Vocal drum sounds in human beatboxing: An acoustic and articulatory exploration using electromagnetic articulography.

Acoustic characteristics, lingual and labial articulatory dynamics, and ventilatory behaviors were studied on a beatboxer producing twelve drum sounds belonging to five main categories of his repertoire (kick, snare, hi-hat, rimshot, cymbal). Various types of experimental data were collected synchronously (respiratory inductance plethysmography, electroglottography, electromagnetic articulography, and acoustic recording). Automatic unsupervised classification was successfully applied on acoustic data with t-SNE spectral clustering technique. A cluster purity value of 94% was achieved, showing that each sound has a specific acoustic signature. Acoustical intensity of sounds produced with the humming technique was found to be significantly lower than their non-humming counterparts. For these sounds, a dissociation between articulation and breathing was observed. Overall, a wide range of articulatory gestures was observed, some of which were non-linguistic. The tongue was systematically involved in the articulation of the explored beatboxing sounds, either as the main articulator or as accompanying the lip dynamics. Two pulmonic and three non-pulmonic airstream mechanisms were identified. Ejectives were found in the production of all the sounds with bilabial occlusion or alveolar occlusion with egressive airstream. A phonetic annotation using the IPA alphabet was performed, highlighting the complexity of such sound production and the limits of speech-based annotation.

A Convolutional Neural Network for 250-MHz Quantitative Acoustic-microscopy Resolution Enhancement.

Quantitative acoustic microscopy (QAM) permits the formation of quantitative two-dimensional (2D) maps of acoustic and mechanical properties of soft tissues at microscopic resolution. The 2D maps formed using our custom SAM systems employing a 250-MHz and a 500-MHz single-element transducer have a nominal resolution of 7 µm and 4µm, respectively. In a previous study, the potential of single-image super-resolution (SR) image post-processing to enhance the spatial resolution of 2D SAM maps was demonstrated using a forward model accounting for blur, decimation, and noise. However, results obtained when the SR method was applied to soft tissue data were not entirely satisfactory because of the limitation of the convolution model considered and by the difficulty of estimating the system point spread function and designing the appropriate regularization term. Therefore, in this study, a machine learning approach based on convolutional neural networks was implemented. For training, data acquired on the same samples at 250 and 500 MHz were used. The resulting trained network was tested on 2D impedance maps (2DZMs) of human lymph nodes acquired from breast-cancer patients. Visual inspection of the reconstructed enhanced 2DZMs were found similar to the 2DZMs obtained at 500 MHz which were used as ground truth. In addition, the enhanced 250-MHz 2DZMs obtained from the proposed method yielded better peak signal to noise ratio and normalized mean square error than those obtained with the previous SR method. This improvement was also demonstrated by the statistical analyses. This pioneering work could significantly reduce challenges and costs associated with current very high-frequency SAM systems while providing enhanced spatial resolution.