Academic literature on the topic 'Spectral-semantic model'

Due to the existing of the semantic gap, images with the same or similar low level features are possibly different on semantic level. How to find the underlying relationship between the high-level semantic and low level features is one of the difficult problems for image annotation. In this paper, a new image annotation method based on graph spectral clustering with the consistency of semantics is proposed with detailed analysis on the advantages and disadvantages of the existed image annotation methods. The proposed method firstly cluster image into several semantic classes by semantic similarity measurement in the semantic subspace. Within each semantic class, images are re-clustered with visual features of region Then, the joint probability distribution of blobs and words was modeled by using Multiple-Bernoulli Relevance Model. We can annotate a unannotated image by using the joint distribution. Experimental results show the the effectiveness of the proposed approach in terms of quality of the image annotation. the consistency of high-level semantics and low level features is efficiently achieved.

Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene classification. But it should be noticed that how the distinct features are fused to fully describe the challenging HSR images, which is a critical factor for scene classification. In this paper, a semantic-feature fusion fully sparse topic model (SFF-FSTM) is proposed for HSR imagery scene classification. In SFF-FSTM, three heterogeneous features – the mean and standard deviation based spectral feature, wavelet based texture feature, and dense scale-invariant feature transform (SIFT) based structural feature are effectively fused at the latent semantic level. The combination of multiple semantic-feature fusion strategy and sparse based FSTM is able to provide adequate feature representations, and can achieve comparable performance with limited training samples. Experimental results on the UC Merced dataset and Google dataset of SIRI-WHU demonstrate that the proposed method can improve the performance of scene classification compared with other scene classification methods for HSR imagery.

Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene classification. But it should be noticed that how the distinct features are fused to fully describe the challenging HSR images, which is a critical factor for scene classification. In this paper, a semantic-feature fusion fully sparse topic model (SFF-FSTM) is proposed for HSR imagery scene classification. In SFF-FSTM, three heterogeneous features – the mean and standard deviation based spectral feature, wavelet based texture feature, and dense scale-invariant feature transform (SIFT) based structural feature are effectively fused at the latent semantic level. The combination of multiple semantic-feature fusion strategy and sparse based FSTM is able to provide adequate feature representations, and can achieve comparable performance with limited training samples. Experimental results on the UC Merced dataset and Google dataset of SIRI-WHU demonstrate that the proposed method can improve the performance of scene classification compared with other scene classification methods for HSR imagery.

In this study, we present a hyperspectral image classification method by combining spectral, spatial, and semantic information. The main steps of the proposed method are summarized as follows: First, principal component analysis transform is conducted on an original image to produce its extended morphological profile, Gabor features, and superpixel-based segmentation map. To model spatial information, the extended morphological profile and Gabor features are used to represent structure and texture features, respectively. Moreover, the mean filtering is performed within each superpixel to maintain the homogeneity of the spatial features. Then, the k-means clustering and the entropy rate superpixel segmentation are combined to produce semantic feature vectors by using a bag of visual-words model for each superpixel. Next, three kernel functions are constructed to describe the spectral, spatial, and semantic information, respectively. Finally, the composite kernel technique is used to fuse all the features into a multiple kernel function that is fed into a support vector machine classifier to produce a final classification map. Experiments demonstrate that the proposed method is superior to the most popular kernel-based classification methods in terms of both visual inspection and quantitative analysis, even if only very limited training samples are available.

<p><strong>Abstract.</strong> Indoor scenes have the characteristics of abundant semantic categories, illumination changes, occlusions and overlaps among objects, which poses great challenges for indoor semantic segmentation. Therefore, we in this paper develop a method based on higher-order Markov random field model for indoor semantic segmentation from RGB-D images. Instead of directly using RGB-D images, we first train and perform RefineNet model only using RGB information for generating the high-level semantic information. Then, the spatial location relationship from depth channel and the spectral information from color channels are integrated as a prior for a marker-controlled watershed algorithm to obtain the robust and accurate visual homogenous regions. Finally, higher-order Markov random field model encodes the short-range context among the adjacent pixels and the long-range context within each visual homogenous region for refining the semantic segmentations. To evaluate the effectiveness and robustness of the proposed method, experiments were conducted on the public SUN RGB-D dataset. Experimental results indicate that compared with using RGB information alone, the proposed method remarkably improves the semantic segmentation results, especially at object boundaries.</p>

The effectiveness of CycleGAN is demonstrated to outperform recent approaches for semisupervised semantic segmentation on public segmentation benchmarks. In contrast to analog images, however, the acoustic images are unbalanced and often exhibit speckle noise. As a consequence, CycleGAN is prone to mode-collapse and cannot retain target details when applied directly to the sonar image dataset. To address this problem, a spectral normalized CycleGAN network is presented, which applies spectral normalization to both generators and discriminators to stabilize the training of GANs. Without using a pretrained model, the experimental results demonstrate that our simple yet effective method helps to achieve reasonably accurate sonar targets segmentation results.

In geospatial applications such as urban planning and land use management, automatic detection and classification of earth objects are essential and primary subjects. When the significant semantic segmentation algorithms are considered, DeepLabV3+ stands out as a state-of-the-art CNN. Although the DeepLabV3+ model is capable of extracting multi-scale contextual information, there is still a need for multi-stream architectural approaches and different training approaches of the model that can leverage multi-modal geographic datasets. In this study, a new end-to-end dual-stream architecture that considers geospatial imagery was developed based on the DeepLabV3+ architecture. As a result, the spectral datasets other than RGB provided increments in semantic segmentation accuracies when they were used as additional channels to height information. Furthermore, both the given data augmentation and Tversky loss function which is sensitive to imbalanced data accomplished better overall accuracies. Also, it has been shown that the new dual-stream architecture using Potsdam and Vaihingen datasets produced 88.87% and 87.39% overall semantic segmentation accuracies, respectively. Eventually, it was seen that enhancement of the traditional significant semantic segmentation networks has a great potential to provide higher model performances, whereas the contribution of geospatial data as the second stream to RGB to segmentation was explicitly shown.

Object detection and segmentation have recently shown encouraging results toward image analysis and interpretation due to their promising applications in remote sensing image fusion field. Although numerous methods have been proposed, implementing effective and efficient object detection is still very challenging for now, especially for the limitation of single modal data. The use of a single modal data is not always enough to reach proper spectral and spatial resolutions. The rapid expansion in the number and the availability of multi-source data causes new challenges for their effective and efficient processing. In this paper, we propose an effective feature information–interaction visual attention model for multimodal data segmentation and enhancement, which utilizes channel information to weight self-attentive feature maps of different sources, completing extraction, fusion, and enhancement of global semantic features with local contextual information of the object. Additionally, we further propose an adaptively cyclic feature information–interaction model, which adopts branch prediction to decide the number of visual perceptions, accomplishing adaptive fusion of global semantic features and local fine-grained information. Numerous experiments on several benchmarks show that the proposed approach can achieve significant improvements over baseline model.

When extracting land-use information from remote sensing imagery using image segmentation, obtaining fine edges for extracted objects is a key problem that is yet to be solved. In this study, we developed a new weight feature value convolutional neural network (WFCNN) to perform fine remote sensing image segmentation and extract improved land-use information from remote sensing imagery. The WFCNN includes one encoder and one classifier. The encoder obtains a set of spectral features and five levels of semantic features. It uses the linear fusion method to hierarchically fuse the semantic features, employs an adjustment layer to optimize every level of fused features to ensure the stability of the pixel features, and combines the fused semantic and spectral features to form a feature graph. The classifier then uses a Softmax model to perform pixel-by-pixel classification. The WFCNN was trained using a stochastic gradient descent algorithm; the former and two variants were subject to experimental testing based on Gaofen 6 images and aerial images that compared them with the commonly used SegNet, U-NET, and RefineNet models. The accuracy, precision, recall, and F1-Score of the WFCNN were higher than those of the other models, indicating certain advantages in pixel-by-pixel segmentation. The results clearly show that the WFCNN can improve the accuracy and automation level of large-scale land-use mapping and the extraction of other information using remote sensing imagery.

Among aquatic biota, corals provide shelter with sufficient nutrition to a wide variety of underwater life. However, a severe decline in the coral resources can be noted in the last decades due to global environmental changes causing marine pollution. Hence, it is of paramount importance to develop and deploy swift coral monitoring system to alleviate the destruction of corals. Performing semantic segmentation on underwater images is one of the most efficient methods for automatic investigation of corals. Firstly, to design a coral investigation system, RGB and spectral images of various types of corals in natural and artificial aquatic sites are collected. Based on single-channel images, a convolutional neural network (CNN) model, named DeeperLabC, is employed for the semantic segmentation of corals, which is a concise and modified deeperlab model with encoder-decoder architecture. Using ResNet34 as a skeleton network, the proposed model extracts coral features in the images and performs semantic segmentation. DeeperLabC achieved state-of-the-art coral segmentation with an overall mean intersection over union (IoU) value of 93.90%, and maximum F1-score of 97.10% which surpassed other existing benchmark neural networks for semantic segmentation. The class activation map (CAM) module also proved the excellent performance of the DeeperLabC model in binary classification among coral and non-coral bodies.

Диссертация на соискание ученой степени кандидата технических наук по специальности 05.13.06 – информационные технологии. – Национальный технический университет "Харьковский политехнический институт", Харьков, 2016. Диссертация посвящена решению научно-практической задачи разработки метода для повышения эффективности обнаружения и распознавания сигналов в радиолокационных комплексах путем интеллектуализации обработки сигнальной информации. В работе проанализированы научные достижения в области обработки сигналов, определены задачи обработки сигналов и подходы к их решению. В технологии обработки радиолокационных сигналов предложено выделить два этапа: внутриобзорная и междуобзорная обработка сигналов. На основе данного подхода разработаны спектрально-семантическая и пространственно-семантическая модели обработки радиолокационных сигналов. В работе усовершенствован метод формализации процессов восприятия и преобразования сигналов и сигнальных образов, который основан на компараторной идентификации, и позволяет определять семантическую составляющую сигналов и сигнальных образов на этапе предварительной обработки информации. Предложена реализация информационной технологии обработки сигналов в интеллектуальных радиолокационных комплексах с учетом спектрально-семантической и пространственно-семантической моделей. Данный подход позволяет моделировать процессы обработки и распознавания радиолокационных сигналов и сигнальных образов средствами алгебры конечных предикатов. На этапе внутриобзорной обработки сигналов и сигнальных образов объекты классифицируются по спектральному образу с помощью спектрально-семантической модели. Предварительная обработка пачки сигналов основана на формировании предикатной формы спектрального образа, затем на ее основе определяются значения признаков, и осуществляется идентификация объекта. На этапе междуобзорной обработки сигналов для уточнения результатов идентификации объектов используется пространственно-семантическая модель. Рассматривается система дискретных выборок – элементов обработки по дальности и азимуту. Для описания ситуации вокруг анализируемого в данный момент элемента изображения (элемента зоны обзора РЛС), вводится система предикатных признаков. По оценке значений признаков в каждом элементе обработки и полученным предикатным уравнениям определяется воздушный объект. Предложенная модель позволяет определять отметки воздушных объектов на фоне мешающих отражений и наблюдать динамику изменения в течение нескольких обзоров РЛС.
Thesis for a candidate degree in technical science, specialty 05.13.06 – Information Technologies. – National Technical University "Kharkiv Polytechnic Institute". – Kharkiv, 2016. This thesis deals with a topical theoretical and practical task to improve the efficiency of information technologies for the processing and identifying of radar signals. Scientific achievements in signal processing are analysed, tasks to process signals and approaches to their solution are determined in the thesis. It is proposed to distinguish two stages in the technology of radar signal processing: intrasurveillance and intersurveillance signal processing. On the basis of this approach, spectral-semantic and spatial-semantic models are developed. Testing and the evaluation of the research results, which are based on the information technology developed, are made. The results are put into practice in: the module of multisurveillance processing of radar signals and data for surveillance radars of the Ministry of Defence of Ukraine; the research project Development of Systems of Radiomonitoring and Passive Direction Finding; Scientific Production Firm Optima Ltd.; an educational process of the Department of Information Technologies and Mechatronics in Kharkov National Automobile and Highway University.

Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.13.06 – інформаційні технології. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2016. У дисертаційній роботі вирішена науково-практична задача розроблення методу для підвищення ефективності виявлення та розпізнавання сигналів в радіолокаційних комплексах шляхом інтелектуалізації обробки сигнальної інформації. У роботі проаналізовано наукові досягнення в галузі обробки сигналів, визначено задачі обробки сигналів та підходи до їх вирішення. У технології обробки радіолокаційних сигналів запропоновано виділити два етапи: внутрішньооглядова й міжоглядова обробка сигналів. На основі цього підходу створено спектрально-семантичну і просторово-семантичну моделі обробки радіолокаційних сигналів. Проведено апробацію й оцінку ефективності результатів дослідження, отриманих на базі розробленої інформаційної технології. Результати впроваджено в модулі багатооглядової обробки радіолокаційних сигналів та інформації для оглядових РЛС МО України, у науково-дослідному проекті "Розробка систем радіоконтролю, радіомоніторингу та систем пасивної пеленгації" ТОВ НПФ "Оптима", а також у навчальний процес кафедри інформаційних технологій та мехатроніки ХНАДУ.
Thesis for a candidate degree in technical science, specialty 05.13.06 – Information Technologies. – National Technical University "Kharkiv Polytechnic Institute". – Kharkiv, 2016. This thesis deals with a topical theoretical and practical task to improve the efficiency of information technologies for the processing and identifying of radar signals. Scientific achievements in signal processing are analysed, tasks to process signals and approaches to their solution are determined in the thesis. It is proposed to distinguish two stages in the technology of radar signal processing: intrasurveillance and intersurveillance signal processing. On the basis of this approach, spectral-semantic and spatial-semantic models are developed. Testing and the evaluation of the research results, which are based on the information technology developed, are made. The results are put into practice in: the module of multisurveillance processing of radar signals and data for surveillance radars of the Ministry of Defence of Ukraine; the research project Development of Systems of Radiomonitoring and Passive Direction Finding; Scientific Production Firm Optima Ltd.; an educational process of the Department of Information Technologies and Mechatronics in Kharkov National Automobile and Highway University.

Дисертаційна робота присвячена вирішенню актуальної науково-практичної проблеми в телекомунікаційних системах, а саме підвищення пропускної здатності каналу передачі семантичних мовних даних за рахунок ефективного їх кодування, тобто формулюється питання підвищення ефективності семантичного кодування, а саме – з якою мінімальною швидкістю можливо кодувати семантичні ознаки мовних сигналів із заданою ймовірністю безпомилкового їх розпізнавання? Саме на це питання буде дана відповідь у даному науковому дослідженні, що є актуальною науково-технічною задачею враховуючи зростаючу тенденцію дистанційної взаємодії людей і роботизованої техніки за допомогою мови, де безпомилковість функціонування даного типу систем безпосередньо залежить від ефективності семантичного кодування мовних сигналів. У роботі досліджено відомий метод підвищення ефективності семантичного кодування мовних сигналів на основі мел-частотних кепстральних коефіцієнтів, який полягає в знаходженні середніх значень коефіцієнтів дискретного косинусного перетворення прологарифмованої енергії спектра дискретного перетворення Фур'є обробленого трикутним фільтром в мел-шкалі. Проблема полягає в тому, що представлений метод семантичного кодування мовних сигналів на основі мел-частотних кепстральних коефіцієнтів не дотримується умови адаптивності, тому було сформульовано основну наукову гіпотезу дослідження, яка полягає в тому що підвищити ефективність семантичного кодування мовних сигналів можливо за рахунок використання адаптивного емпіричного вейвлет-перетворення з подальшим застосуванням спектрального аналізу Гільберта. Під ефективністю кодування розуміється зниження швидкості передачі інформації із заданою ймовірністю безпомилкового розпізнавання семантичних ознак мовних сигналів, що дозволить значно знизити необхідну смугу пропускання, тим самим підвищуючи пропускну здатність каналу зв'язку. У процесі доведення сформульованої наукової гіпотези дослідження були отримані наступні результати: 1) вперше розроблено метод семантичного кодування мовних сигналів на основі емпіричного вейвлетперетворення, який відрізняється від існуючих методів побудовою множини адаптивних смугових вейвлет-фільтрів Мейера з подальшим застосуванням спектрального аналізу Гільберта для знаходження миттєвих амплітуд і частот функцій внутрішніх емпіричних мод, що дозволить визначити семантичні ознаки мовних сигналів та підвищити ефективність їх кодування; 2) вперше запропоновано використовувати метод адаптивного емпіричного вейвлет-перетворення в задачах кратномасштабного аналізу та семантичного кодування мовних сигналів, що дозволить підвищити ефективність спектрального аналізу за рахунок розкладання високочастотного мовного коливання на його низькочастотні складові, а саме внутрішні емпіричні моди; 3) отримав подальший розвиток метод семантичного кодування мовних сигналів на основі мел-частотних кепстральних коефіцієнтів, але з використанням базових принципів адаптивного спектрального аналізу за допомогою емпіричного вейвлет-перетворення, що підвищує ефективність даного методу.
The thesis is devoted to the solution of the actual scientific and practical problem in telecommunication systems, namely increasing the bandwidth of the semantic speech data transmission channel due to their efficient coding, that is the question of increasing the efficiency of semantic coding is formulated, namely – at what minimum speed it is possible to encode semantic features of speech signals with the set probability of their error-free recognition? It is on this question will be answered in this research, which is an urgent scientific and technical task given the growing trend of remote human interaction and robotic technology through speech, where the accurateness of this type of system directly depends on the effectiveness of semantic coding of speech signals. In the thesis the well-known method of increasing the efficiency of semantic coding of speech signals based on mel-frequency cepstral coefficients is investigated, which consists in finding the average values of the coefficients of the discrete cosine transformation of the prologarithmic energy of the spectrum of the discrete Fourier transform treated by a triangular filter in the mel-scale. The problem is that the presented method of semantic coding of speech signals based on mel-frequency cepstral coefficients does not meet the condition of adaptability, therefore the main scientific hypothesis of the study was formulated, which is that to increase the efficiency of semantic coding of speech signals is possible through the use of adaptive empirical wavelet transform followed by the use of Hilbert spectral analysis. Coding efficiency means a decrease in the rate of information transmission with a given probability of error-free recognition of semantic features of speech signals, which will significantly reduce the required passband, thereby increasing the bandwidth of the communication channel. In the process of proving the formulated scientific hypothesis of the study, the following results were obtained: 1) the first time the method of semantic coding of speech signals based on empirical wavelet transform is developed, which differs from existing methods by constructing a sets of adaptive bandpass wavelet-filters Meyer followed by the use of Hilbert spectral analysis for finding instantaneous amplitudes and frequencies of the functions of internal empirical modes, which will determine the semantic features of speech signals and increase the efficiency of their coding; 2) the first time it is proposed to use the method of adaptive empirical wavelet transform in problems of multiscale analysis and semantic coding of speech signals, which will increase the efficiency of spectral analysis due to the decomposition of high-frequency speech oscillations into its low-frequency components, namely internal empirical modes; 3) received further development the method of semantic coding of speech signals based on mel-frequency cepstral coefficients, but using the basic principles of adaptive spectral analysis with the application empirical wavelet transform, which increases the efficiency of this method. Conducted experimental research in the software environment MATLAB R2020b showed, that the developed method of semantic coding of speech signals based on empirical wavelet transform allows you to reduce the encoding speed from 320 to 192 bit/s and the required passband from 40 to 24 Hz with a probability of error-free recognition of about 0.96 (96%) and a signal-to-noise ratio of 48 dB, according to which its efficiency increases 1.6 times in contrast to the existing method. The results obtained in the thesis can be used to build systems for remote interaction of people and robotic equipment using speech technologies, such as speech recognition and synthesis, voice control of technical objects, low-speed encoding of speech information, voice translation from foreign languages, etc.

AbstractIn this chapter, we present an advanced machine learning strategy to detect objects and characterize traffic dynamics in complex urban areas by airborne LiDAR. Both static and dynamical properties of large-scale urban areas can be characterized in a highly automatic way. First, LiDAR point clouds are colorized by co-registration with images if available. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per-pixel or per-point. Then, various spatial-statistical and spectral features can be extracted using a cuboid volumetric neighborhood. The most important features highlighted by the feature-relevance assessment, such as LiDAR intensity, NDVI, and planarity or covariance-based features, are selected to span the feature space for the AdaBoost classifier. Classification results as labeled points or pixels are acquired based on pre-selected training data for the objects of building, tree, vehicle, and natural ground. Based on the urban classification results, traffic-related vehicle motion can further be indicated and determined by analyzing and inverting the motion artifact model pertinent to airborne LiDAR. The performance of the developed strategy towards detecting various urban objects is extensively evaluated using both public ISPRS benchmarks and peculiar experimental datasets, which were acquired across European and Canadian downtown areas. Both semantic and geometric criteria are used to assess the experimental results at both per-pixel and per-object levels. In the datasets of typical city areas requiring co-registration of imagery and LiDAR point clouds a priori, the AdaBoost classifier achieves a detection accuracy of up to 90% for buildings, up to 72% for trees, and up to 80% for natural ground, while a low and robust false-positive rate is observed for all the test sites regardless of object class to be evaluated. Both theoretical and simulated studies for performance analysis show that the velocity estimation of fast-moving vehicles is promising and accurate, whereas slow-moving ones are hard to distinguish and yet estimated with acceptable velocity accuracy. Moreover, the point density of ALS data tends to be related to system performance. The velocity can be estimated with high accuracy for nearly all possible observation geometries except for those vehicles moving in or (quasi-)along the track. By comparative performance analysis of the test sites, the performance and consistent reliability of the developed strategy for the detection and characterization of urban objects and traffic dynamics from airborne LiDAR data based on selected features was validated and achieved.

This article is devoted to the innovative violin method of teaching which reveals different angles of the paradigm processes of the formation of the text of violin instrumental music of the era on Romanticism and the semantic organization of the musical narrative in the development of the intertextual dialogue "one's own-another's" on the material of the 5th Caprice for Paganini's violin-solo. The problem of dialogue in art in a broad sense is now one of the most popular both in modern domestic musicology and in culture in general. This exploration constitutes a fragmented but very important section from the structural elements of which the research innovative violin methodology is formed and consist, which rapidly increases motility, qualitatively alters of sound production, accurately, clear articulation, uses new technology that instantly allows you get rid of tension in the muscles during the performances of musical pieces, regardless of the speed of the pace of the instrumental pieces being performed, allows instant concentration and helps to overcome the stage excitement of the performer, using a combination of carefully selected instrumental and psychological tools (attitude and practices). The technique pushed the author to highlight the main directions and principles of interaction of textual reincarnations. The stated problem significantly expands the spectral directions in the study of the semantic organization of narratives in research exploration. The study of the texts of instrumental pieces in intertextual interactions allows to get into the processes of text creation and to investigate their influence on the realization of the final idea of the composer and performer. The process of forming the text of instrumental music as the end result and the end point of full-fledged research is an open scientific problem. The peculiarity of this study is the intersection of musicological, cultural and psychological views on the stated issues. We’re guided in its work by various multifunctional methodologies of several scientific areas: from purely musical to culturological (using synergistic, hermeneutic, gnoseological and aesthetic approaches) and psychological (relying on the knowledge of the psychology of creativity using various setting and practices, existential approach). The use of research innovative violin technique makes this study unique and different from previous explorations. The approach to studying the text of any volume and complexity will be the key to a better understanding of the cultural dialogue of postmodern times, when the performer face to face enters into an imaginary dialogue of artists in the study of cultural processes, to better understand themselves, to clarify cultural, psychological and musicological paradigms of reading an artistic text. The study of the semantic mechanisms of the instrumental text will provide an opportunity to delve into the processes of textual dialogue and will reveal the features of the artistic possibilities of the theme of Caprice 5, which acts as an intertextual model for any instrumental pieces, regardless of the time of their creation. Culture reveals its meanings through the stylistics of texts, which is why it is so important to master different ways of thinking. And cultural stereoscopicity will allow us to consider the problem of intertext mechanisms polyphonically and dynamically, from the study of cultural codes and communications to the revival of cultural space in the polyphony of existence.

Fans are main components e.g. in heating, ventilating and air conditioning systems for vehicles or buildings, cooling units of engines and electronic circuits, and household appliances such as kitchen exhaust hoods or vacuum cleaners. End-users increasingly demand a high sound quality of their system or device. The overall objective of a recent research project at the University of Siegen is a multidimensional assessment of fan sound quality. In a first step an advanced novel semantic differential for the assessment of fan-related sounds is established with the aid of carefully designed jury tests. Eventually, this semantic differential is employed for sound quality jury tests of fans in kitchen exhaust hoods, heat pumps and air purifiers as a first case. Finally, a prediction model is suggested, which relates the outcome from the jury tests to objective metrics. A principal component analysis is carried out and yields five main assessment criteria with 23 relevant adjective scales. The results show that the perceived sound quality of fan systems is mainly determined by the loudness and tonality of the sound. The spectral content (represented by the sharpness) as well as the time structure (represented by the roughness) have no significant impact on perceived sound quality of the fan systems investigated.