# Vocal melody extraction using patch-based CNN

arXiv: https://arxiv.org/abs/1804.09202 · HTML: https://mlsanity.com/paper/1804.09202/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2018.

## Built on by (most cited first, 6 of 6)

- [Omnizart: A General Toolbox for Automatic Music Transcription](https://mlsanity.com/paper/2106.00497/) (2021)
- [TONet: Tone-Octave Network for Singing Melody Extraction from Polyphonic Music](https://mlsanity.com/paper/2202.00951/) (2022)
- [Leveraging Symmetrical Convolutional Transformer Networks for Speech to Singing Voice Style Transfer](https://mlsanity.com/paper/2208.12410/) (2022)
- [Towards Improving Harmonic Sensitivity and Prediction Stability for Singing Melody Extraction](https://mlsanity.com/paper/2308.02723/) (2023)
- [Song Data Cleansing for End-to-End Neural Singer Diarization Using Neural Analysis and Synthesis Framework](https://mlsanity.com/paper/2406.16315/) (2024)
- [A Mamba-based Network for Semi-supervised Singing Melody Extraction Using Confidence Binary Regularization](https://mlsanity.com/paper/2505.08681/) (2025)

## Abstract (arXiv)

A patch-based convolutional neural network (CNN) model presented in this paper for vocal melody extraction in polyphonic music is inspired from object detection in image processing. The input of the model is a novel time-frequency representation which enhances the pitch contours and suppresses the harmonic components of a signal. This succinct data representation and the patch-based CNN model enable an efficient training process with limited labeled data. Experiments on various datasets show excellent speed and competitive accuracy comparing to other deep learning approaches.