# A Novel Audio Representation for Music Genre Identification in MIR

arXiv: https://arxiv.org/abs/2404.01058 · HTML: https://mlsanity.com/paper/2404.01058/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [MLOps: A Review](https://mlsanity.com/paper/2308.10908/) (2023)
- [From Simulations to Reality: Enhancing Multi-Robot Exploration for Urban Search and Rescue](https://mlsanity.com/paper/2311.16958/) (2023)
- [Roulette-Wheel Selection-Based PSO Algorithm for Solving the Vehicle Routing Problem with Time Windows](https://mlsanity.com/paper/2306.02308/) (2023)
- [Detection of a facemask in real-time using deep learning methods: Prevention of Covid 19](https://mlsanity.com/paper/2401.15675/) (2024)
- [Enhancing End-to-End Multi-Task Dialogue Systems: A Study on Intrinsic Motivation Reinforcement Learning Algorithms for Improved Training and Adaptability](https://mlsanity.com/paper/2401.18040/) (2024)
- [Advancing Audio Fingerprinting Accuracy Addressing Background Noise and Distortion Challenges](https://mlsanity.com/paper/2402.13957/) (2024)

## Built on by (most cited first, 2 of 2)

- [Optimizing Photometric Light Curve Analysis: Evaluating Scipy's Minimize Function for Eclipse Mapping of Cataclysmic Variables](https://mlsanity.com/paper/2406.00071/) (2024)
- [SonicVerse: Multi-Task Learning for Music Feature-Informed Captioning](https://mlsanity.com/paper/2506.15154/) (2025)

## Abstract (arXiv)

For Music Information Retrieval downstream tasks, the most common audio representation is time-frequency-based, such as Mel spectrograms. In order to identify musical genres, this study explores the possibilities of a new form of audio representation one of the most usual MIR downstream tasks. Therefore, to discretely encoding music using deep vector quantization; a novel audio representation was created for the innovative generative music model i.e. Jukebox. The effectiveness of Jukebox's audio representation is compared to Mel spectrograms using a dataset that is almost equivalent to State-of-the-Art (SOTA) and an almost same transformer design. The results of this study imply that, at least when the transformers are pretrained using a very modest dataset of 20k tracks, Jukebox's audio representation is not superior to Mel spectrograms. This could be explained by the fact that Jukebox's audio representation does not sufficiently take into account the peculiarities of human hearing perception. On the other hand, Mel spectrograms are specifically created with the human auditory sense in mind.