15 citations · 66 across the 10 of their papers we have counts for
11 papers
Hierarchical Conflict Propagation: Sequence Learning in a Recurrent Deep Neural Network
Andrew J. R. Simpson
Recurrent neural networks (RNN) are capable of learning to encode and exploit activation history over an arbitrary timescale. However, in practice, state of the art gradient descen…
Deep Remix: Remixing Musical Mixtures Using a Convolutional Deep Neural Network
Andrew J. R Simpson, Gerard Roma, Mark D. Plumbley
Audio source separation is a difficult machine learning problem and performance is measured by comparing extracted signals with the component source signals. However, if separation…
Time-Frequency Trade-offs for Audio Source Separation with Binary Masks
Andrew J. R. Simpson
The short-time Fourier transform (STFT) provides the foundation of binary-mask based audio source separation approaches. In computing a spectrogram, the STFT window size parameteri…
Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network
Andrew J. R. Simpson, Gerard Roma, Mark D. Plumbley
Identification and extraction of singing voice from within musical mixtures is a key challenge in source separation and machine audition. Recently, deep neural networks (DNN) have…
Deep Transform: Cocktail Party Source Separation via Complex Convolution in a Deep Neural Network
Andrew J. R. Simpson
Convolutional deep neural networks (DNN) are state of the art in many engineering problems but have not yet addressed the issue of how to deal with complex spectrograms. Here, we u…
Probabilistic Binary-Mask Cocktail-Party Source Separation in a Convolutional Deep Neural Network
Andrew J. R. Simpson
Separation of competing speech is a key challenge in signal processing and a feat routinely performed by the human auditory brain. A long standing benchmark of the spectrogram appr…