4 accepted papers
State-of-the-art audio classification systems often apply deep neural networks on hand-crafted features (e.g., spectrogram-based representations), instead of learning features directly from raw audio. Moreover, these audio networks have millions of unknown parameters need to be learned, which causes…
Parsimonious modelling, including sparsity and low rankness, has becomes a cornerstone in modern machine learning and signal processing. However, these modelling techniques have limited capabity to learn from large-scale data, and often require some pre-defined parameters to define their optimizatio…
Robust principal component analysis (RPCA), decomposes a data matrix into a superposition of a low-rank matrix and a sparse matrix under certain incoherent conditions. In this paper, we propose a nonlinear generalization of RPCA that uses two autoencoder networks to achieve such a decomposition, in…
The ability to localize visual objects that are associated with an audio source and at the same time seperate the audio signal is a corner stone in several audio-visual signal processing applications. Past efforts usually focused on localizing only the visual objects, without audio separation abilit…