Event Type
Research Presentation
Academic Department
Mathematics and Statistics
Location
Dana Science Building, 2nd floor
Start Date
25-4-2025 1:00 PM
End Date
25-4-2025 2:30 PM
Description
Under the direction of Dr. Giancarlo Schrementi
Music genres are typically categorized based on auditory characteristics, but some genres, such as sleep, chill, ambient, and study music, share striking similarities. This paper aims to provide insights into the mathematical differences underlying genre distinctions. Initial exploratory data analysis is performed on a dataset from Spo- tify that contains numeric measures for tracks across these four genres and statistical differences are noted. Three classification models, a discriminative model (Logistic Re- gression) and two generative models (Linear Discriminant Analysis and Naive Bayes) are trained and then used to predict the genre of novel tracks. All the models are able to distinguish the genres, but have different patterns of error. These modeling results demonstrate that there is a measurable mathematical difference between the genres.
Discriminative VS Generative Classification Models : Classify Music Genres In A Spotify Dataset
Dana Science Building, 2nd floor
Under the direction of Dr. Giancarlo Schrementi
Music genres are typically categorized based on auditory characteristics, but some genres, such as sleep, chill, ambient, and study music, share striking similarities. This paper aims to provide insights into the mathematical differences underlying genre distinctions. Initial exploratory data analysis is performed on a dataset from Spo- tify that contains numeric measures for tracks across these four genres and statistical differences are noted. Three classification models, a discriminative model (Logistic Re- gression) and two generative models (Linear Discriminant Analysis and Naive Bayes) are trained and then used to predict the genre of novel tracks. All the models are able to distinguish the genres, but have different patterns of error. These modeling results demonstrate that there is a measurable mathematical difference between the genres.