# A Mamba-based Network for Semi-supervised Singing Melody Extraction Using Confidence Binary Regularization

arXiv: https://arxiv.org/abs/2505.08681 · HTML: https://mlsanity.com/paper/2505.08681/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Speech and audio SSMs, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 13 May 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Speech and audio SSMs](https://mlsanity.com/ssm/speech-and-audio-ssms/)

## Builds on (most cited first, 9 of 9)

- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [FMA: A Dataset For Music Analysis](https://mlsanity.com/paper/1612.01840/) (2016)
- [SpecTNT: a Time-Frequency Transformer for Music Audio](https://mlsanity.com/paper/2110.09127/) (2021)
- [Vocal melody extraction using patch-based CNN](https://mlsanity.com/paper/1804.09202/) (2018)
- [A Streamlined Encoder/Decoder Architecture for Melody Extraction](https://mlsanity.com/paper/1810.12947/) (2018)
- [AllMatch: Exploiting All Unlabeled Data for Semi-Supervised Learning](https://mlsanity.com/paper/2406.15763/) (2024)
- [TONet: Tone-Octave Network for Singing Melody Extraction from Polyphonic Music](https://mlsanity.com/paper/2202.00951/) (2022)
- [Between Homomorphic Signal Processing and Deep Neural Networks: Constructing Deep Algorithms for Polyphonic Music Transcription](https://mlsanity.com/paper/1706.08231/) (2017)
- [Frequency-Temporal Attention Network for Singing Melody Extraction](https://mlsanity.com/paper/2102.09763/) (2021)

## Abstract (arXiv)

Singing melody extraction (SME) is a key task in the field of music information retrieval. However, existing methods are facing several limitations: firstly, prior models use transformers to capture the contextual dependencies, which requires quadratic computation resulting in low efficiency in the inference stage. Secondly, prior works typically rely on frequencysupervised methods to estimate the fundamental frequency (f0), which ignores that the musical performance is actually based on notes. Thirdly, transformers typically require large amounts of labeled data to achieve optimal performances, but the SME task lacks of sufficient annotated data. To address these issues, in this paper, we propose a mamba-based network, called SpectMamba, for semi-supervised singing melody extraction using confidence binary regularization. In particular, we begin by introducing vision mamba to achieve computational linear complexity. Then, we propose a novel note-f0 decoder that allows the model to better mimic the musical performance. Further, to alleviate the scarcity of the labeled data, we introduce a confidence binary regularization (CBR) module to leverage the unlabeled data by maximizing the probability of the correct classes. The proposed method is evaluated on several public datasets and the conducted experiments demonstrate the effectiveness of our proposed method.