Apollo: Band-sequence Modeling for High-Quality Audio Restoration (2409.08514v2)

Abstract: 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.

• The paper introduces a GAN-based band-sequence modeling technique that effectively decomposes frequency bands for precise audio restoration.
• It employs a dual-module framework combining Roformer and TCN to capture both spectral and temporal dependencies, outperforming SR-GAN in SDR and SI-SNR.
• Experimental results indicate Apollo consistently delivers robust performance across diverse music genres and bitrates while maintaining computational efficiency.

Analysis of "Apollo: Band-sequence Modeling for High-Quality Audio Restoration"

The paper "Apollo: Band-sequence Modeling for High-Quality Audio Restoration" explores the advancements in audio restoration, a domain that benefits greatly from generative modeling techniques to repair and enhance audio signals damaged by compression artifacts. Apollo stands out as a novel approach leveraging generative adversarial networks (GANs) for audio restoration by introducing a sophisticated model structure that explicitly models frequency bands and sequences, resulting in superior restoration performance across various audio scenarios, genres, and bitrates.

Model Architecture and Approach

Apollo is architected with a focus on band-sequence modeling, a distinctive aspect that differentiates it from other state-of-the-art models like SR-GAN. This architecture includes several modules targeting different aspects of the audio restoration process:

1. Band-Split Module: This module segregates the frequency dimension into sub-band spectrograms. By leveraging gain-shape representation, it effectively decomposes the sub-band spectrogram's content and energy components, enabling precise modeling of audio features.
2. Band-Sequence Modeling Module: Leveraging a combination of Roformer and Temporal Convolutional Network (TCN), this module captures global dependencies across both sub-bands and temporal sequences. This dual focus allows for the synthesis of audio signals with refined spectral and temporal detail.
3. Band-Reconstruction Module: Finally, the output from the sequence modeling is used to reconstruct the spectrogram, which is subsequently transformed back into an audio signal via the inverse Short-Time Fourier Transform.

Apollo’s training is carried out within a GAN framework, utilizing an STFT-based discriminator and a composite loss function to ensure high-quality audio restoration that balances perceptual quality and statistical accuracy.

Experimental Evaluation and Results

In its empirical evaluation, Apollo was tested against SR-GAN on the MUSDB18-HQ and MoisesDB datasets across a variety of music genres and bitrates. The results underscored Apollo’s efficacy:

• Superior Performance Across Bitrates: Apollo demonstrated consistent superiority over SR-GAN across all evaluated bitrates, with significant improvements in Signal-to-Distortion Ratio (SDR) and Scale-Invariant Signal-to-Noise Ratio (SI-SNR).
• Enhanced Genre Versatility: The model showed notable efficacy across diverse musical contexts, including vocals and complex instrument combinations, confirming its robustness in handling intricate audio restoration tasks.
• Efficient and Compact: Despite its advanced capabilities, Apollo maintains a relatively small model size, leading to more efficient computation, which is critical for deployment in real-time applications.

Implications and Future Directions

The implications of Apollo’s success are multifaceted. Practically, it presents a significant step forward in audio restoration, offering a means to enhance user experiences in music playback, telecommunication, and even in the augmentation of generative audio models. Theoretically, its approach to explicit band-sequence modeling could inspire further research into the decomposition of complex audio signals for robust restoration in more varied and challenging acoustic environments.

Future research could explore extending Apollo’s capabilities by exploring alternative neural architectures for sub-band modeling, such as attention mechanisms, that could further enhance its ability to distinguish and restore fine-grained audio features. Additionally, investigating its applicability to other audio tasks like noise reduction or live audio streaming could expand its utility.

Overall, the Apollo model sets a new benchmark in high-quality audio restoration, combining advanced machine learning techniques with a domain-specific understanding of audio signal processing to advance the state-of-the-art in generative audio restoration.