Bulk download - click topic to download Zip archive of all papers related to that topic: Applications Loudspeakers Loudspeakers & Microphones Network Perception Perception & Evaluation Posters: Applications Posters: Loudspeakers Posters: Loudspeakers & Microphones Posters: Perception Posters: Perception & Evaluation Posters: Rec/Pro/Edu Posters: Room Acoustics Posters: Signal Processing Posters: Spatial Audio Rec/Pro/Edu Room Acoustics Signal Processing Spatial Audio
An interlaboratory round robin test to measure a hall’s reverberation time is performed using different equipment employed by 17 professional participants. All common measuring methods are represented: direct recording of impulse responses using impulse sound sources, indirect impulse response generation using software, and recording interrupted noise. Systematic differences are detected between these methods. Scatterings in measurement results are generally low, and, compared to previous studies, standard deviation is similar however some participants clearly differed in performance from others. The interrupted noise method shows the largest data scatter. Reverberation time specified as a single value shows large differences because the number is determined in a variety of ways. A revision of ISO 3382 is recommended
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This paper presents an application-oriented approach to Acoustic Source Localization using a coincident microphone array. Multiple processing blocks are presented to generate a reactive, yet stable Direction of Arrival estimation tuned toward speaker tracking. Building on an energy based scanning method, individual characteristics, such as sound field directivity and static sound source positions are used for adaptive smoothing of the detected angle. The methods and resulting performance gain are discussed for the individual components of the algorithm. Objective performance is evaluated using simulated and recorded data. Audio quality is assessed using listening tests, which show a significant increase in subjective sound quality, noise suppression, and speech intelligibility when combining the tracker with a beamforming algorithm for coincident microphone arrays.
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Audio post-production for film involves the manipulation of large amounts of audio data. There is a need for the automation of many organization tasks currently performed manually by sound engineers, such as grouping and renaming multiple audio recordings. Here, we present a method to classify such sound files in two categories, ambient recordings and single-source sounds. Automating these classification tasks requires a deep learning model capable of answering questions about the nature of each sound recording based on specific features. This study focuses on the relevant features for this type of audio classification and the design of one possible model. In addition, an evaluation of the model is presented, resulting in high accuracy, precision and recall values for audio classification.
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Photogrammetric computational methods can acquire precise personal head, external ear, and upper torso shapes using video captured with a mobile phone. We analyze the accuracy and repeatability of generating such 3D ge-ometry information. A known head and torso simulator (Kemar) as well as a 3D printed plastic head-and-torso dummy of a real person are considered. The resulting 3D geometry data is compared to the manufacturer’s or the actual 3D geometry. Shape differences between geometries are analyzed. The computer-modeled head-related transfer functions implied by the geometries are compared. The impacts of differences in geometries are dis-cussed. Photogrammetric determination of the 3D head-and-torso geometry can have sufficient accuracy to allow the computation of realistic personal head-related transfer function.
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During the monitoring of the behavior of different types of exciters attached to the same loudspeaker panel, significant deviations of the upper-frequency limit were discovered. These deviations depend on the resonance of the voice coil former and cannot be explained with the linear T/S parameters. This paper shows the indirect and direct measurement of the voice coil’s break-up with two exemplary exciters. Furthermore, an FE-simulation model has been built to validate and visualize these break-up modes. Finally, a prototype with reinforced structure was constructed to increase the resonance frequency and to extend the frequency range of the loudspeaker panel.
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A binaural rendering addon based on the EBU ADM Renderer has been developed, which renders Next Generation Audio scenes using virtual loudspeaker systems. During development, two optimization approaches emerged regarding rendering quality and efficiency. The first approach concerns the rendering of coherent signals from different emitter positions, which is particularly relevant for virtual speakers since object positioning is based on amplitude panning. The second approach concerns the reduction of computational costs when rendering the binaural room response by using different virtual speaker layouts for the rendering of the direct path and of the room response. To evaluate both approaches, a listening test was conducted. The results of this test showed that each approach positively influenced either rendering quality or performance.
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This paper presents a subjective study of spatial impression attributes and their perception in a 6 Degree-Of-Freedom context. For a plausible reproduction of virtual acoustics in a VR/AR/MR it is important to understand how different spatial impression attributes change perceptually for different positions in the room and for different head orientations. An elicitation test was carried out using the Repertory Grid Technique (RGT), in a virtual environment, using Binaural Room Impulse Responses (BRIRs) recorded in a reverberant concert hall. Results show an agreement with current literature and a previous elicitation test. New attributes such as “echo/reverb directionality” were found to also be perceived by participants, as a result of the different listening positions and head orientations.
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In this paper, we propose a method for sound ?eld reproduction using an elliptical loudspeaker array and a circular microphone array. This study aims to reproduce the interior sound ?eld of the elliptical loudspeaker array in a 2-dimensional sound ?eld. The solutions of the Helmholtz equation in an elliptical coordinate system, which are known as Mathieu functions, are used to expand the sound ?eld in an elliptical-coordinate-system-based wave domain. We provide a method to transform the signals recorded by a circular microphone array that are generally expanded with circular harmonics to the Mathieu expansion coef?cients. These coef?cients can be applied to derive the driving functions of loudspeakers. Computer simulations show that the method is valid for sound ?eld reproduction.
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When performing microphone measurements, all obstacles that may disturb the sound field around the microphone are removed. The microphone may even be suspended by thin wires to ensure that the influence of a mic stand is avoided. In real life, some microphones are managed somewhat differently, handheld microphones in particular. Some artists even prefer to cover most of the microphone grid with their hand (“cupping”). This habit affects the performance of the microphone. This paper presents measurements of the influence of five different hand positions on handheld vocal microphones. Both frequency response and directivity are measured.
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During the recent years, convolutional neural networks have been the standard on audio semantics, surpassing traditional classification approaches which employed hand-crafted feature engineering as front-end and various classifiers as back-end. Early studies were based on prominent 2D convolutional topologies for image recognition, adapting them to audio classification tasks. After the surge of deep learning in the past decade, real end-to-end audio learning, employing algorithms that directly process waveforms are to become the standard. This paper attempts a comparison between deep neural setups on typical audio classification tasks, focusing on optimizing 1D convolutional neural networks that can be deployed on various audio in-formation retrieval tasks, such as general audio detection and classification, environmental sound or speech emotion recognition.
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