# Frequency-Temporal Attention Network for Singing Melody Extraction

arXiv: https://arxiv.org/abs/2102.09763 · HTML: https://mlsanity.com/paper/2102.09763/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2021.

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

- [A Mamba-based Network for Semi-supervised Singing Melody Extraction Using Confidence Binary Regularization](https://mlsanity.com/paper/2505.08681/) (2025)

## Abstract (arXiv)

Musical audio is generally composed of three physical properties: frequency, time and magnitude. Interestingly, human auditory periphery also provides neural codes for each of these dimensions to perceive music. Inspired by these intrinsic characteristics, a frequency-temporal attention network is proposed to mimic human auditory for singing melody extraction. In particular, the proposed model contains frequency-temporal attention modules and a selective fusion module corresponding to these three physical properties. The frequency attention module is used to select the same activation frequency bands as did in cochlear and the temporal attention module is responsible for analyzing temporal patterns. Finally, the selective fusion module is suggested to recalibrate magnitudes and fuse the raw information for prediction. In addition, we propose to use another branch to simultaneously predict the presence of singing voice melody. The experimental results show that the proposed model outperforms existing state-of-the-art methods.