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CFP Data-driven ASP: Methods and Apps

Data-driven approaches have been successfully applied to various problems on acoustical signal detection, enhancement and analysis/classification in the recent past. The tremendous success of these approaches, compared to the classical methods, has led to a renewed interest in, and an expansion of applications for, audio signal processing. For example, in the field of speech enhancement, the time-variant nature of the noise and interference makes it very difficult to find a perfect method for eliminating noise or separating multiple interfering speech sources using classical approaches based on statistical signal models. Furthermore, the nature of the acoustic environment is another key factor to be considered when designing the enhancement approaches. This makes the development of robust acoustic signal enhancement methods very challenging. In such cases, data-driven approaches based, e.g., on deep learning, offer a significant advantage either on their own or when paired with the classical approaches. Another example is from the field of predictive maintenance of industrial machinery, where the acoustical fields corresponding to the different conditions to be diagnosed are often difficult to model or characterize. Here, data-driven approaches that learn the relevant features and models from the available data often offer solutions that are not achievable by classical means.

This special issue invites researchers and practitioners to present novel contributions addressing theoretical and practical aspects of data-driven approaches for acoustic signal processing. We welcome high quality theoretical articles that offer novel insights and creative solutions to key challenges in this field as well as articles on state-of-the-art practical systems which demonstrate high industrial impact. Contributions addressing emerging problems and directions are also very welcome.

The topics of this special issue include but are not limited to:

  • Audio and speech processing
  • Acoustic echo cancellation
  • Noise reduction
  • Dereverberation;
  • (Near-end) Listening enhancement 
  • Microphone arrays for distant audio capture, classification and detection
  • Source localization and separation
  • Machine listening
  • Robot audition
  • Predictive maintenance
  • Auditory scene analysis and classification
  • Signal coding and transmission
  • Speech, speaker and emotion recognition

Submission Instructions:

Before submitting your manuscript, please ensure you have carefully read the submission guidelines for EURASIP Journal on Audio, Speech, and Music Processing. The complete manuscript should be submitted through the EURASIP Journal on Audio, Speech, and Music Processing submission system. To ensure that you submit to the correct special issue please select the appropriate special issue in the drop-down menu upon submission. In addition, indicate within your cover letter that you wish your manuscript to be considered as part of the special issue on Data-driven Approaches in Acoustic Signal Processing: Methods and Applications. All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.

Deadline for submissions is 30 September 2020

Lead Guest Editor

Changchun Bao, Beijing University of Technology.

Guest Editor

Nilesh Madhu, Ghent University, Belgium.

Submissions will also benefit from the usual advantages of open access publication: 

Rapid publication: Online submission, electronic peer review and production make the process of publishing your article simple and efficient

High visibility and international readership in your field: Open access publication ensures high visibility and maximum exposure for your work - anyone with online access can read your article

No space constraints: Publishing online means unlimited space for figures, extensive data and video footage

Authors retain copyright, licensing the article under a Creative Commons license: articles can be freely redistributed and reused as long as the article is correctly attributed

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