The problem of Audio Source Separation is a well-studied problem and is also known as the cocktail party problem, i.e. the problem of separating of a particular audio source of interest in an environment full of auditory stimuli and noises. The problem has attracted the attention of researchers over the past 25 years and a handful of solutions have been proposed that can solve the problem in a number of very special cases. Nonetheless, there are several scenarios where the proposed methods fail, rendering the problem still unsolvable. Recently, the widespread success of online music providers that stream audio content to millions of users has made audio source separation fashionable again in order to provide more interactive audio content to subscribers.
The goal of this Research Topic is to investigate several new trends and techniques to address this challenging signal processing problem. The introduction of extra source information in order to facilitate the task of source separation has proved quite successful. The emergence of deep learning algorithms have revitalized most signal processing areas, including audio source separation. Current deep learning based source separation approaches tend to outperform traditional source separation, opening a wide room for new developments and solution to the problem. The main aim of this research topic is to attract all the novel trends, developments and solutions in the field of audio source separation.
Themes of interest in this Research Topic include but are not limited to the following:
• Statistical audio source separation
• Informed audio source separation
• Audio-visual source separation
• Deep-learning source separation
• Application of Generative Adversarial Networks on source separation
• Post-processing of separated sources
• Separation in reverberant environments
Topic editor Sebastian Ewert is employed by Spotify. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
The problem of Audio Source Separation is a well-studied problem and is also known as the cocktail party problem, i.e. the problem of separating of a particular audio source of interest in an environment full of auditory stimuli and noises. The problem has attracted the attention of researchers over the past 25 years and a handful of solutions have been proposed that can solve the problem in a number of very special cases. Nonetheless, there are several scenarios where the proposed methods fail, rendering the problem still unsolvable. Recently, the widespread success of online music providers that stream audio content to millions of users has made audio source separation fashionable again in order to provide more interactive audio content to subscribers.
The goal of this Research Topic is to investigate several new trends and techniques to address this challenging signal processing problem. The introduction of extra source information in order to facilitate the task of source separation has proved quite successful. The emergence of deep learning algorithms have revitalized most signal processing areas, including audio source separation. Current deep learning based source separation approaches tend to outperform traditional source separation, opening a wide room for new developments and solution to the problem. The main aim of this research topic is to attract all the novel trends, developments and solutions in the field of audio source separation.
Themes of interest in this Research Topic include but are not limited to the following:
• Statistical audio source separation
• Informed audio source separation
• Audio-visual source separation
• Deep-learning source separation
• Application of Generative Adversarial Networks on source separation
• Post-processing of separated sources
• Separation in reverberant environments
Topic editor Sebastian Ewert is employed by Spotify. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
