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Thematische Bibliographien / Acoustic Classification / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Acoustic Classification“

Autor: Grafiati

Veröffentlicht am 27. Juni 2021

Zuletzt aktualisiert am 9. Februar 2022

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1

Zheng, Hong Bo, Pin Yan und Jing Chen. „The Discussion of Acoustic Seabed Sediment Classification Methods“. Applied Mechanics and Materials 226-228 (November 2012): 1811–16. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.1811.

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Acoustic seabed sediment classification method is always important research contents in marine geology and marine acoustics because of its characters of low-cost and high efficiency. At present, there are mainly three types of acoustic seabed sediment classification methods:(1) the echo signal statistical characteristics classification; (2) image texture classification; (3) submarine acoustic parameter inversion method. The principles of anterior two classification methods are similar, which is based on statistics, unknown sediment type can be concluded according to the statistical characteristics of known sediment. There are many usable acoustic equipments and commercial classification software for the two kinds of methods. The third type method is based on suitable seabed sediment model. Seabed acoustic characteristic parameters are inversed and thus seabed sediment can be classified. At present, there are few usable acoustic equipment and commercial classification software for the third method, but it's more accurate than the anterior two classification methods.

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2

Ma, Ling, Ben Milner und Dan Smith. „Acoustic environment classification“. ACM Transactions on Speech and Language Processing 3, Nr. 2 (Juli 2006): 1–22. http://dx.doi.org/10.1145/1149290.1149292.

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3

Hichem, Hafdaoui, und Benatia Djamel. „Comparative between (LiNbO3) and (LiTaO3) in detecting acoustics microwaves using classification“. IAES International Journal of Artificial Intelligence (IJ-AI) 8, Nr. 1 (01.03.2019): 33. http://dx.doi.org/10.11591/ijai.v8.i1.pp33-43.

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Our work is mainly about detecting acoustics microwaves in the type of BAW (Bulk acoustic waves), where we compared between Lithium Niobate (LiNbO3) and Lithium Tantalate (LiTaO3) ,during the propagation of acoustic microwaves in a piezoelectric substrate. In this paper, We have used the classification by Probabilistic Neural Network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity for conclude whichever is the best in utilization for generating bulk acoustic waves.This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

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4

Choi. „Acoustic Target of Interest Tracking Algorithm Using Classification Feedback“. Journal Of The Acoustical Society Of Korea 33, Nr. 4 (2014): 225. http://dx.doi.org/10.7776/ask.2014.33.4.225.

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5

Martin, Linda V., Timothy K. Stanton, Peter H. Wiebe und James F. Lynch. „Acoustic classification of zooplankton“. Journal of the Acoustical Society of America 98, Nr. 5 (November 1995): 2881. http://dx.doi.org/10.1121/1.413130.

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6

Wilson, Joshua D., und Nicholas C. Makris. „Ocean acoustic hurricane classification“. Journal of the Acoustical Society of America 119, Nr. 1 (Januar 2006): 168–81. http://dx.doi.org/10.1121/1.2130961.

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7

Martin, L. „Acoustic classification of zooplankton“. ICES Journal of Marine Science 53, Nr. 2 (April 1996): 217–24. http://dx.doi.org/10.1006/jmsc.1996.0025.

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8

Nooralahiyan, A. Y., H. R. Kirby und D. McKeown. „Vehicle classification by acoustic signature“. Mathematical and Computer Modelling 27, Nr. 9-11 (Mai 1998): 205–14. http://dx.doi.org/10.1016/s0895-7177(98)00060-0.

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9

Leonetti, Marc C., und Edward A. Hand. „Acoustic classification using fuzzy sets“. Journal of the Acoustical Society of America 92, Nr. 4 (Oktober 1992): 2418–19. http://dx.doi.org/10.1121/1.404644.

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10

Malkin, R. A., und D. Alexandrou. „Acoustic classification of abyssopelagic animals“. IEEE Journal of Oceanic Engineering 18, Nr. 1 (1993): 63–72. http://dx.doi.org/10.1109/48.211495.

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11

Blevins, Matthew G., Edward T. Nykaza und William M. Nick. „Active learning for acoustic classification“. Journal of the Acoustical Society of America 142, Nr. 4 (Oktober 2017): 2620. http://dx.doi.org/10.1121/1.5014595.

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12

Xie, Jie, und Mingying Zhu. „Investigation of acoustic and visual features for acoustic scene classification“. Expert Systems with Applications 126 (Juli 2019): 20–29. http://dx.doi.org/10.1016/j.eswa.2019.01.085.

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13

Shete, Rupali. „Acoustic Signal Classification from Monaural Recordings“. International Journal of Intelligent Systems and Applications 6, Nr. 3 (08.02.2014): 62–68. http://dx.doi.org/10.5815/ijisa.2014.03.06.

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14

Dufaux, Alain, Laurent Besacier, Michael Ansorge und Fausto Pellandini. „Automatic classification of wideband acoustic signals“. Journal of the Acoustical Society of America 105, Nr. 2 (Februar 1999): 1359–60. http://dx.doi.org/10.1121/1.426440.

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15

Neff, M., G. Anton, A. Enzenhöfer, K. Graf, J. Hößl, U. Katz, R. Lahmann und C. Richardt. „Signal classification for acoustic neutrino detection“. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 662 (Januar 2012): S242—S245. http://dx.doi.org/10.1016/j.nima.2010.11.016.

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16

Themistocleous, Charalambos. „Dialect classification using vowel acoustic parameters“. Speech Communication 92 (September 2017): 13–22. http://dx.doi.org/10.1016/j.specom.2017.05.003.

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17

Wilson, Joshua D., und Nicholas C. Makris. „Acoustic detection and classification of hurricanes“. Journal of the Acoustical Society of America 108, Nr. 5 (November 2000): 2544. http://dx.doi.org/10.1121/1.4743433.

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18

Alexandrou, D., und D. Pantzartzis. „A methodology for acoustic seafloor classification“. IEEE Journal of Oceanic Engineering 18, Nr. 2 (April 1993): 81–86. http://dx.doi.org/10.1109/48.219527.

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19

Richardson, Michael, Warren Wood, Wolfgang Jans, Peter Fleischer, Keven Briggs, Dawn Lavoie, Dan Lott et al. „Recent advances in acoustic sediment classification“. Journal of the Acoustical Society of America 105, Nr. 2 (Februar 1999): 1207. http://dx.doi.org/10.1121/1.425678.

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20

Rippengill, S., K. Worden, K. M. Holford und R. Pullin. „Automatic Classification of Acoustic Emission Patterns“. Strain 39, Nr. 1 (Februar 2003): 31–41. http://dx.doi.org/10.1046/j.1475-1305.2003.00041.x.

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21

Jung, Jee-Weon, Hee-Soo Heo, Hye-Jin Shim und Ha-Jin Yu. „Knowledge Distillation in Acoustic Scene Classification“. IEEE Access 8 (2020): 166870–79. http://dx.doi.org/10.1109/access.2020.3021711.

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22

Roberts, Paul L. D., und Jules S. Jaffe. „Multiple angle acoustic classification of zooplankton“. Journal of the Acoustical Society of America 121, Nr. 4 (April 2007): 2060–70. http://dx.doi.org/10.1121/1.2697471.

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23

Nystuen, Jeffrey A., und Harry D. Selsor. „Weather Classification Using Passive Acoustic Drifters“. Journal of Atmospheric and Oceanic Technology 14, Nr. 3 (Juni 1997): 656–66. http://dx.doi.org/10.1175/1520-0426(1997)014<0656:wcupad>2.0.co;2.

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24

Thomas, P. „Stability classification by acoustic remote sensing“. Atmospheric Research 20, Nr. 2-4 (Dezember 1986): 165–72. http://dx.doi.org/10.1016/0169-8095(86)90022-0.

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25

Lee, Yerin, Soyoung Lim und Il-Youp Kwak. „CNN-Based Acoustic Scene Classification System“. Electronics 10, Nr. 4 (03.02.2021): 371. http://dx.doi.org/10.3390/electronics10040371.

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Acoustic scene classification (ASC) categorizes an audio file based on the environment in which it has been recorded. This has long been studied in the detection and classification of acoustic scenes and events (DCASE). This presents the solution to Task 1 of the DCASE 2020 challenge submitted by the Chung-Ang University team. Task 1 addressed two challenges that ASC faces in real-world applications. One is that the audio recorded using different recording devices should be classified in general, and the other is that the model used should have low-complexity. We proposed two models to overcome the aforementioned problems. First, a more general classification model was proposed by combining the harmonic-percussive source separation (HPSS) and deltas-deltadeltas features with four different models. Second, using the same feature, depthwise separable convolution was applied to the Convolutional layer to develop a low-complexity model. Moreover, using gradient-weight class activation mapping (Grad-CAM), we investigated what part of the feature our model sees and identifies. Our proposed system ranked 9th and 7th in the competition for these two subtasks, respectively.

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26

Prodeus, Arkadii Mykolaiovych. „Interpretability problem of classification signs in acoustic signals classification task“. Electronics and Communications 17, Nr. 6 (28.02.2013): 26–35. http://dx.doi.org/10.20535/2312-1807.2012.17.6.11393.

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27

Rathor, Sandeep, und R. S. Jadon. „Acoustic domain classification and recognition through ensemble based multilevel classification“. Journal of Ambient Intelligence and Humanized Computing 10, Nr. 9 (11.10.2018): 3617–27. http://dx.doi.org/10.1007/s12652-018-1087-6.

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28

Svatos, Jakub, Jan Holub und Jan Belak. „System for an acoustic detection, localisation and classification“. ACTA IMEKO 10, Nr. 2 (29.06.2021): 62. http://dx.doi.org/10.21014/acta_imeko.v10i2.1041.

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<p class="Abstract">Currently, acoustic detection techniques of gunshots (gunshot detection and its classification) are increasingly being used not only for military applications but also for civilian purposes. Detection, localisation, and classification of a dangerous event such as gunshots employing acoustic detection is a perspective alternative to visual detection, which is commonly used. In some situations, to detect and localise the source of a gunshot, an automatic acoustic detection system, which can classify the caliber, may be preferable. This paper presents a system for acoustic detection, which can detect, localise and classify acoustic events such as gunshots. The system has been tested in open and closed shooting ranges and tested firearms are 9 mm short gun, 6.35 mm short gun, .22 short gun, and .22 rifle gun with various subsonic and supersonic ammunition. As ‘false alarms’, sets of different impulse acoustic events like door slams, breaking glass, etc. have been used. Localisation and classification algorithms are also introduced. To successfully classify the tested acoustic signals, Continuous Wavelet and Mel Frequency Transformation methods have been used for the signal processing, and the fully two-layer connected neural network has been implemented. The results show that the acoustic detector can be used for reliable gunshot detection, localisation, and classification.</p>

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29

Teixeira, João Paulo, Nuno Alves und Paula Odete Fernandes. „Vocal Acoustic Analysis“. International Journal of E-Health and Medical Communications 11, Nr. 1 (Januar 2020): 37–51. http://dx.doi.org/10.4018/ijehmc.2020010103.

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Vocal acoustic analysis is becoming a useful tool for the classification and recognition of laryngological pathologies. This technique enables a non-invasive and low-cost assessment of voice disorders, allowing a more efficient, fast, and objective diagnosis. In this work, ANN and SVM were experimented on to classify between dysphonic/control and vocal cord paralysis/control. A vector was made up of 4 jitter parameters, 4 shimmer parameters, and a harmonic to noise ratio (HNR), determined from 3 different vowels at 3 different tones, with a total of 81 features. Variable selection and dimension reduction techniques such as hierarchical clustering, multilinear regression analysis and principal component analysis (PCA) was applied. The classification between dysphonic and control was made with an accuracy of 100% for female and male groups with ANN and SVM. For the classification between vocal cords paralysis and control an accuracy of 78,9% was achieved for female group with SVM, and 81,8% for the male group with ANN.

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30

Bruno, Michael, Alexander Sutin, Kil Woo Chung, Alexander Sedunov, Nikolay Sedunov, Hady Salloum, Hans Graber und Paul Mallas. „Satellite Imaging and Passive Acoustics in Layered Approach for Small Boat Detection and Classification“. Marine Technology Society Journal 45, Nr. 3 (01.05.2011): 77–87. http://dx.doi.org/10.4031/mtsj.45.3.10.

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AbstractThe research being conducted in the Center for Secure and Resilient Maritime Commerce, a Department of Homeland Security National Center of Excellence for Port Security, examines some basic science issues and emerging technologies to improve the security of ports and inland waterways as well as coastal and offshore operations. This research follows a layered approach, utilizing above-water and underwater surveillance techniques. The investigated layers include satellite-based wide-area surveillance, high-frequency radar systems providing over-the-horizon monitoring, and nearshore and harbor passive acoustic surveillance. In this paper, we present a brief review of the Stevens research in passive acoustics aimed at achieving underwater and surface targets detection, classification, and tracking. The passive acoustic data were combined with satellite imagery provided by the University of Miami CSTARS facility’s electro-optical and synthetic aperture radar satellite imaging capabilities. Advantages of concurrent use of satellite imaging and passive acoustics for maritime domain awareness are analyzed.

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31

Hovem, Jens M., und Hefeng Dong. „Understanding Ocean Acoustics by Eigenray Analysis“. Journal of Marine Science and Engineering 7, Nr. 4 (25.04.2019): 118. http://dx.doi.org/10.3390/jmse7040118.

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Acoustics is important for all underwater systems for object detection, classification, surveillance systems, and communication. However, underwater acoustics is often difficult to understand, and even the most carefully conducted measurements may often give unexpected results. The use of theory and acoustic modelling in support of measurements is very important since theory tends to be better behaved and more consistent than experiments, and useful to acquire better knowledge about the physics principle. This paper, having a tutorial flair, concerns the use of ray modelling and in particular eigenray analysis to obtain increased knowledge and understanding of underwater acoustic propagation.

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32

Bissell, Marie. „Automatic phonetic classification of vocalic allophones in Tol“. Proceedings of the Linguistic Society of America 6, Nr. 1 (20.03.2021): 403. http://dx.doi.org/10.3765/plsa.v6i1.4977.

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The aim of the present study involving automatic phonetic classification of /e/ and /u/ tokens in Tol is two-fold: first, I test existing claims about allophonic variation within these vowel classes, and second, I investigate allophonic variation within these vowel classes that has yet to be documented. The acoustic phonetic classifications derived in the present study contribute to a more detailed understanding of the allophonic systems operating within the Tol language. Operationalizing machine learning algorithms to investigate under-resourced, indigenous languages has the potential to provide detailed insights into the acoustic phonetic dynamics of a diverse range of vocalic systems.

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Mogas Recalde, Jordi, Ramon Palau und Marian Márquez. „How classroom acoustics influence students and teachers: A systematic literature review“. Journal of Technology and Science Education 11, Nr. 2 (27.04.2021): 245. http://dx.doi.org/10.3926/jotse.1098.

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Acoustics in schools have been studied during years, but nowadays there are more possibilities than ever before to introduce improvements. This study presents a systematic literature review determining what acoustic parameters are present in classrooms and how they affect both teachers and students. Following the analysis, we put forward a two-block classification: the physical parameters of the sound or noise in the classroom and the consequences of the acoustics on the people in the classroom. Advances in the design of learning spaces and the use of technologies ranging from devices and green material to advanced automation systems make it possible to direct acoustic solutions toward smarter learning spaces. This review also highlights the acoustic parameters to consider in smart classrooms (noise, reverberation, speech transmission and speech clarity) and the main effects of acoustics on teachers and students. Some conclusions and recommendations are drawn, but more research is needed in terms of school improvement considering acoustics influence and smart classrooms possibilities.

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34

Zhou, Lu Jun, Qiang Chen und Jian Qing Fang. „Acoustic Resonance Spectroscopy for Hazards Materials Classification“. Applied Mechanics and Materials 427-429 (September 2013): 686–90. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.686.

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In this paper, a measure system based on acoustic resonance is developed for hazards materials classification. It employs the lock-in amplifier as core processor to collect the acoustic resonance spectroscopy (ARS) of sealed containers which storied hazards materials. The transmitter and receiver are coupled externally to the wall of the container. The transmitter generates the sound by using a swept frequency source. The receiver on the opposite side of the wall of the container can detect the transmitted signal. The acoustic properties of hazards materials such as velocity and attenuation can be learned from the observed spectrum signal. Then multivariate methods are used to evaluate pretreatment methods, such as normalization, and classification possibilities of data collected by ARS in a laboratory environment. Principal component analysis (PCA) shows that it is possible to observe differences between samples using the data acquired from the ARS system. Further results obtained from Linear Discriminant Analysis (LDA) show that the identification rates for hazards materials classification are 100%.It is concluded that the ARS system has significant potential in the hazards materials classification.

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35

Parsa, Vijay, und Donald G. Jamieson. „Acoustic Discrimination of Pathological Voice“. Journal of Speech, Language, and Hearing Research 44, Nr. 2 (April 2001): 327–39. http://dx.doi.org/10.1044/1092-4388(2001/027).

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We investigated the ability of acoustic measures to discriminate between normal and pathological talkers. Two groups of measures were compared: (a) those extracted from sustained vowels and (b) those based on continuous speech samples. Nine acoustic measures, which include fundamental frequency and amplitude perturbation measures, long term average spectral measures, and glottal noise measures were extracted from both sustained vowel and continuous speech samples. Our experiments were performed on a published database of 53 normal talkers and 175 talkers with a pathological voice. The classification performance of the nine acoustic measures was quantified using linear discriminant analysis and receiver operating characteristic (ROC) curve analysis. When individual measures were considered in isolation, classification was more accurate for measures extracted from sustained vowels than for those based on continuous speech samples. Classification accuracy improved when combinations of acoustic parameters were considered. For such combinations of measures, classification results were comparable for measures extracted from continuous speech samples and for those based on sustained vowels.

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Zhu, Xuefeng, Guoyong Huang, Zao Feng und Jiande Wu. „Condition Classification of Water-Filled Underground Siphon Using Acoustic Sensors“. Sensors 20, Nr. 1 (28.12.2019): 186. http://dx.doi.org/10.3390/s20010186.

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Siphons have been widely used in water supply systems and sewage networks. However, it is difficult to implement non-destructive testing due to structural complexity and limited accessibility. In this paper, a novel condition classification method for water-filled underground siphons is proposed, which uses the acoustic signals received from acoustic sensors installed in the siphon. The proposed method has the advantages of simpler operation, lower cost, and higher detection efficiency. The acoustic wave forms in the siphons reflect on the system characteristics. Seven typical conditions of a water-filled underground siphon were investigated, and a series of experiments were conducted. Acoustic signals were recorded and transformed into acoustic pressure responses for further analysis. The variational mode decomposition (VMD) and the acoustic energy flow density were used for signal processing and feature extraction. The acoustic energy flux density eigenvectors were input to three different classifiers to classify the siphon conditions. The results demonstrate that the proposed acoustic-based approach can effectively classify the blockage and damage conditions of siphons, and the recognition accuracy of the proposed method is higher than 94.4%. Therefore, this research has value for engineering applications.

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37

Anderson, John T., D. Van Holliday, Rudy Kloser, Dave G. Reid und Yvan Simard. „Acoustic seabed classification: current practice and future directions“. ICES Journal of Marine Science 65, Nr. 6 (29.04.2008): 1004–11. http://dx.doi.org/10.1093/icesjms/fsn061.

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Abstract Anderson, J. T., Holliday, D. V., Kloser, R., Reid, D. G., and Simard, Y. 2008. Acoustic seabed classification: current practice and future directions. – ICES Journal of Marine Science, 65: 1004–1011. Acoustic remote sensing of the seabed using single-beam echosounders, multibeam echosounders, and sidescan sonars combined and individually are providing technological solutions to marine-habitat mapping initiatives. We believe the science of acoustic seabed classification (ASC) is at its nascence. A comprehensive review of ASC science was undertaken by an international group of scientists under the auspices of ICES. The review was prompted by the growing need to classify and map marine ecosystems across a range of spatial scales in support of ecosystem-based science for ocean management. A review of the theory of sound-scattering from seabeds emphasizes the variety of theoretical models currently in use and the ongoing evolution of our understanding. Acoustic-signal conditioning and data quality assurance before classification using objective, repeatable procedures are important technical considerations where standardization of methods is only just beginning. The issue of temporal and spatial scales is reviewed, with emphasis on matching observational scales to those of the natural world. It is emphasized throughout that the seabed is not static but changes over multiple time-scales as a consequence of natural physical and biological processes. A summary of existing commercial ASC systems provides an introduction to existing capabilities. Verification (ground-truthing) methods are reviewed, emphasizing the difficulties of matching observational scales with acoustic-backscatter data. Survey designs for ASC explore methods that extend beyond traditional oceanographic and fisheries survey techniques. Finally, future directions for acoustic seabed classification science were identified in the key areas requiring immediate attention by the international scientific community.

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Stanton, Timothy K. „Acoustic classification of a shell‐covered seafloor“. Journal of the Acoustical Society of America 105, Nr. 2 (Februar 1999): 1265. http://dx.doi.org/10.1121/1.426054.

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39

Redmon, Charles. „Acoustic classification of velar fricatives in Assamese“. Journal of the Acoustical Society of America 139, Nr. 4 (April 2016): 2016. http://dx.doi.org/10.1121/1.4949938.

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40

Lee, Suk-Myung, und Jeung-Yoon Choi. „Classification of Diphthongs using Acoustic Phonetic Parameters“. Journal of the Acoustical Society of Korea 32, Nr. 2 (31.03.2013): 167–73. http://dx.doi.org/10.7776/ask.2013.32.2.167.

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41

Park, Sangwook, Woohyun Choi und Hanseok Ko. „Acoustic scene classification using recurrence quantification analysis“. Journal of the Acoustical Society of Korea 35, Nr. 1 (31.01.2016): 42–48. http://dx.doi.org/10.7776/ask.2016.35.1.042.

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42

Averbuch, Amir, Valery Zheludev, Pekka Neittaanmäki, Pekka Wartiainen, Kari Huoman und Kim Janson. „Acoustic detection and classification of river boats“. Applied Acoustics 72, Nr. 1 (Januar 2011): 22–34. http://dx.doi.org/10.1016/j.apacoust.2010.09.006.

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43

Gaunaurd, Guillermo C. „Active acoustic classification via transient resonance scattering“. Optical Engineering 31, Nr. 12 (1992): 2553. http://dx.doi.org/10.1117/12.60012.

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44

Mellinger, David K. „Acoustic feature extraction and classification in Ishmael“. Journal of the Acoustical Society of America 134, Nr. 5 (November 2013): 3986. http://dx.doi.org/10.1121/1.4830526.

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45

Blevins, Matthew G., Steven L. Bunkley, Edward T. Nykaza, Anton Netchaev und Gordon Ochi. „Improved feature extraction for environmental acoustic classification“. Journal of the Acoustical Society of America 141, Nr. 5 (Mai 2017): 3964. http://dx.doi.org/10.1121/1.4989023.

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46

Ren, Zhao, Kun Qian, Zixing Zhang, Vedhas Pandit, Alice Baird und Bjorn Schuller. „Deep Scalogram Representations for Acoustic Scene Classification“. IEEE/CAA Journal of Automatica Sinica 5, Nr. 3 (Mai 2018): 662–69. http://dx.doi.org/10.1109/jas.2018.7511066.

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47

Waldekar, Shefali, und Goutam Saha. „Two-level fusion-based acoustic scene classification“. Applied Acoustics 170 (Dezember 2020): 107502. http://dx.doi.org/10.1016/j.apacoust.2020.107502.

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Djeddou, Mustapha, und Tayeb Touhami. „Classification and Modeling of Acoustic Gunshot Signatures“. Arabian Journal for Science and Engineering 38, Nr. 12 (04.09.2013): 3399–406. http://dx.doi.org/10.1007/s13369-013-0655-5.

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Guo, Baofeng, Mark S. Nixon und Thyagaraju Damarla. „Improving acoustic vehicle classification by information fusion“. Pattern Analysis and Applications 15, Nr. 1 (11.03.2011): 29–43. http://dx.doi.org/10.1007/s10044-011-0202-5.

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50

JAGNIATINSKIS, Aleksandras, Boris FIKS, Marius MICKAITIS und Ritoldas ŠUKYS. „Features of sound classification scheme designated to label buildings in Lithuania“. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 23, Nr. 3 (02.03.2017): 409–20. http://dx.doi.org/10.3846/13923730.2016.1269021.

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Annotation:

In Lithuania’s case, the legal requirements for the building acoustic quality since the year 2004 has been expressed through the sound classification scheme (SCS). The relationship of the subjective indoor acoustic comfort with the value of objective sound insulation was considered as a core for the classification scheme. SCS was designed to provide at least one sound class as a request for the newly erected building, other lower classes for reconstructed buildings and higher classes for premises with enhanced acoustic comfort. The adopted scheme contains five sound classes with various steps between them and is based on rating by two different sound insulation descriptors both having the same limit value. A request to protect against noise for newly erected and reconstructed buildings was enforced via the mandatory pre-completion acoustical testing. The database collected during testing allowed for the analysis of about 2000 in situ measurements of sound insulation properties of building partitions. It showed that the possibility of selecting either of the two airborne sound insulation descriptors Dn,T,w or R’w ensures better conformity with subjective comfort perception. This paper also addresses the particularities and advantages of simultaneous application of two different descriptors for regulation of sound insulation performance of dwellings.

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