Phase-aware Music Super-resolution Using Generative Adversarial Networks

Abstract

Audio super-resolution is a challenging task of recovering the missing high-resolution features from a low-resolution signal. To address this, generative adversarial networks (GAN) have been used to achieve promising results by training the mappings between magnitudes of the low and high-frequency components. However, phase information is not well-considered for waveform reconstruction in conventional methods. In this paper, we tackle the problem of music super-resolution and conduct a thorough investigation on the importance of phase for this task. We use GAN to predict the magnitudes of the high-frequency components. The corresponding phase information can be extracted using either a GAN-based waveform synthesis system or a modified Griffin-Lim algorithm. Experimental results show that phase information plays an important role in the improvement of the reconstructed music quality. Moreover, our proposed method significantly outperforms other state-of-the-art methods in terms of objec

Stats

• citations18
• S2 citations—
• github stars0
• HF likes0
• heat score9.59
• arxiv keyhu2020phase

Related papers

• Bigwavgan: A Wave-to-wave Generative Adversarial Network For Music Super-resolution (2023)0.00
• Bandwidth Extension On Raw Audio Via Generative Adversarial Networks (2019)0.00
• Phase Repair For Time-domain Convolutional Neural Networks In Music Super-resolution (2023)0.00
• Generative Adversarial Network-based Approach To Signal Reconstruction From Magnitude Spectrograms (2018)10.97
• An Investigation Of Pre-upsampling Generative Modelling And Generative Adversarial Networks In Audio Super Resolution (2021)0.00
• Gansynth: Adversarial Neural Audio Synthesis (2019)0.00
• Inspiremusic: Integrating Super Resolution And Large Language Model For High-fidelity Long-form Music Generation (2025)6.26
• Learning Audio Representations Via Phase Prediction (2019)0.00