# Musical Tempo and Key Estimation using Convolutional Neural Networks with Directional Filters

arXiv: https://arxiv.org/abs/1903.10839 · HTML: https://mlsanity.com/paper/1903.10839/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2019.

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

- [LLark: A Multimodal Instruction-Following Language Model for Music](https://mlsanity.com/paper/2310.07160/) (2023)
- [MIRFLEX: Music Information Retrieval Feature Library for Extraction](https://mlsanity.com/paper/2411.00469/) (2024)
- [Music Tempo Estimation via Neural Networks -- A Comparative Analysis](https://mlsanity.com/paper/2107.09208/) (2021)
- [Automatic Embedding of Stories Into Collections of Independent Media](https://mlsanity.com/paper/2111.02216/) (2021)

## Abstract (arXiv)

In this article we explore how the different semantics of spectrograms' time and frequency axes can be exploited for musical tempo and key estimation using Convolutional Neural Networks (CNN). By addressing both tasks with the same network architectures ranging from shallow, domain-specific approaches to deep variants with directional filters, we show that axis-aligned architectures perform similarly well as common VGG-style networks developed for computer vision, while being less vulnerable to confounding factors and requiring fewer model parameters.