Download HD-AD: A New Approach to Audio Atomic Decomposition with Hyperdimensional Computing In this paper, we approach the problem of atomic decomposition of audio at the symbolic level of atom parameters through the lens of hyperdimensional computing (HDC) – a non-traditional computing paradigm. Existing atomic decomposition algorithms often operate using waveforms from a redundant dictionary of atoms causing them to become increasingly memory/computationally intensive as the signal length grows and/or the atoms become more complicated. We systematically build an atom encoding using vector function architecture (VFA), a field of HDC. We train a neural network encoder on synthetic audio signals to generate these encodings and observe that the network can generalize to real recordings. This system, we call Hyperdimensional Atomic Decomposition (HD-AD), avoids time-domain correlations all together. Because HD-AD scales with the sparsity of the signal, rather than its length in time, atomic decompositions are often produced much faster than real-time.
Download Hybrid Audio Inpainting Approach with Structured Sparse Decomposition and Sinusoidal Modeling This research presents a novel hybrid audio inpainting approach that considers the diversity of signals and enhances the reconstruction quality. Existing inpainting approaches have limitations, such as energy drop and poor reconstruction quality for non-stationary signals. Based on the fact that an audio signal can be considered as a mixture of three components: tonal, transients, and noise, the proposed approach divides the left and right reliable neighborhoods around the gap into these components using a structured sparse decomposition technique. The gap is reconstructed by extrapolating parameters estimated from the reliable neighborhoods of each component. Component-targeted methods are refined and employed to extrapolate the parameters based on their own acoustic characteristics. Experiments were conducted to evaluate the performance of the hybrid approach and compare it with other stateof-the-art inpainting approaches. The results show the hybrid approach achieves high-quality reconstruction and low computational complexity across various gap lengths and signal types, particularly for longer gaps and non-stationary signals.