Report on Current Developments in Speech and Audio Processing
General Trends and Innovations
The field of speech and audio processing is witnessing a significant shift towards more integrated and hybrid models that combine traditional signal processing techniques with modern deep learning frameworks. This trend is driven by the need for models that are not only computationally efficient and robust but also explainable and capable of handling a diverse range of distortions. The recent advancements can be broadly categorized into three main areas: biomimetic approaches, dual-path architectures, and diffusion-based generative models.
Biomimetic Approaches: There is a growing interest in leveraging classical models of human hearing and making them differentiable. This allows for the integration of traditional, explainable signal processing methods with deep learning, resulting in models that are both expressive and easily trainable on modest amounts of data. These models are particularly effective in tasks such as audio classification and enhancement, where they demonstrate superior computational efficiency and robustness compared to black-box deep learning models.
Dual-Path Architectures: The introduction of dual-path networks, particularly those that employ parallel decoders with shared parameters, is gaining traction. These architectures are designed to handle multiple types of distortions, such as noise, reverberation, and bandwidth degradation, more effectively. By integrating the outputs of different decoders through skip connections, these models can overcome the limitations of previous approaches and achieve substantial improvements in speech restoration with fewer parameters.
Diffusion-Based Generative Models: Diffusion-based models are emerging as a powerful tool for speech and vocal enhancement, particularly due to their ability to model complex speech data distributions. Recent innovations in this area include the integration of latent representations from discriminative models to improve the fidelity of diffusion-based models. Additionally, there is a focus on developing novel training objectives and perceptual loss functions to enhance the performance and perceptual quality of the enhanced speech signals.
Noteworthy Papers
Biomimetic Frontend for Differentiable Audio Processing: This paper introduces a differentiable model that combines traditional biomimetic signal processing with deep learning, achieving superior computational efficiency and robustness with modest training data.
DM: Dual-path Magnitude Network for General Speech Restoration: The DM network, with its dual parallel magnitude decoders and integrated skip connections, demonstrates substantial improvements in general speech restoration with fewer parameters.
High-Resolution Speech Restoration with Latent Diffusion Model: Hi-ResLDM, a latent diffusion model, excels in restoring high-frequency details and is preferred in human evaluations, making it ideal for professional applications.
