Subject: [AUDITORY] CfP: Special Issue "Machine Learning Applied to Music/Audio Signal Processing" From: "Lerch, Alexander G" <alexander.lerch@xxxxxxxx> Date: Tue, 16 Jun 2020 17:56:36 +0000Apologies for cross-posting.=20 Dear all, Please find the call for a special issue on " Machine Learning Applied to M= usic/Audio Signal Processing" in MDPI Electronics at=20 https://www.mdpi.com/si/51394 Thanks and we are looking forward to your contributions! --- Dear Colleagues, The applications of audio and music processing range from music discovery a= nd recommendation systems over speech enhancement, audio event detection, a= nd music transcription, to creative applications such as sound synthesis an= d morphing. The last decade has seen a paradigm shift from expert-designed algorithms t= o data-driven approaches. Machine learning approaches, and Deep Neural Netw= orks specifically, have been shown to outperform traditional approaches on = a large variety of tasks including audio classification, source separation,= enhancement, and content analysis. With data-driven approaches, however, c= ame a set of new challenges. Two of these challenges are training data and = interpretability. As supervised machine learning approaches increase in com= plexity, the increasing need for more annotated training data can often not= be matched with available data. The lack of understanding of how data are = modeled by neural networks can lead to unexpected results and open vulnerab= ilities for adversarial attacks. The main aim of this Special Issue is to seek high-quality submissions that= present novel data-driven methods for audio/music signal processing and an= alysis and address main challenges of applying machine learning to audio si= gnals. Within the general area of audio and music information retrieval as = well as audio and music processing, the topics of interest include, but are= not limited to, the following: - unsupervised and semi-supervised systems for audio/music processing a= nd analysis - machine learning methods for raw audio signal analysis and transforma= tion - approaches to understanding and controlling the behavior of audio pro= cessing systems such as visualization, auralization, or regularization meth= ods - generative systems for sound synthesis and transformation - adversarial attacks and the identification of 'deepfakes' in audio an= d music - audio and music style transfer methods - audio recording and music production parameter estimation - data collection methods, active learning, and interactive machine lea= rning for data-driven approaches Dr. Peter Knees Dr. Alexander Lerch