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Undecimated and decimated multivariate empirical mode decomposition filter banks (MEMDFBs) are introduced in order to incorporate MEMD equipped with downsampling into any arbitrary tree structure and provide flexibility in the choice of frequency bands. Undecimated MEMDFBs show the same results as those of original MEMD for an octave tree structure. Since the exact cut-off frequencies of MEMD are not known (i.e. due to data-driven decomposition), employing just simple downsampling in MEMD might cause aliasing. However, decimated MEMDFBs in this paper achieve perfect reconstruction with aliasing cancelled for any arbitrary tree. Applications of decimated/undecimated MEMDFBs for speech/audio and image signals are also included. Since decimated MEMDFBs can be applied into any arbitrary tree structure, this extends into MEMD packets. Arbitrary tree structures in decimated MEMDFBs also lead to more diverse choices in frequency bands for various multivariate applications requiring decimations.

With the increasing popularity of automatic speaker verification (ASV), the reliability of ASV systems has also gained importance. ASV is vulnerable to various spoofing attacks, especially replay attacks. Thus, recent public competitions and studies based on spoofing attack detection for ASV have mainly focused on the detection of replay attacks. Generally, replayed speech includes the attributes of one playback and two recording devices: the playback device, the recording device used by the attacker, and the recording device embedded in any system to verify input utterances. Therefore, the main attributes differentiating a replayed speech from the genuine speech are the attributes of the playback and the recording devices used by the attacker. In this paper, we propose a novel replay attack and its defense through observation of the general speech-spoofing process. The proposed attack includes only the attribute of one recording device embedded in an ASV system; genuine speech passes through the recording device only once, and the replayed speech produced for the proposed attack passes through the same recording device twice. Because the proposed attack is feasible, it can be considered a new task for replay countermeasures in the training process in order to develop a robust ASV protection system. The experimental results show that this novel replay attack cannot be detected by several of the existing state-of-the-art replay attack detection systems. Furthermore, the new attack can be detected by the same systems successfully if they are retrained with an appropriate dataset designed for the new task. INDEX TERMS Automatic speaker verification, replay attack, same recording device, spoofing detection.

This paper explores the in vivo distributed detection of an undesired biological agent's (BAs) biomarkers by a group of biological sized nanomachines in an aqueous medium under drift. The term distributed, indicates that the system information relative to the BAs presence is dispersed across the collection of nanomachines, where each nanomachine possesses limited communication, computation, and movement capabilities. Using Brownian motion with drift, a probabilistic detection and optimal data fusion framework, coined molecular distributed detection, will be introduced that combines theory from both molecular communication and distributed detection. Using the optimal data fusion framework as a guide, simulation indicates that a sub-optimal fusion method exists, allowing for a significant reduction in implementation complexity while retaining BA detection accuracy.

This paper presents a novel set of critical band filterbanks, i.e. filters that mimic the human auditory system (HAS). The filterbanks are based on the empirical mode decomposition (EMD). Two cases are investigated: decimated and undecimated filters. Since the HAS does not follow conventional linear and stationary properties, non-uniform filterbanks approximating critical bands with EMD are developed. The EMD is a data-driven decomposition and, as such, is well suited to deal with nonlinear and nonstationary signals. Thus, it is natural that it is good fit for modeling the HAS both for speech and audio systems. As an application of the developed non-uniform filterbanks, noise removal is applied into each EMD critical band so that the auditory masking effect within the critical bands can be utilized in speech enhancement with the properties of EMD. The speech enhancement in the proposed EMD critical bands is compared in this paper with a speech enhancement algorithm that removes colored noises through simultaneous diagonalization of covariance matrices. Since the proposed filterbanks are very flexible in designing arbitrary tree structures, it is expected they can be used in various applications.