# BioSEN: A Bio-acoustic Signal Enhancement Network for Animal Vocalizations

arXiv: https://arxiv.org/abs/2605.12534 · HTML: https://mlsanity.com/paper/2605.12534/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 66 references that are in the map.
- First posted on arXiv 2 May 2026.

## Builds on (most cited first, 14 of 14)

- [Real Time Speech Enhancement in the Waveform Domain](https://mlsanity.com/paper/2006.12847/) (2020)
- [DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement](https://mlsanity.com/paper/2008.00264/) (2020)
- [Computational bioacoustics with deep learning: a review and roadmap](https://mlsanity.com/paper/2112.06725/) (2021)
- [FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement](https://mlsanity.com/paper/2203.12188/) (2022)
- [LiSenNet: Lightweight Sub-band and Dual-Path Modeling for Real-Time Speech Enhancement](https://mlsanity.com/paper/2409.13285/) (2024)
- [Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics Processing](https://mlsanity.com/paper/2501.05987/) (2025)
- [Wind-robust sound event detection and denoising for bioacoustics](https://mlsanity.com/paper/2110.05632/) (2021)
- [MPSA-DenseNet: A novel deep learning model for English accent classification](https://mlsanity.com/paper/2306.08798/) (2023)
- [Deep learning-based method for weather forecasting: A case study in Itoshima](https://mlsanity.com/paper/2403.14918/) (2024)
- [Towards Sub-millisecond Latency Real-Time Speech Enhancement Models on Hearables](https://mlsanity.com/paper/2409.18239/) (2024)
- [Stochastic forest transition model dynamics and parameter estimation via deep learning](https://mlsanity.com/paper/2507.21486/) (2025)
- [Towards High-Fidelity and Controllable Bioacoustic Generation via Enhanced Diffusion Learning](https://mlsanity.com/paper/2509.00318/) (2025)
- [Biodenoising: Animal Vocalization Denoising without Access to Clean Data](https://mlsanity.com/paper/2410.03427/) (2024)
- [Deep Learning for Automated Identification of Vietnamese Timber Species: A Tool for Ecological Monitoring and Conservation](https://mlsanity.com/paper/2508.10938/) (2025)

## Abstract (arXiv)

Most work in audio enhancement targets human speech, while bioacoustics is less studied due to noisy recordings and the distinct traits of animal sounds. To fill this gap, we adapt speech enhancement methods and build BioSEN, a model made for bioacoustic signals. BioSEN has three modules: a multi-scale dual-axis attention unit for time-frequency feature extraction, a bio-harmonic multi-scale enhancement unit for capturing harmonic structures, and an energy-adaptive gating connection unit that uses frequency weights to keep vocalizations from being removed as noise. Tests on three bioacoustic datasets show that BioSEN matches or exceeds state-of-the-art speech enhancement models while using far less computation. These results show BioSEN's strength for bioacoustic audio enhancement and its promise for biodiversity monitoring and conservation.