In many applications, there are multiple interacting entities, generating time series of data over the space. To describe the relation within the set of data, the underlying topology may be used. In many real applicat...
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ISBN:
(纸本)9781728133775
In many applications, there are multiple interacting entities, generating time series of data over the space. To describe the relation within the set of data, the underlying topology may be used. In many real applications, not only the signal/data of interest is measured in noise, but it is also contaminated with outliers. The proposed method, called RGTL, infers the graph topology from noisy measurements and removes these outliers simultaneously. Here, it is assumed that we have no information about the space graph topology, while we know that graph signal are sampled consecutively in time and thus the graph in time domain is given. The simulation results show that the proposed algorithm has a better performance for different graph orders, compared with the conventional graph topology inference methods. Due to the nature of stock market data and the presence of noise and high power outliers, the proposed method is also applied to find some relations among selected ticker symbol prices in the USA market.
In the recent years, singing voice separation systems showed increased performance due to the use of supervised training. The design of training datasets is known as a crucial factor in the performance of such systems...
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ISBN:
(纸本)9781479981311
In the recent years, singing voice separation systems showed increased performance due to the use of supervised training. The design of training datasets is known as a crucial factor in the performance of such systems. We investigate on how the characteristics of the training dataset impacts the separation performances of state-of-the-art singing voice separation algorithms. We show that the separation quality and diversity are two important and complementary assets of a good training dataset. We also provide insights on possible transforms to perform data augmentation for this task.
A differential acoustic OFDM technique is presented to embed data imperceptibly in existing music. The method allows playing back music containing the data with a speaker without users noticing the embedded data chann...
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ISBN:
(纸本)9781479981311
A differential acoustic OFDM technique is presented to embed data imperceptibly in existing music. The method allows playing back music containing the data with a speaker without users noticing the embedded data channel. Using a microphone, the data can be recovered from the recording. Experiments with smartphone microphones show that transmission distances of 24 meters are possible, while achieving bit error ratios of less than 10 percent, depending on the environment. Furthermore, we present a user study which shows that many people do not recognize the added data channel in music, even when being informed about the experiment and therefore actively listening for the data transmission. Depending on the source music, data rates of 300 to 400 bits per second are achieved.
Biometric system security requires cryptographic protection of sample data under certain circumstances. We assess low complexity selective encryption schemes applied to JPEG2000 compressed iris data by conducting iris...
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ISBN:
(纸本)9781479981311
Biometric system security requires cryptographic protection of sample data under certain circumstances. We assess low complexity selective encryption schemes applied to JPEG2000 compressed iris data by conducting iris recognition on the selectively encrypted data. This paper specifically compares the effects of a recently proposed approach, i.e. applying selective encryption to normalised texture data, to encrypting classical sample data. We assess achieved protection level as well as computational cost of the considered schemes, and particularly highlight the role of segmentation in obtaining surprising results.
Applications of signalprocessing and control are classically model-based, involving a two-step procedure for modeling and design: first a model is built from given data, and second, the estimated model is used for fi...
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ISBN:
(纸本)9781479981311
Applications of signalprocessing and control are classically model-based, involving a two-step procedure for modeling and design: first a model is built from given data, and second, the estimated model is used for filtering, estimation, or control. Both steps typically involve optimization problems, but the combination of both is not necessarily optimal, and the modeling step often ignores the ultimate design objective. Recently, data-driven alternatives are receiving attention, which employ a direct approach combining the modeling and design into a single step. In earlier work, it was shown that datadriven signalprocessing problems can often be rephrased as missing data completion problems, where the signal of interest is part of an incomplete low-rank mosaic Hankel structured matrix. In this paper, we consider the exact data case and the problem of simulating from a given input, an output trajectory of the unknown data generating system. Our findings suggest that, when using an adequate rescaling of the given data, the exact data-driven simulation problem can be solved by replacing the original structured low-rank matrix completion problem by a convex optimization problem, using the nuclear norm heuristic.
Along with the rapid increase in information technology, there is a challenge in data security. For this reason, data protection has been a focus for some decades. One of the methods, called data hiding, is introduced...
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ISBN:
(纸本)9781728133775
Along with the rapid increase in information technology, there is a challenge in data security. For this reason, data protection has been a focus for some decades. One of the methods, called data hiding, is introduced to solve this security issue. It can protect secret data;nevertheless, the quality of the resulted stego data for a certain amount of the secret is limited. Moreover, the reversibility of the algorithm is also a concern. In this research, we intend to improve the method by combining Reduced Difference Expansion (RDE) and the concept of fuzzy logic, by considering some factors, such as brightness and its entropy value. It is to specify the number of embedding, which should be done for each block in an image. Therefore, the embedding level is dynamic, which means that it may be different for each block. The experimental result shows that this proposed method works well;adaptive such that it follows the characteristics of the carrier. Moreover, the method is also reversible that both the secret data and the carrier can be successfully reconstructed.
There are many use cases in singing synthesis where creating voices from small amounts of data is desirable. In text-to-speech there have been several promising results that apply voice cloning techniques to modern de...
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ISBN:
(纸本)9781479981311
There are many use cases in singing synthesis where creating voices from small amounts of data is desirable. In text-to-speech there have been several promising results that apply voice cloning techniques to modern deep learning based models. In this work, we adapt one such technique to the case of singing synthesis. By leveraging data from many speakers to first create a multispeaker model, small amounts of target data can then efficiently adapt the model to new unseen voices. We evaluate the system using listening tests across a number of different use cases, languages and kinds of data.
Sensor play an important role in the growth of big data. It is a basic operation for nodes to periodically transmit reports to a sink in many applications in a Wireless Sensor Network (WSN). Because of the limited ene...
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ISBN:
(纸本)9781728135557
Sensor play an important role in the growth of big data. It is a basic operation for nodes to periodically transmit reports to a sink in many applications in a Wireless Sensor Network (WSN). Because of the limited energy of the nodes, it is important to process data efficiently. Under such applications, a tree is generally adopted the path structure for maintaining the routing tables of sensors. In this paper, considering the difference in the amount of data collected and the speed of data acquisition, we assign different acquisition frequency to the nodes. In addition, we propose a new Minimum Spanning Tree (MST) algorithm which is devoted to construct a data aggregation tree with the lowest energy consumption so that the network lifetime is maximized. Simulation results illustrate that the proposed algorithm outperforms traditional MST in terms of energy consumption.
Many machine learning models lack the consideration that an adversary can alter data at the time of training or testing. Over the past decade, the machine learning models' vulnerability has been a concern and more...
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ISBN:
(纸本)9781479981311
Many machine learning models lack the consideration that an adversary can alter data at the time of training or testing. Over the past decade, the machine learning models' vulnerability has been a concern and more secure algorithms are needed. Unfortunately, the security of feature selection (FS) remains an under-explored area. There are only a few works that address data poisoning algorithms that are targeted at embedded FS;however, data poisoning techniques targeted at information-theoretic FS do not exist. In this contribution, a novel data poisoning algorithm is proposed that targets failures in minimum Redundancy Maximum Relevance (mRMR). We demonstrate that mRMR can be easily poisoned to select features that would not normally have been selected.
The spatial decomposition method decomposes acoustic room impulse responses into a pressure signal and a direction of arrival for each time instant of the pressure signal. An acoustic space can be auralized by distrib...
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ISBN:
(纸本)9781479981311
The spatial decomposition method decomposes acoustic room impulse responses into a pressure signal and a direction of arrival for each time instant of the pressure signal. An acoustic space can be auralized by distributing the pressure signal over the available loudspeakers or head-related transfer functions so that the required instantaneous propagation direction is recreated. We present a user study that demonstrates based on binaural auralization that the arrival directions can be synthesized from random data such that the auralization is nearly indistinguishable from the auralization of the original data. The presented concept constitutes the fundament of a highly scalable spatialization method for omnidirectional room impulse responses.
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