Music Genre Recognition Lecture
Acoustic digital signals are ubiquitous and have many applications in several disciplines, notably in:
-Digital Media, Social Media (music, voice signals),
-Biomedical Signal Analysis and Diagnosis,
-Scientific signal acquisition of any sort, e.g., Environment Sensing, Geophysical Prospecting.
Text can be considered as 1D signal, as it evolves over time (actually as an order time series of letters or words). Its analysis is extremely important in social media applications (e.g., Tweet analysis), in the analysis of any written and/or broadcasted text (e.g., news articles in newspapers) and in literary text analysis.
This lecture overviews Music Genre Recognition that has many applications in the music industry and in the social/broadcasted media. It covers the following topics in detail: Audio Feature Extraction. Music Spectrograms. Sound Texture Selection. Machine Learning Algorithms. Gaussian Processes. Support Vector Machines. Music Recognition using Deep Neural Networks.