Predicting Music Track Popularity by Convolutional Neural Networks on Spotify Features and Spectrogram of Audio Waveform

TL;DR

This paper presents a CNN-based model that leverages Spotify features and spectrogram data to accurately predict music track popularity, achieving a 97% F1 score across diverse genres and demographics.

Contribution

It introduces a novel CNN approach utilizing Spotify data and spectrogram analysis for music popularity prediction, demonstrating high accuracy and adaptability.

Findings

Achieved 97% F1 score in popularity prediction

Effective across various genres and demographics

Provides industry-relevant predictive insights

Abstract

In the digital streaming landscape, it's becoming increasingly challenging for artists and industry experts to predict the success of music tracks. This study introduces a pioneering methodology that uses Convolutional Neural Networks (CNNs) and Spotify data analysis to forecast the popularity of music tracks. Our approach takes advantage of Spotify's wide range of features, including acoustic attributes based on the spectrogram of audio waveform, metadata, and user engagement metrics, to capture the complex patterns and relationships that influence a track's popularity. Using a large dataset covering various genres and demographics, our CNN-based model shows impressive effectiveness in predicting the popularity of music tracks. Additionally, we've conducted extensive experiments to assess the strength and adaptability of our model across different musical styles and time periods, with promising results yielding a 97\% F1 score. Our study not only offers valuable insights into the dynamic landscape of digital music consumption but also provides the music industry with advanced predictive tools for assessing and predicting the success of music tracks.