Audio Source Separation via Multi-Scale Learning with Dilated Dense U-Nets
Audio source separation refers to the process of extracting constituent sources from a given audio mixture. Despite being a critical component of audio enhancement and retrieval systems, the task of source separation is severely challenged by variabilities in acoustic conditions and the highly ill-posed nature of this inverse problem.
A majority of conventional source separation techniques operate in the spectral domain, specifically the magnitude spectrum. However, by ignoring the crucial phase information, these methods often require extensive tuning of front-end spectral transformations to produce accurate source estimates. Recent approaches have resorted to time-domain processing to bypass the need for front-end transformations. On the other hand, fully time-domain approaches must contend with variable temporal contexts to extract useful features, making network training challenging even with sophisticated sequence models such as long short-term memory (LSTM) and one-dimensional convolutional neural networks (1DCNNs). This motivates the design of architectures that can effectively extract multi-scale features and produce generalizable source estimation models for highly underdetermined scenarios.
Researchers at Arizona State University have developed DDU-Net, a fully convolutional approach for time-domain audio source separation. Designed as a U-Net style architecture, DDU-Net utilizes dilated convolutions to leverage information from exponentially increasing receptive fields and features dense connections to improve the robustness of the training process. The modeling approach can produce multi-scale features which are robust to sampling rate changes and can enable complex temporal modeling. Experiments demonstrate that the improved feature extraction process outperforms state-of-the-art time-domain separation approaches, namely the Wave-U-Net and the WaveNet models.
• Audio source separation
• Time-domain feature extraction
Benefits and Advantages
• Robust to sampling rate changes
• Efficient model training due to improved gradient flow
• Efficient feature reuse resulting from dense connections between convolutional layers
• Improved local context for source reconstruction from the use of skip connections between layers