# Apollo: Band-sequence Modeling for High-Quality Audio Restoration

arXiv: https://arxiv.org/abs/2409.08514 · HTML: https://mlsanity.com/paper/2409.08514/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 36 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 24 of 24)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Root Mean Square Layer Normalization](https://mlsanity.com/paper/1910.07467/) (2019)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [High-Fidelity Audio Compression with Improved RVQGAN](https://mlsanity.com/paper/2306.06546/) (2023)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [SEGAN: Speech Enhancement Generative Adversarial Network](https://mlsanity.com/paper/1703.09452/) (2017)
- [Music Source Separation with Band-split RNN](https://mlsanity.com/paper/2209.15174/) (2022)
- [Solving Audio Inverse Problems with a Diffusion Model](https://mlsanity.com/paper/2210.15228/) (2022)
- [A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions](https://mlsanity.com/paper/2011.06801/) (2020)
- [VoiceFixer: Toward General Speech Restoration with Neural Vocoder](https://mlsanity.com/paper/2109.13731/) (2021)
- [AudioDec: An Open-source Streaming High-fidelity Neural Audio Codec](https://mlsanity.com/paper/2305.16608/) (2023)
- [Moisesdb: A dataset for source separation beyond 4-stems](https://mlsanity.com/paper/2307.15913/) (2023)
- [An efficient encoder-decoder architecture with top-down attention for speech separation](https://mlsanity.com/paper/2209.15200/) (2022)
- [The Sound Demixing Challenge 2023 unicodex2013 Cinematic Demixing Track](https://mlsanity.com/paper/2308.06981/) (2023)
- [An Audio-Visual Speech Separation Model Inspired by Cortico-Thalamo-Cortical Circuits](https://mlsanity.com/paper/2212.10744/) (2022)
- [Subnetwork-to-go: Elastic Neural Network with Dynamic Training and Customizable Inference](https://mlsanity.com/paper/2312.03464/) (2023)
- [Enrollment-stage Backdoor Attacks on Speaker Recognition Systems via Adversarial Ultrasound](https://mlsanity.com/paper/2306.16022/) (2023)
- [Gull: A Generative Multifunctional Audio Codec](https://mlsanity.com/paper/2404.04947/) (2024)
- [Joint Speaker Encoder and Neural Back-end Model for Fully End-to-End Automatic Speaker Verification with Multiple Enrollment Utterances](https://mlsanity.com/paper/2209.00485/) (2022)
- [MC-SpEx: Towards Effective Speaker Extraction with Multi-Scale Interfusion and Conditional Speaker Modulation](https://mlsanity.com/paper/2306.16250/) (2023)
- [InstructSing: High-Fidelity Singing Voice Generation via Instructing Yourself](https://mlsanity.com/paper/2409.06330/) (2024)
- [Stochastic Restoration of Heavily Compressed Musical Audio using Generative Adversarial Networks](https://mlsanity.com/paper/2207.01667/) (2022)

## Built on by (most cited first, 8 of 8)

- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
- [Audio Super-Resolution with Latent Bridge Models](https://mlsanity.com/paper/2509.17609/) (2025)
- [Music Source Restoration](https://mlsanity.com/paper/2505.21827/) (2025)
- [Learning to Upsample and Upmix Audio in the Latent Domain](https://mlsanity.com/paper/2506.00681/) (2025)
- [EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis](https://mlsanity.com/paper/2506.20333/) (2025)
- [Multi-Perspective Subimage CLIP with Keyword Guidance for Remote Sensing Image-Text Retrieval](https://mlsanity.com/paper/2601.18190/) (2026)
- [Music Source Restoration with Ensemble Separation and Targeted Reconstruction](https://mlsanity.com/paper/2603.16926/) (2026)
- [Music Restoration via Latent Operator Optimization and Diffusion Model Priors](https://mlsanity.com/paper/2608.01972/) (2026)

## Abstract (arXiv)

Audio restoration has become increasingly significant in modern society, not only due to the demand for high-quality auditory experiences enabled by advanced playback devices, but also because the growing capabilities of generative audio models necessitate high-fidelity audio. Typically, audio restoration is defined as a task of predicting undistorted audio from damaged input, often trained using a GAN framework to balance perception and distortion. Since audio degradation is primarily concentrated in mid- and high-frequency ranges, especially due to codecs, a key challenge lies in designing a generator capable of preserving low-frequency information while accurately reconstructing high-quality mid- and high-frequency content. Inspired by recent advancements in high-sample-rate music separation, speech enhancement, and audio codec models, we propose Apollo, a generative model designed for high-sample-rate audio restoration. Apollo employs an explicit frequency band split module to model the relationships between different frequency bands, allowing for more coherent and higher-quality restored audio. Evaluated on the MUSDB18-HQ and MoisesDB datasets, Apollo consistently outperforms existing SR-GAN models across various bit rates and music genres, particularly excelling in complex scenarios involving mixtures of multiple instruments and vocals. Apollo significantly improves music restoration quality while maintaining computational efficiency. The source code for Apollo is publicly available at https://github.com/JusperLee/Apollo.