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Artificial bandwidth extension (ABE) algorithms can improve speech quality when wideband devices are used with narrowband

Generalized frequency division multiplexing (GFDM) is considered a nonorthogonal waveform and can cause difficulties when used in the spatial multiplexing mode of a multiple-input-multiple-output (MIMO) scenario. In this paper, a class of GFDM prototype filters, in which the GFDM system is free from inter subcarrier interference, is investigated, enabling frequency-domain decoupling during processing at the GFDM receiver. An efficient MIMO-GFDM detection method based on depth-first sphere decoding is subsequently proposed with this class of filters.

Ambisonics i.e., a full-sphere surround sound, is quintessential with 360° visual content to provide a realistic virtual reality (VR) experience. While 360° visual content capture gained a tremendous boost recently, the estimation of corresponding spatial sound is still challenging due to the required sound-field microphones or information about the sound-source locations. In this paper, we introduce a novel problem of generating Ambisonics in 360° videos using the audiovisual cue.

For spreading-based multiple access, whether orthogonal (OMA) or non-orthogonal (NOMA), the spreading sequences (signatures) are selected from a predefined codebook. When operating in a cellular system, intercell interference will be inherently present between close base stations that share the same resources. If the codebook is reused across the different cells, then intercell interference can cause a full collision of the interfering users in the code-domain, thus deteriorating their performance, especially those at the cell-edge.

The results of spoofing detection systems proposed during ASVspoof Challenges 2015 and 2017 confirmed the perspective in detection of unforseen spoofing trials in microphone channel. However, telephone channel presents much more challenging conditions for spoofing detection, due to limited bandwidth, various coding standards and channel effects. Research on the topic has thus far only made use of program codecs and other telephone channel emulations. Such emulations does not quite match the real telephone spoofing attacks.

DNNs play a major role in the state-of-the-art ASR systems. They can be used for extracting features and building probabilistic models for acoustic and language modelling. Despite their huge practical success, the level of theoretical understanding has remained shallow. This paper investigates DNNs from a statistical standpoint. In particular, the effect of activation functions on the distribution of the pre-activations and activations is investigated and discussed from both analytic and empirical viewpoints.

Many speech enhancement algorithms have been proposed over the years and it has been shown that deep neural networks can lead to significant improvements. These algorithms, however, have not been validated for hearing-impaired listeners. Additionally, these algorithms are often evaluated under a limited range of signal-to-noise ratios (SNR). Here, we construct a diverse speech dataset with a broad range of SNRs and noises.

In this paper, we address the fundamental problem of Sparse
Bayesian Learning (SBL), where the received signal is a high-order
tensor. We furthermore consider the problem of dictionary learning
(DL), where the tensor observations are assumed to be generated
from a Kronecker structured (KS) dictionary matrix multiplied by
the sparse coefficients. Exploiting the tensorial structure results in
a reduction in the number of degrees of freedom in the learning
problem, since the dimensions of each of the factor matrices are significantly