# Multitask Learning for Fundamental Frequency Estimation in Music

arXiv: https://arxiv.org/abs/1809.00381 · HTML: https://mlsanity.com/paper/1809.00381/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Pitch-Informed Instrument Assignment Using a Deep Convolutional Network with Multiple Kernel Shapes](https://mlsanity.com/paper/2107.13617/) (2021)

## Abstract (arXiv)

Fundamental frequency (f0) estimation from polyphonic music includes the tasks of multiple-f0, melody, vocal, and bass line estimation. Historically these problems have been approached separately, and only recently, using learning-based approaches. We present a multitask deep learning architecture that jointly estimates outputs for various tasks including multiple-f0, melody, vocal and bass line estimation, and is trained using a large, semi-automatically annotated dataset. We show that the multitask model outperforms its single-task counterparts, and explore the effect of various design decisions in our approach, and show that it performs better or at least competitively when compared against strong baseline methods.