15 citations · 66 across the 9 of their papers we have counts for
9 papers
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…
Deep Transform: Cocktail Party Source Separation via Probabilistic Re-Synthesis
Andrew J. R. Simpson
In cocktail party listening scenarios, the human brain is able to separate competing speech signals. However, the signal processing implemented by the brain to perform cocktail par…
Deep Transform: Time-Domain Audio Error Correction via Probabilistic Re-Synthesis
Andrew J. R. Simpson
In the process of recording, storage and transmission of time-domain audio signals, errors may be introduced that are difficult to correct in an unsupervised way. Here, we train a…