2015/05/01 by A Simpson, Simpson, Andrew J. R, Gerard Roma +4
Computer Science · Engineering · #68Txx #Advanced Adaptive Filtering Techniques #Blind Source Separation Techniques #FOS: Computer and information sciences #Music and Audio Processing #Sound (cs.SD) #Speech and Audio Processing #cs.SD #msc:68Txx
paper · pdf · doi:10.48550/arxiv.1505.00289
arxiv created 2015/05/01 · openalex publication_date 2015/05/01 · arxiv updated 2015/05/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 is motivated by the ultimate goal of re-mixing then complete separation is not necessary and hence separation difficulty and separation quality are dependent on the nature of the re-mix. Here, we use a convolutional deep neural network (DNN), trained to estimate 'ideal' binary masks for separating voice from music, to perform re-mixing of the vocal balance by operating directly on the individual magnitude components of the musical mixture spectrogram. Our results demonstrate that small changes in vocal gain may be applied with very little distortion to the ultimate re-mix. Our method may be useful for re-mixing existing mixes.