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

2025-05-12

AI Summary

To address the challenge of predicting music popularity in the streaming era, this paper proposes an end-to-end deep learning framework that fuses audio and multi-source platform features. Methodologically, it introduces the first joint modeling of STFT spectrograms—capturing acoustic characteristics—and Spotify’s structured features—including metadata and user interaction signals—via a convolutional neural network for cross-modal feature fusion and discriminative pattern learning. The model achieves robust generalization across genres and time periods, overcoming the limitations of conventional unimodal approaches. Evaluated on a large-scale, multi-genre dataset, it attains an F1 score of 97%, significantly outperforming state-of-the-art baselines. This work offers both theoretical innovation—through principled multimodal integration—and practical applicability, delivering an interpretable, production-ready solution for intelligent music recommendation and artist-and-repertoire (A&R) decision support.

Technology Category

• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Predict music track popularity using CNNs and Spotify features
• Analyze spectrogram and metadata to forecast music success
• Evaluate model effectiveness across genres and demographics

Innovation

• CNN predicts music popularity using Spotify features
• Analyzes spectrogram and metadata for pattern recognition
• Achieves 97% F1 score across diverse genres