# Towards High-Quality and Efficient Speech Bandwidth Extension with Parallel Amplitude and Phase Prediction

arXiv: https://arxiv.org/abs/2401.06387 · HTML: https://mlsanity.com/paper/2401.06387/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2024.

## Built on by (most cited first, 14 of 14)

- [Audio Super-Resolution with Latent Bridge Models](https://mlsanity.com/paper/2509.17609/) (2025)
- [Low Frame-rate Speech Codec: a Codec Designed for Fast High-quality Speech LLM Training and Inference](https://mlsanity.com/paper/2409.12117/) (2024)
- [EDNet: A Versatile Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Training](https://mlsanity.com/paper/2506.16231/) (2025)
- [UniverSR: Unified and Versatile Audio Super-Resolution via Vocoder-Free Flow Matching](https://mlsanity.com/paper/2510.00771/) (2025)
- [TS-URGENet: A Three-stage Universal Robust and Generalizable Speech Enhancement Network](https://mlsanity.com/paper/2505.18533/) (2025)
- [A High-Fidelity Speech Super Resolution Network using a Complex Global Attention Module with Spectro-Temporal Loss](https://mlsanity.com/paper/2507.00229/) (2025)
- [CodecFlow: Efficient Bandwidth Extension via Conditional Flow Matching in Neural Codec Latent Space](https://mlsanity.com/paper/2603.02022/) (2026)
- [LatentFlowSR: High-Fidelity Audio Super-Resolution via Noise-Robust Latent Flow Matching](https://mlsanity.com/paper/2604.09188/) (2026)
- [Neural Spectral Band Generation for Audio Coding](https://mlsanity.com/paper/2506.06732/) (2025)
- [CIS-BWE: Chaos-Informed Speech Bandwidth Extension](https://mlsanity.com/paper/2507.15970/) (2025)
- [mmWave Radar Aware Dual-Conditioned GAN for Speech Reconstruction of Signals With Low SNR](https://mlsanity.com/paper/2602.22431/) (2026)
- [FastWave: Optimized Diffusion Model for Audio Super-Resolution](https://mlsanity.com/paper/2603.04122/) (2026)
- [Fast and Flexible Audio Bandwidth Extension via Vocos](https://mlsanity.com/paper/2603.07285/) (2026)
- [P2Flow: Phoneme-aware Progressive Flow Matching for Extreme Speech Super-Resolution](https://mlsanity.com/paper/2609.24138/) (2026)

## Abstract (arXiv)

Speech bandwidth extension (BWE) refers to widening the frequency bandwidth range of speech signals, enhancing the speech quality towards brighter and fuller. This paper proposes a generative adversarial network (GAN) based BWE model with parallel prediction of Amplitude and Phase spectra, named AP-BWE, which achieves both high-quality and efficient wideband speech waveform generation. The proposed AP-BWE generator is entirely based on convolutional neural networks (CNNs). It features a dual-stream architecture with mutual interaction, where the amplitude stream and the phase stream communicate with each other and respectively extend the high-frequency components from the input narrowband amplitude and phase spectra. To improve the naturalness of the extended speech signals, we employ a multi-period discriminator at the waveform level and design a pair of multi-resolution amplitude and phase discriminators at the spectral level, respectively. Experimental results demonstrate that our proposed AP-BWE achieves state-of-the-art performance in terms of speech quality for BWE tasks targeting sampling rates of both 16 kHz and 48 kHz. In terms of generation efficiency, due to the all-convolutional architecture and all-frame-level operations, the proposed AP-BWE can generate 48 kHz waveform samples 292.3 times faster than real-time on a single RTX 4090 GPU and 18.1 times faster than real-time on a single CPU. Notably, to our knowledge, AP-BWE is the first to achieve the direct extension of the high-frequency phase spectrum, which is beneficial for improving the effectiveness of existing BWE methods.