Audio Processing in Python with Feature Extraction for Machine Learning

Learn audio processing in Python using the librosa library for music and audio analysis in this comprehensive 44-minute tutorial. Explore essential concepts including core I/O, DSP, feature extraction, onset detection, beat and tempo analysis, spectrogram decomposition, temporal segmentation, and sequential modeling. Dive into practical implementations of audio processing techniques such as beats retrieval and generation, zero crossing rate, spectral centroid, spectral rolloff, MFCCs, chroma frequencies, and root-mean-square calculations. Follow along with step-by-step code examples and gain hands-on experience in building music information retrieval systems. By the end of this tutorial, you'll have a solid foundation in Python audio processing and feature extraction for machine learning applications.

- Video Start
- Content Introduction
- Python Audio processing resources
- Tutorial Source code intro
- Tutorial Starts
- Royalty free audio
- Audio processing with librosa
- Beats retrieval from audio
- Beats Generation
- Features Extraction
- Zero Crossing Rate
- Spectral Centroid
- Spectral Rolloff
- MFCCs
- Chroma Frequencies
- RMS Root-mean-square
- Code Push to GitHub
- Recap
- Credits