06-29, 10:30–10:50 (Asia/Jerusalem), PyData
An enriching talk about music theory and analysis using python tools.
Have you ever noticed that, depending on your mood, not every song is suitable for you at a given time? And how about songs that change your mood while listening to them? For instance, Queen’s “Don’t Stop Me Now”, voted as the most feel-good song of all times, seems to make people pretty happy. If you wondered why this happens, this talk is for you!
We will walk through songs’ features that influence our mood based on the acoustic parameters, such as tempo and loudness. We will use signal processing tools written in Python to analyze it. We will also learn about compositional and lyrical features which help us decide if a song is “happy” or “sad” (or a mix of both).
After this talk, you will know some basic terms in signal processing and music. You then will understand, from the perspective of a signal processing engineer, what are the musical features which arouse your feelings.
English
Target audience –Data Scientists
Noga Karni is a Signal Processing and Machine Learning engineer at MyPart. She fell in love with audio signals and is researching musical production features’ extraction, using audio signal processing techniques, and incorporation into ML models. Noga Karni holds a Bachelor's degree in Electrical and Computer Engineering from Ben Gurion University. In her free time she teaches swimming.