Special Issue "Deep Learning for Applications in Acoustics: Modeling, Synthesis, and Listening"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".

Deadline for manuscript submissions: 15 April 2020.

Special Issue Editors

Dr. Leonardo Gabrielli
E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Italy
Dr. George Fazekas
E-Mail Website
Guest Editor
Queen Mary University, London, UK
Dr. Juhan Nam
E-Mail Website
Guest Editor
Korea Advanced Institute of Science and Technology, Korea

Special Issue Information

Dear Colleagues,

Recent introduction of Deep Learning has led to a vast array of breakthroughs in many fields of science and engineering. The data-driven approach has gathered the attention of research communities and has often been successful in yielding solutions to very complex classification and regression problems.

In the fields of audio analysis, processing and acoustic modelling, Deep Learning has been adopted, initially borrowing their methods from the image processing and computer vision field, and then finding creative and innovative solutions to suit domain-specific needs of acoustic research. In this process, researchers are facing two big challenges: learning meaningful spatio-temporal representations of audio signals and making sense of the black-box model of neural networks, i.e. extracting knowledge that is useful for scientific advance.

In this special issue, we welcome the submission of papers dealing with novel computational methods involving modelling, parametrization, and knowledge extraction of acoustic data. The considered topics include, e.g.:

  • Applications of Deep Learning to sound synthesis
  • Control and estimation problems in physical modeling
  • Intelligent music production and novel digital audio effects
  • Representation learning and/or transfer of musical composition and performance characteristics including, timbre, style and playing technique
  • Analysis and modelling of acoustic phenomena including musical acoustics, speech signals, room acoustics, environmental, ecological, medical and machine sounds.
  • Machine listening and perception models inspired by human hearing
  • Application of Deep Learning to wave propagation problems in fluids and solids

We aim at fostering good research practices in Deep Learning. Considering current scientific and ethical concerns with Deep Learning, including reproducibility and explainability, we strongly support works that are based on open datasets and source code, works that excel on the scientific method, and works providing evidences and explanations for the observed phenomena.

Dr. Leonardo Gabrielli
Dr. George Fazekas
Dr. Juhan Nam
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Deep learning
  • Sound synthesis
  • Machine listening
  • Audio signal processing
  • Sound event detection
  • Acoustic modelling
  • Digital audio effects
  • Audio style transfer

Published Papers (2 papers)

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Research

Open AccessArticle
An Analysis of Rhythmic Patterns with Unsupervised Learning
Appl. Sci. 2020, 10(1), 178; https://doi.org/10.3390/app10010178 - 25 Dec 2019
Abstract
This paper presents a model capable of learning the rhythmic characteristics of a music signal through unsupervised learning. The model learns a multi-layer hierarchy of rhythmic patterns ranging from simple structures on lower layers to more complex patterns on higher layers. The learned [...] Read more.
This paper presents a model capable of learning the rhythmic characteristics of a music signal through unsupervised learning. The model learns a multi-layer hierarchy of rhythmic patterns ranging from simple structures on lower layers to more complex patterns on higher layers. The learned hierarchy is fully transparent, which enables observation and explanation of the structure of the learned patterns. The model employs tempo-invariant encoding of patterns and can thus learn and perform inference on tempo-varying and noisy input data. We demonstrate the model’s capabilities of learning distinctive rhythmic structures of different music genres using unsupervised learning. To test its robustness, we show how the model can efficiently extract rhythmic structures in songs with changing time signatures and live recordings. Additionally, the model’s time-complexity is empirically tested to show its usability for analysis-related applications. Full article
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Open AccessArticle
Noise-Robust Voice Conversion Using High-Quefrency Boosting via Sub-Band Cepstrum Conversion and Fusion
Appl. Sci. 2020, 10(1), 151; https://doi.org/10.3390/app10010151 - 23 Dec 2019
Abstract
This paper presents a noise-robust voice conversion method with high-quefrency boosting via sub-band cepstrum conversion and fusion based on the bidirectional long short-term memory (BLSTM) neural networks that can convert parameters of vocal tracks of a source speaker into those of a target [...] Read more.
This paper presents a noise-robust voice conversion method with high-quefrency boosting via sub-band cepstrum conversion and fusion based on the bidirectional long short-term memory (BLSTM) neural networks that can convert parameters of vocal tracks of a source speaker into those of a target speaker. With the implementation of state-of-the-art machine learning methods, voice conversion has achieved good performance given abundant clean training data. However, the quality and similarity of the converted voice are significantly degraded compared to that of a natural target voice due to various factors, such as limited training data and noisy input speech from the source speaker. To address the problem of noisy input speech, an architecture of voice conversion with statistical filtering and sub-band cepstrum conversion and fusion is introduced. The impact of noises on the converted voice is reduced by the accurate reconstruction of the sub-band cepstrum and the subsequent statistical filtering. By normalizing the mean and variance of the converted cepstrum to those of the target cepstrum in the training phase, a cepstrum filter was constructed to further improve the quality of the converted voice. The experimental results showed that the proposed method significantly improved the naturalness and similarity of the converted voice compared to the baselines, even with the noisy inputs of source speakers. Full article
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