strong Python library for analysing audio and music is called Librosa. It offers a number of features for extracting audio features, including MFCC, Spectrogram, Chromagram, and Spectral Contrast. This study offers helpful advice on how to extract features from audio data.