Explore audio analytics using Azure Automated ML in this 27-minute video from Microsoft's AI Show. Learn about extracting audio signal information into spectrograms and building custom vision models with Azure. Discover how to use Fast Fourier Transform (FFT) to convert time domain signals to frequency domain signals, and understand spectrograms showing frequency variations over time. Follow along with demos on music genre classification, creating chromagrams, and generating spectrograms. Dive into using Automated ML for image-based music genre classification and calling AutoML CV models. Gain insights into audio processing techniques and their applications in AI and machine learning.
Audio Analytics with Azure Automated ML for Music Genre Classification
Overview
Syllabus
Welcome to the AI Show.
Intro to Audio analytics with Azure Automated ML.
Fast Fourier Transform (FFT) - Time Domain Signal to Frequency Domain Signal.
Spectogram of how the spectrum of of frequencies vary over time.
Demo - Music Genre Classification.
Creating a Chromagram.
Spectograms generation.
Music Genre Classification with Automated ML for Images.
Calling AutoML CV Model.
Demo 2.
Learn more.
Taught by
Microsoft Developer
