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BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200924T090000
DTEND;TZID=Europe/Berlin:20200924T100000
DTSTAMP:20260426T055151
CREATED:20200909T151451Z
LAST-MODIFIED:20200922T171712Z
UID:1047-1600938000-1600941600@blog.rwth-aachen.de
SUMMARY:Ermert: Examining the influence of controlled HRTF-variation on various distance measures
DESCRIPTION:Binaural hearing is a complex process that is significantly influenced by physical attributes of the listener. These dependencies on the shape of the torso\, head\, but also the resonance properties of the pinna can be summarized in a digital filter\, the so-called head-related transfer function (HRTF). Such HRTFs are not only widely used in the entertainment industry\, for example for virtual reality\, but also in medical applications like hearing aid calibration. However\, the fact that HRTFs must be highly individual for a realistic listening experience is a major obstacle to their usage. Especially deviations in the dimensions of the head and pinna have a significant impact on the subject’s orientation ability. \nIndividualized HRTFs are an alternative to individual filters\, which can only be recorded requiring considerable time and computational effort\, as well as special equipment such as an anechoic chamber. An established approximation procedure of such individual transfer functions is the principal component analysis (PCA)\, a statistical method which is used for reconstructing HRTFs depending on the listener’s anthropometric dimensions. Another individualization procedure examined in this work is frequency scaling. \nHowever\, those simulated filters do not represent the original HRTFs flawlessly. Due to their high dimensionality\, HRTFs comparison is not an intuitive task and comprehensive visualization is challenging. To enable a meaningful comparison\, so called distance measures are required. Although numerous distance measures have been developed\, there have been few comparative studies on their attributes. \nIn this thesis a better understanding of the behavior of distance measures shall be achieved. Various distance measures are presented and evaluated. Subsequently\, simulated HRTFs are varied in a controlled manner to display the behavior of the different distance measures. These insights are finally applied to several HRTF measurements to compare measurement setups in a purposeful way. \nMelden Sie sich Hier an um die Einladung zu dem Vortrag per E-Mail zu erhalten.\nRegister here to receive the invitation to this talk via E-Mail. \nZoom-Meeting-ID: 912 7408 8155\nPasswort: 130862
URL:https://blog.rwth-aachen.de/akustik/event/ermert-wird-noch-bekannt-gegeben/
LOCATION:Zoom-Meeting
CATEGORIES:Verteidigung Masterarbeit
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BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200924T110000
DTEND;TZID=Europe/Berlin:20200924T120000
DTSTAMP:20260426T055151
CREATED:20200903T065628Z
LAST-MODIFIED:20200920T120329Z
UID:1101-1600945200-1600948800@blog.rwth-aachen.de
SUMMARY:Tegetmeyer-Kleine: Machine learning for pass-by vehicle sound classification
DESCRIPTION:In order to provide a diagnosis about the urban sound environment\, especially for traffic\, sound classification using deep learning with convolutional neural networks has received increasing attention. There are already some attempts of deep convolutional neural networks (CNN) for environmental sound classification and data augmentation. These CNNs are using spectro-temporal patterns as its input to identify or classify a given sound by recognizing features from these patterns. However\, to train these networks\, a lot of data is required. To solve this problem data augmentation is used\, which is a method to recreate one or more existing training samples to receive additional training data. In this thesis a state-of-the-art audio data augmentation technique with help of a virtual reality environment is introduced. A synthesized sound signal is designed using an existing sound data set of vehicle pass-by sounds with combustion engines. The synthesized signal is generated by using spectral granular synthesis. This signal is implemented in a virtual reality environment and generates the desired training data set (virtual pass-by sounds) for different neural networks (NNs)\, like CNNs\, recurrent neural networks (RNNs) and convolutional recurrent neural networks (CRNNs). Furthermore\, audio descriptors like the mel frequency cepstral coefficients (MFCCs) and the frequency spectrum are extracted from the generated data set. This information is used as the input for the NNs. Finally these NNs are evaluated with the help of the original recordings and predictions are made with completely untouched recordings (not used for training or validation). In addition\, a case study of the model’s applicability was conducted using real-world measurements. The models are examined in two different ways: first\, a vehicle is inspected at different speeds and second several vehicles are examined at the same speed. \nMelden Sie sich Hier an um die Einladung zu dem Vortrag per E-Mail zu erhalten.\nRegister here to receive the invitation to this talk via E-Mail. \nZoom-Meeting-ID: 975 1160 1777\nPasswort: 638806
URL:https://blog.rwth-aachen.de/akustik/event/tegetmeyer-kleine-machine-learning-for-pass-by-vehicle-sound-classification/
LOCATION:Zoom-Meeting
CATEGORIES:Verteidigung Masterarbeit
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