Audio Signal Processing for Machine Learning

Master key audio signal processing concepts essential for machine learning applications through this comprehensive 10-hour course. Learn to process raw audio data and extract meaningful features to power audio-driven AI systems. Begin with fundamental concepts including sound and waveforms, intensity, loudness, and timbre before diving into audio signal analysis for machine learning contexts. Explore various types of audio features and discover practical extraction techniques using Python. Develop proficiency in time domain audio features by implementing amplitude envelope extraction from scratch and learning to extract root-mean square energy and zero-crossing rate from audio signals. Gain deep understanding of the Fourier Transform through intuitive explanations and complex number applications in audio signal processing. Master the Discrete Fourier Transform and implement Fourier Transform extraction using Python libraries. Progress to advanced topics including Short-Time Fourier Transform and spectrogram extraction from audio data. Delve into Mel spectrograms and their practical implementation, followed by comprehensive coverage of Mel-Frequency Cepstral Coefficients (MFCCs) with hands-on Python extraction techniques. Conclude with frequency-domain audio features, implementing Band Energy Ratio from scratch and extracting spectral centroid and bandwidth using Python and Librosa library.