Collision is a familiar problem and one of the largest disadvantages in RFID system. There are always two methods to deal with the collision problems. One is the deterministic collision resolution and the other is the stochastic collision resolution. The ALOHA-based anti-collision algorithms belong to the latter. In this paper, the ALOHA-based anti-collision algorithms are introduced and summarized. The most important is that two Tag estimation methods which are necessary to the DFSA algorithm (Dynamic framed slotted ALOHA algorithm) are investigated using the conventional ternary feedback model. How to use these two Tag estimation methods is also analyzed.
In current playback speech detection (PSD) Letter, commonly used features are often extracted from magnitude spectrum while phase spectrum information is not used. In order to extract more discriminative information for PSD, the idea of magnitude-phase spectrum (MPS) is proposed. Then a new feature based on MPS is proposed, namely constant-Q magnitude-phase octave coefficients (CMPOC). The experimental result on ASVspoof 2017 evaluation set using CMPOC indicates that: (i) the performance of CMPOC is better than features extracted not only from magnitude spectrum but also from MPS. (ii) CMPOC performs better than some commonly used features. (iii) Their system gives better performance than some known systems.
Conventional speaker verification systems become frail or incompetent while facing attack from spoofed speech. Presently many anti-spoofing countermeasures have been studied for automatic speaker verification. It has been known that the salient feature is of a more important role rather than the selection of classifiers in the current research field of spoofing detection. The effectiveness of constant-Q transform (CQT) has been demonstrated for anti-spoofing feature analysis in many research literatures on automatic speaker verification. On the basis of CQT-based information sub-features, i.e. octave-band principal information (OPI), full-band principal information (FPI), short-term spectral statistics information (STSSI) and magnitude-phase energy information (MPEI), three concatenated features are proposed by investigating their information complementarity in this paper, the first one is constant-Q statistics-plusprincipal information coefficients (CQSPIC) by combining OPI, FPI and STSSI; the second one is constant-Q energy-plus-principal information coefficients (CQEPIC) by combining OPI, FPI and MPEI and the third one is constant-Q energy-statistics-principal information coefficients (CESPIC) by combining OPI, FPI, MPEI and STSSI. In this paper, we set up deep neural network (DNN) classifiers for evaluation of the proposed features. Experiments show that the proposed features can outperform some commonly used features meanwhile the proposed systems give better or comparable performance comparing with state-ofthe-art performance on ASVspoof 2019 logical access and physical access corpus.
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