Single-Channel Speech Enhancement with Deep Complex U-Networks and Probabilistic Latent Space Models

In this paper, we propose to extend the deep, complex U-Network architecture for speech enhancement by incorporating a probabilistic (i.e., variational) latent space model. The proposed model is evaluated against several ablated versions of itself in order to study the effects of the variational latent space model, complex-value processing, and self-attention. Evaluation on the MS-DNS 2020 and Voicebank+Demand datasets yields consistently high performance. E.g., the proposed model achieves an SI-SDR of up to 20.2 dB, about 0.5 to 1.4 dB higher than its ablated version without probabilistic latent space, 2-2.4 dB higher than WaveUNet, and 6.7 dB above PHASEN. Compared to real-valued magnitude spectrogram processing with a variational U-Net, the complex U-Net achieves an improvement of up to 4.5 dB SI-SDR. Complex spectrum encoding as magnitude and phase yields best performance in anechoic conditions whereas real and imaginary part representation results in better generalization to (novel) reverberation conditions, possibly due to the underlying physics of sound

Complex Independent Component Analysis of Frequency-Domain Electroencephalographic Data

Independent component analysis (ICA) has proven useful for modeling brain and electroencephalographic (EEG) data. Here, we present a new, generalized method to better capture the dynamics of brain signals than previous ICA algorithms. We regard EEG sources as eliciting spatio-temporal activity patterns, corresponding to, e.g., trajectories of activation propagating across cortex. This leads to a model of convolutive signal superposition, in contrast with the commonly used instantaneous mixing model. In the frequency-domain, convolutive mixing is equivalent to multiplicative mixing of complex signal sources within distinct spectral bands. We decompose the recorded spectral-domain signals into independent components by a complex infomax ICA algorithm. First results from a visual attention EEG experiment exhibit (1) sources of spatio-temporal dynamics in the data, (2) links to subject behavior, (3) sources with a limited spectral extent, and (4) a higher degree of independence compared to sources derived by standard ICA.Comment: 21 pages, 11 figures. Added final journal reference, fixed minor typo

Dynamic behaviour of brain and surrogate materials under ballistic impact

In the last several decades the number of the fatalities related to criminally inflicted cranial gunshot wounds has increased (Aarabi et al.; Jena et al., 2014; Mota et al., 2003). Back-spattered bloodstain patterns are often important in investigations of cranial gunshot fatalities, particularly when there is a doubt whether the death is suicide or homicide. Back-spatter is the projection of blood and tissue back toward the firearm. However, the mechanism of creation of the backspatter is not understood well. There are several hypotheses, which describe the formation of the backspatter. However, as it is difficult to study the internal mechanics of formation of the backspatter in animal experiments as the head is opaque and sample properties vary from animal to animal. Performing ballistic experiments on human cadavers is rarely not possible for ethical reasons. An alternative is to build a realistic physical 3D model of the human head, which can be used for reconstruction of crime scenes and BPA training purposes. This requires a simulant material for each layer of the human head. In order to build a realistic model of human head, it is necessary to understand the effect of the each layer of the human head to the generation of the back-spatter. Simulant materials offer the possibility of safe, well‐controlled experiments. Suitable simulants must be biologically inert, be stable over some reasonable shelf‐life, and respond to ballistic penetration in the same way as the responding human tissues. Traditionally 10-20% (w/w) gelatine have been used as a simulant for human soft tissues in ballistic experiments. However, 10-20% of gelatine has never been validated as a brain simulant. Moreover, due to the viscoelastic nature of the brain it is not possible to find the exact mechanical properties of the brain at ballistic strain rates. Therefore, in this study several experiments were designed to obtain qualitative and quantitative data using high speed cameras to compare different concentrations of gelatine and new composite material with the bovine and ovine brains. Factors such as the form of the fragmentation, velocity of the ejected material, expansion rate, stopping distance, absorption of kinetic energy and effect of the suction as well as ejection of the air from the wound cavity and its involvement in the generation of the backspatter have been investigated. Furthermore, in this study a new composite material has been developed, which is able to create more realistic form of the fragmentation and expansion rate compared to the all different percentage of the gelatine. The results of this study suggested that none of the concentrations the gelatine used in this study were capable of recreating the form of the damage to the one observed from bovine and ovine brain. The elastic response of the brain tissue is much lower that observed in gelatine samples. None of the simulants reproduced the stopping distance or form of the damage seen in bovine brain. Suction and ejection of the air as a result of creation of the temporary cavity has a direct relation to the elasticity of the material. For example, by reducing the percentage of the gelatine the velocity of the air drawn into the cavity increases however, the reverse scenario can be seen for the ejection of the air. This study showed that elastic response of the brain tissue was not enough to eject the brain and biological materials out of the cranium. However, the intracranial pressure raises as the projectile passes through the head. This pressure has the potential of ejecting the brain and biological material backward and create back-spatter. Finally, the results of this study suggested that for each specific type of experiment, a unique simulant must be designed to meet the requirements for that particular experiment

Correlated modulation: a criterion for blind source separation,” Joint meeting of Acoustical Society of America and European Acoustics Association

Summary: The problem of blindly separating a convolutive mixture of modulated signals is considered. Spectrograms of the signals are computed and separation is performed in the frequency domain. A new algorithm for blind source separation is proposed, which is based on correlated modulation in the sources ’ different frequency channels. For example, speech contains correlated modulation in different frequency regions. The algorithm successfully separates mixtures of modulated artificial signals and of speech