1,720,965 research outputs found
Digital Filters Design for Personal Sound Zones: A Neural Approach
No full textThe creation of Personal Sound Zones (PSZ) is a recent application of digital signal processing that allows differentiating the sound intensity in neighboring regions of space (e.g. a 'bright' and a 'dark' zone). Given the impulse responses of the environment, digital filters can be designed in order to obtain an attenuation of the signal in the dark zone as a result of the superposition of the filtered IR coming from each loudspeaker. A neural optimization approach was recently shown to enable PSZ by designing digital FIR filters. In this work we propose an improvement of that neural optimization approach using a simpler neural network architecture. Furthermore we extend the method to the design of IIR filters, which is computationally more effective for a real-time implementation. The neural technique is compared with two state-of-the-art methods, analyzing the performance in terms of Acoustic Contrast. Experiments have been performed using a vehicle composed of standard loudspeakers and two speaker arrays, and show that the proposed approach achieves remarkable Acoustic Contrast without sacrificing audio quality
Deep Optimization of Parametric IIR Filters for Audio Equalization
Full text linkThis paper describes a novel Deep Learning method for the design of IIR parametric filters for automatic multipoint audio equalization, that is the task of improving the sound quality of a listening environment at multiple listening points employing multiple loudspeakers. The filters are designed to approximate the inverse of the RIR and achieve almost flat magnitude response. A simple and effective neural architecture, named BiasNet, is proposed to determine the IIR equalizer parameters. This novel architecture is conceived for optimization and, as such, is able to produce optimal IIR equalizer parameters at its output, after training, with no input required. In absence of input, the presence of learnable non-zero bias terms ensures that the network works properly. An output scaling method is used to obtain accurate tuning of the IIR filters center frequency, quality factor and gain. All layers involved in the proposed method are shown to be differentiable, allowing backpropagation to optimize the network weights and achieve, after a number of training iterations, the optimal output according to a given RIR. The parameters are optimized with respect to a loss function based on a spectral distance between the measured and desired magnitude response, and a regularization term is used to keep the same microphone-loudspeaker energy balance after equalization. Two experimental scenarios are employed, a room and a car cabin, with several loudspeakers. The performance of the proposed method improves over the baseline techniques and achieves an almost flat band at a lower computational cost
A Novel Step Detection and Step Length Estimation Algorithm for Hand-held Smartphones
No full textIn this paper, we present an innovative inertial navigation system based on the data collected through the Inertial Measurement Unit (IMU) embedded in a commercial smart-phone. We propose an innovative step detection algorithm which is independent of the holding mode, the only assumption being that the device is hand-held (i.e., the user is texting/navigating or phoning) and its movement is related to the upper body displacement during walking. We also present a new approach able to automatically calibrate the step length estimation formula according to the smartphone positioning. The developed algorithms have been validated through a test campaign in which we have evaluated the system performance considering three different smartphone models and different path lengths. The obtained results show that the maximum step detection error is always below 4% (average: 2.08%; standard deviation: 1.82%) whereas the maximum path length estimation error is below 8.1% (average: 3.6%; standard deviation: 1.81%) in all the considered cases
Experimental Analysis and Design Guidelines for Microphone Virtualization in Automotive Scenarios
Full text linkIn this article, a performance analysis on the estimation of the so-called observation filter for the Virtual Microphone Technique (VMT) in a realistic automotive environment is presented. A performance comparison between adaptive and fixed observation filter estimation methods, namely Least Mean Square (LMS) and Minimum Mean Square Error (MMSE), respectively, was carried on. Two different experimental setups were implemented on a popular B-segment car. Eight microphones were placed at the monitoring and virtual positions in order to sense environmental acoustic noise propagating within the cabin of the car running at variable speed on a smooth asphalt. Our experimental results show that a large spectral coherence between monitoring and virtual microphone signals indicates a potentially effective and relatively wide-band virtual microphone signal reconstruction. The fixed observation filter estimation method achieves better performance than the adaptive one, guaranteeing remarkable broadband estimation accuracy. Moreover, for each considered setup, design guidelines are proposed to obtain a good trade-off between estimation accuracy and material costs
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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