HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement (2203.13086v4)

Abstract: Generative adversarial networks have recently demonstrated outstanding performance in neural vocoding outperforming best autoregressive and flow-based models. In this paper, we show that this success can be extended to other tasks of conditional audio generation. In particular, building upon HiFi vocoders, we propose a novel HiFi++ general framework for bandwidth extension and speech enhancement. We show that with the improved generator architecture, HiFi++ performs better or comparably with the state-of-the-art in these tasks while spending significantly less computational resources. The effectiveness of our approach is validated through a series of extensive experiments.

• The paper introduces the HiFi++ framework, which unifies bandwidth extension and speech enhancement using a GAN-based architecture.
• It employs SpectralUNet, WaveUNet, and SpectralMaskNet modules for efficient waveform reconstruction and noise reduction.
• Extensive evaluations using PESQ, STOI, and MOS tests confirm its superior performance despite lower computational requirements.

Overview of "HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement"

The paper "HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement" presents a sophisticated approach to conditional speech generation using advancements introduced by generative adversarial networks (GANs). By leveraging the HiFi-GAN framework, the authors propose HiFi++, an architecture designed to tackle bandwidth extension (BWE) and speech enhancement (SE) more efficiently. The intriguing aspect of the HiFi++ framework is its ability to maintain performance levels comparable to existing state-of-the-art models while reducing computational resource demands significantly.

Key Contributions

At the core of this research lies the novel architectural design of the HiFi++ generator, which is an adaption of the HiFi-GAN framework. The following modules are incorporated:

• SpectralUNet: Targets the initial mel-spectrogram input to ease the transformation into a waveform, utilizing a UNet-like architecture specifically designed for two-dimensional convolutional operations.
• WaveUNet: Operates directly on the time domain, post-processing the output of the HiFi-GAN to merge the predicted and source waveforms seamlessly.
• SpectralMaskNet: Applies frequency-domain post-processing, which utilizes learnable spectral masking techniques aimed at removing noise and artifacts from the generated waveform.

The integration of these modules allows HiFi++ to address the intricacies and challenges of BWE and SE with surprising computational efficacy.

Methodology and Experimental Analysis

The authors utilize a multi-discriminator adversarial training setup, which includes LS-GAN loss, feature matching loss, and mel-spectrogram loss to refine their models. This sophisticated loss combination supports learning in the complex tasks of bandwidth extension and speech enhancement by fostering higher-fidelity reconstructions in audio signal restoration tasks.

Their model evaluation, conducted through a spectrum of objective and subjective metrics such as PESQ, STOI, and DNSMOS, alongside crowd-sourced mean opinion score (MOS) tests, provides ample evidence of HiFi++'s effectiveness. Notably, HiFi++ shows robust results across various bandwidth constraints (1 kHz, 2 kHz, and 4 kHz) and in speech enhancement scenarios, achieving favorable comparisons against competing models like VoiceFixer and SEANet.

The computational efficiency is one of the most compelling outcomes, as the HiFi++ is shown to utilize resources far more efficiently than its larger counterparts with similar or even superior qualitative outputs.

Implications and Future Directions

The results underscore the potential of GAN-based frameworks in audio-signal processing applications, affording practical benefits across telecommunications and media broadcasts, among others. The HiFi++ design optimizes the trade-off between computational overhead and model performance, suggesting broader applicability in real-time systems where resource constraints are critical.

Theoretically, the modular structuring of architectures like HiFi++ presents promising avenues for further research into conditional audio generation. The integration of advanced preprocessing and postprocessing modules indicates an interesting direction to experiment with more adaptive and context-aware networks, potentially expanding GANs' usability beyond current horizons.

Future work could expand these paradigms into lower-resource adaptation, cross-language applications, and integration with other forms of multimedia enhancement, ensuring that HiFi++ and its successors continue to push forward the boundaries of what is feasible in speech technology and AI-driven audio processing. Additionally, exploring more robust training frameworks and alternative GAN configurations may yield even more efficient models and state-of-the-art outcomes.