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Dyad-to-pure transitions caused substantial resetting, but pure-to-dyad transitions sometimes elicited even greater segregation than for the corresponding interval in dyad-only sequences (overshoot). The results indicate that abrupt changes in timbre can strongly affect the likelihood of stream segregation without introducing significant peripheral-channeling cues. These asymmetric effects of transition direction are reminiscent of subtractive adaptation in vision.Additive manufacturing has expanded greatly in recent years with the promise of being able to create complex and custom structures at will. Enhanced control over the microstructure properties, such as percent porosity, is valuable to the acoustic design of materials. In this work, aluminum foams are fabricated using a modified powder bed fusion method, which enables voxel-by-voxel printing of structures ranging from fully dense to approximately 50% porosity. To understand the acoustic response, samples are measured in an acoustic impedance tube and characterized with the Johnson-Champoux-Allard-Lafarge model for rigid-frame foams. Bayesian statistical inversion of the model parameters is performed to assess the applicability of commonly employed measurement and modeling methods for traditional foams to the additively manufactured, low porosity aluminum foams. This preliminary characterization provides insights into how emerging voxel-by-voxel additive manufacturing approaches could be used to fabricate acoustic metal foams and what could be learned about the microstructure using traditional measurement and analysis techniques.A high resolution direction-of-arrival (DOA) approach is presented based on deep neural networks (DNNs) for multiple speech sources localization using a small scale array. First, three invariant features from the time-frequency spectrum of the input signal include generalized cross correlation (GCC) coefficients, GCC coefficients in the mel-scaled subband, and the combination of GCC coefficients and logarithmic mel spectrogram. Then the DNN labels are designed to fit the Gaussian distribution, which is similar to the spatial spectrum of the multiple signal classification. Finally, DOAs are predicted by performing peak detection on the DNN outputs, where the maximum values correspond to speech signals of interest. The DNN-based DOA estimation method outperforms the existing high resolution beamforming techniques in numerical simulations. The proposed framework implemented with a four-element microphone array can effectively localize multiple speech sources in an indoor environment.COVID-19 is a global health crisis that has been affecting our daily lives throughout the past year. The symptomatology of COVID-19 is heterogeneous with a severity continuum. Many symptoms are related to pathological changes in the vocal system, leading to the assumption that COVID-19 may also affect voice production. For the first time, the present study investigates voice acoustic correlates of a COVID-19 infection based on a comprehensive acoustic parameter set. We compare 88 acoustic features extracted from recordings of the vowels /i/, /e/, /u/, /o/, and /a/ produced by 11 symptomatic COVID-19 positive and 11 COVID-19 negative German-speaking participants. We employ the Mann-Whitney U test and calculate effect sizes to identify features with prominent group differences. The mean voiced segment length and the number of voiced segments per second yield the most important differences across all vowels indicating discontinuities in the pulmonic airstream during phonation in COVID-19 positive participants. Group differences in front vowels are additionally reflected in fundamental frequency variation and the harmonics-to-noise ratio, group differences in back vowels in statistics of the Mel-frequency cepstral coefficients and the spectral slope. Our findings represent an important proof-of-concept contribution for a potential voice-based identification of individuals infected with COVID-19.Time reversal (TR) focusing of acoustical waves is a widely studied phenomenon that usually requires a chaotic cavity or disordered scattering medium to achieve spatial and frequency decorrelation of the acoustic field when using a single channel. click here On the other hand, sonic crystals were disregarded as scattering media for the TR process because of their periodic structure and previous results showing poor spatial focusing when compared to a disordered medium. In this paper, an experimental realization of a tunable sonic crystal, which can achieve single-channel TR focusing amplitudes in the audible range comparable to those obtained in a disordered scattering medium, is presented. Furthermore, the tunable nature of the system allows it to switch the time-reversed pulse on and off by changing its geometrical configuration. A robustness analysis with respect to the perturbations in the sonic crystal configurations is also presented, showing that the time-reversed pulses with high temporal and spatial contrasts are preserved only for configurations that are close to the original one.When performing measurements with wall-installed microphone array, the turbulent boundary layer that develops over the measuring system can induce pressure fluctuations that are much greater than those of acoustic sources. It then becomes necessary to process the data to extract each component of the measured field. For this purpose, it is proposed in this paper to decompose the measured spectral matrix into the sum of matrices associated with the acoustic and aerodynamic contributions. This decomposition exploits the statistical properties of each pressure field. On the one hand, assuming that the acoustic contribution is highly correlated over the sensors, the rank of the corresponding cross-spectral matrix is limited to a finite number. On the other hand, the correlation structure of the aerodynamic noise matrix is constrained to resemble a Corcos-like model, with physical parameters estimated within the separation procedure. This separation problem is solved by a Bayesian inference approach, which takes into account the uncertainties on each component of the model. The performance of the method is first evaluated on wind tunnel measurements and then on a particularly noisy industrial measurement setup microphones flush-mounted on the fuselage of a large aircraft.