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

Gabrielli, Leonardo; Fazekas, György; Nam, Juhan

doi:10.3390/app11020473

Cited by 3 publications

(3 citation statements)

References 11 publications

(13 reference statements)

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“…In some cases, classification was derived from band-level indices (e.g., Širović et al, 2015;Oestreich et al, 2020). When the training data are insufficient, additional exemplars can be added by transforming the true signals (and labeling them as false) or adding different types and amounts (Barchiesi et al, 2015;Benetos et al, 2015;Shane et al, 2015;Zabalza et al, 2015;Freeman and Freeman, 2016;Lin et al, 2017a,b;Lin et al, , 2019Lin et al, , 2021Tsao, 2018, 2020;Colonna et al, 2018;Guan et al, 2018;Reis et al, 2018;Seger et al, 2018;Virtanen et al, 2018;Abeßer, 2020;Gatto et al, 2020;Mooney et al, 2020;Shajahan et al, 2020;Xie et al, 2020;Dias et al, 2021;Dimoff et al, 2021;Gabrielli et al, 2021;Ick and McFee, 2021;Ozanich et al, 2021).…”

Section: Analytical Approaches To Soundscape Analysesmentioning

confidence: 99%

“…The literature referenced simply serve as examples, and not an exhaustive or systematic search of the literature. References cited in the table:(Barchiesi et al, 2015;Benetos et al, 2015;Shane et al, 2015;Zabalza et al, 2015;Freeman and Freeman, 2016; Lin et al, 2017a,b;Lin et al, , 2019Lin et al, , 2021 Tsao, 2018, 2020;Colonna et al, 2018;Guan et al, 2018;Reis et al, 2018;Seger et al, 2018;Virtanen et al, 2018;Abeßer, 2020;Gatto et al, 2020;Mooney et al, 2020;Shajahan et al, 2020;Xie et al, 2020;Dias et al, 2021;Dimoff et al, 2021;Gabrielli et al, 2021;Ick and McFee, 2021;Ozanich et al, 2021).…”

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confidence: 99%

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Advancing the Interpretation of Shallow Water Marine Soundscapes

et al. 2021

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Soundscapes offer rich descriptions of composite acoustic environments. Characterizing marine soundscapes simply through sound levels results in incomplete descriptions, limits the understanding of unique features, and impedes meaningful comparisons. Sources that contribute to sound level metrics shift in time and space with changes in biological patterns, physical forces, and human activity. The presence of a constant or chronic source is often interwoven with episodic sounds. Further, the presence and intensity of sources can influence other sources, calling for a more integrated approach to characterizing soundscapes. Here, we illustrate this complexity using data from a national-scale effort, the Sanctuary Soundscape Monitoring Project (SanctSound), an initiative designed to support collection of biological, environmental, and human use data to compliment the interpretation of sound level measurements. Using nine examples from this diverse dataset we demonstrate the benefit of integrating source identification and site features to interpret sound levels across a diversity of shallow water marine soundscapes (<150 m). Sound levels from sites in high human use areas reflect the anthropogenic influences on the soundscape, especially when measuring broad frequency bands, whereas sites with relatively low human activity and high sound levels reveal biological features of the soundscape. At sites with large tidal changes, sound levels correlated with the magnitude of tidal flow, and sound levels during high tidal flow periods were similar to sound levels at sites near commercial shipping lanes. At sites in relatively close proximity (<30 km), sound levels diverge because of the presence of different proximate sound contributors and propagation features of the site. A review of emerging methodologies for integrated soundscape analysis, including acoustic scenes, provides a framework for interpreting soundscapes across a variety of conditions. With a global growth in monitoring efforts collecting standardized measurements over widely distributed arrays, more integrated methods are needed to advance the utility of soundscapes in marine resource management.

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Section: Analytical Approaches To Soundscape Analysesmentioning

confidence: 99%

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confidence: 99%

Advancing the Interpretation of Shallow Water Marine Soundscapes

et al. 2021

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“…divides multiple administrator roles for different museum managers and specifies various operation authority scopes for each role according to their diverse permissions [33][34][35]. This differentiated permission setting is designed to guarantee the security and effectiveness of the system.…”

Section: Plos Onementioning

confidence: 99%

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums

Lin,

Hu,

Cheng

et al. 2024

PLoS ONE

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This study aims to develop a digital retrieval system for art museums to solve the problems of inaccurate information and low retrieval efficiency in the digital management of cultural heritage. By introducing an improved Genetic Algorithm (GA), digital management and access efficiency are enhanced, to bring substantial optimization and innovation to the digital management of cultural heritage. Based on the collection of art museums, this study first integrates the collection’s images, texts, and metadata with multi-source intelligent information to achieve a more accurate and comprehensive description of digital content. Second, a GA is introduced, and a GA 2 Convolutional Neural Network (GA2CNN) optimization model combining domain knowledge is proposed. Moreover, the convergence speed of traditional GA is improved to adapt to the characteristics of cultural heritage data. Lastly, the Convolutional Neural Network (CNN), GA, and GA2CNN are compared to verify the proposed system’s superiority. The results show that in all models, the sample output results’ actual value is 2.62, which represents the real data observation results. For sample number 5, compared with the actual value of 2.62, the predicted values of the GA2CNN and GA models are 2.6177 and 2.6313, and their errors are 0.0023 and 0.0113. The CNN model’s predicted value is 2.6237, with an error of 0.0037. It can be found that the network fitting accuracy after optimization of the GA2CNN model is high, and the predicted value is very close to the actual value. The digital retrieval system integrated with the GA2CNN model has a good performance in enhancing retrieval efficiency and accuracy. This study provides technical support for the digital organization and display of cultural heritage and offers valuable references for innovative exploration of museum information management in the digital era.

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Spatial Audio Coding and Machine Learning

Dabbabi

2022

Encyclopedia of Data Science and Machine Learning

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Spatial audio encoding plays a fundamental role in the ultra-high-definition TV (UHDTV) and the latest generation of television broadcasting, as well as other technological devices by providing a three-dimensional (3D) audio content to consumers. In this article, the fundamental concepts of the spatial audio coding including its techniques, standards, and applications are exhibited. The object-based audio reproduction system will be presented and compared to the traditional channel-based system in order to offer a good understanding of this system to the users and to give them more flexibility in their preferred audio composition. Moreover, the MPEG standard for encoding multi-channel audio signals will be exposed. Machine learning (ML) methods and their applications in acoustics and spatial audio scenes will then be offered. Ultimately, further research directions will be illustrated and discussed.

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

Abstract: The recent introduction of Deep Learning has led to a vast array of breakthroughs in many fields of science and engineering [...]

Cited by 3 publications

References 11 publications

Advancing the Interpretation of Shallow Water Marine Soundscapes

Advancing the Interpretation of Shallow Water Marine Soundscapes

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums

Spatial Audio Coding and Machine Learning

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