# Revisiting the problem of audio-based hit song prediction using convolutional neural networks

arXiv: https://arxiv.org/abs/1704.01280 · HTML: https://mlsanity.com/paper/1704.01280/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2017.

## Built on by (most cited first, 4 of 4)

- [VPN: Video Provenance Network for Robust Content Attribution](https://mlsanity.com/paper/2109.10038/) (2021)
- [SpotHitPy: A Study For ML-Based Song Hit Prediction Using Spotify](https://mlsanity.com/paper/2301.07978/) (2023)
- [Music Era Recognition Using Supervised Contrastive Learning and Artist Information](https://mlsanity.com/paper/2407.05368/) (2024)
- [APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music](https://mlsanity.com/paper/2605.03395/) (2026)

## Abstract (arXiv)

Being able to predict whether a song can be a hit has impor- tant applications in the music industry. Although it is true that the popularity of a song can be greatly affected by exter- nal factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on its own. Motivated by the recent success of deep learning techniques, we attempt to ex- tend previous work on hit song prediction by jointly learning the audio features and prediction models using deep learning. Specifically, we experiment with a convolutional neural net- work model that takes the primitive mel-spectrogram as the input for feature learning, a more advanced JYnet model that uses an external song dataset for supervised pre-training and auto-tagging, and the combination of these two models. We also consider the inception model to characterize audio infor- mation in different scales. Our experiments suggest that deep structures are indeed more accurate than shallow structures in predicting the popularity of either Chinese or Western Pop songs in Taiwan. We also use the tags predicted by JYnet to gain insights into the result of different models.