# Music Genre Classification using Machine Learning Techniques

arXiv: https://arxiv.org/abs/1804.01149 · HTML: https://mlsanity.com/paper/1804.01149/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2018.

## Built on by (most cited first, 7 of 7)

- [Learning Music Sequence Representation from Text Supervision](https://mlsanity.com/paper/2305.19602/) (2023)
- [Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming](https://mlsanity.com/paper/2210.01515/) (2022)
- [Enhancing Music Genre Classification through Multi-Algorithm Analysis and User-Friendly Visualization](https://mlsanity.com/paper/2405.17413/) (2024)
- [Music Genre Classification with ResNet and Bi-GRU Using Visual Spectrograms](https://mlsanity.com/paper/2307.10773/) (2023)
- [Audio Processing using Pattern Recognition for Music Genre Classification](https://mlsanity.com/paper/2410.14990/) (2024)
- [Music Genre Classification Using Machine Learning Techniques](https://mlsanity.com/paper/2509.01762/) (2025)
- [Bangla Music Genre Classification Using Bidirectional LSTMS](https://mlsanity.com/paper/2601.15083/) (2026)

## Abstract (arXiv)

Categorizing music files according to their genre is a challenging task in the area of music information retrieval (MIR). In this study, we compare the performance of two classes of models. The first is a deep learning approach wherein a CNN model is trained end-to-end, to predict the genre label of an audio signal, solely using its spectrogram. The second approach utilizes hand-crafted features, both from the time domain and the frequency domain. We train four traditional machine learning classifiers with these features and compare their performance. The features that contribute the most towards this multi-class classification task are identified. The experiments are conducted on the Audio set data set and we report an AUC value of 0.894 for an ensemble classifier which combines the two proposed approaches.