Music classification by genre, era, and mood has become an interesting area of research in music and audio processing. By identifying the genre, era, and mood of music, we can provide music tailored to the user's preferences. To recognize music types, we need features that represent the music itself, such as spectrograms, zero-crossing rates, bandwidth, and others. We also need machine learning or deep learning models that can perform feature extraction and classification. Python supports all the tools we need to classify music, from feature extraction and preprocessing to classification and evaluation. Libraries like Librosa, Scikit-learn, and TensorFlow enable programmers to create code for music classification tasks.
In this talk, I would like to share how to conduct research in music classification from start to finish, how to write code for music classification using Python, and how to write a conference paper about music classification.
Speaker
Octaviano Pratama
I am currently establishing Edutech Startup in Indonesia which hold position as a Director. My specific research interests are in Machine Learning, Deep Learning, Music Retrieval, Computer Science and Programming. I Use only Python Programming as a tools for research and my startup. I graduated from Universitas Indonesia both bachelor and master degree.