Modelling black-box audio effects with time-varying feature modulation

Deep learning approaches for black-box modelling of audio effects have shown promise, however, the majority of existing work focuses on nonlinear effects with behaviour on relatively short time-scales, such as guitar amplifiers and distortion. While recurrent and convolutional architectures can theoretically be extended to capture behaviour at longer time scales, we show that simply scaling the width, depth, or dilation factor of existing architectures does not result in satisfactory performance when modelling audio effects such as fuzz and dynamic range compression. To address this, we propose the integration of time-varying feature-wise linear modulation into existing temporal convolutional backbones, an approach that enables learnable adaptation of the intermediate activations. We demonstrate that our approach more accurately captures long-range dependencies for a range of fuzz and compressor implementations across both time and frequency domain metrics. We provide sound examples, source code, and pretrained models to faciliate reproducibility

ATGNN: Audio Tagging Graph Neural Network

Deep learning models such as CNNs and Transformers have achieved impressive performance for end-to-end audio tagging. Recent works have shown that despite stacking multiple layers, the receptive field of CNNs remains severely limited. Transformers on the other hand are able to map global context through self-attention, but treat the spectrogram as a sequence of patches which is not flexible enough to capture irregular audio objects. In this work, we treat the spectrogram in a more flexible way by considering it as graph structure and process it with a novel graph neural architecture called ATGNN. ATGNN not only combines the capability of CNNs with the global information sharing ability of Graph Neural Networks, but also maps semantic relationships between learnable class embeddings and corresponding spectrogram regions. We evaluate ATGNN on two audio tagging tasks, where it achieves 0.585 mAP on the FSD50K dataset and 0.335 mAP on the AudioSet-balanced dataset, achieving comparable results to Transformer based models with significantly lower number of learnable parameters

Esophageal Carcinoma Histology Affects Perioperative Morbidity Following Open Esophagogastrectomy

Background. Esophagectomy for esophageal cancer is being practiced routinely with favorable results at many centers. We sought to determine if tumor histology is a powerful surrogate marker for perioperative morbidity. Methods. Seventy three consecutive patients managed operatively were reviewed from our prospectively maintained database. Results. Adenocarcinoma (AC) was present in 52 (71%) and squamous cell (SCC) in 21 (29%). The use of neoadjuvant therapy was similar for the AC (34.62%) and SCC (42.86%) groups. The SCC group had a higher incidence of prior pulmonary disease than the AC group (23.8% versus 5.8%, resp.; P = .03). SCC patients were more likely to have a prolonged ICU stay than AC patients (P = .004) despite similar complication rates, EBL, and prognostic nutritional index. The SCC group did, however, experience higher grades of complications (P = .0053). Conclusions. Presence of SCC was the single best predictor of prolonged ICU stay and more severe complications as defined by this study. Only a past history of pulmonary disease was different between the two histologic subgroups

Soldadura, inspección y verificación, en laboratorio, de un prototipo con chip BGA

Este trabajo presenta el procedimiento utilizado para la soldadura e inspección de un chip con encapsulado BGA sin plomo. Se mencionan los criterios considerados para definir el perfil de temperatura y el procedimiento para lograrlo utilizando un equipo de soldadura por infrarrojos. Luego se mencionan las inspecciones realizadas con microscopio, con rayos X, y las pruebas utilizando boundary scan.Sección: Análisis para el Diseño de HardwareCentro de Técnicas Analógico-Digitale

Synthesis, Crystal Structure and Interaction With DNA of N,N′-(Butane-1,4-Diyl)Bis(Guanidinium) Tetrachloroplatinate (II)

The design, synthesis, crystal structure and interaction with DNA of the N,N′-(butane-1,4-diyl)bis(guanidinium) tetrachloroplatinate(ll) are described. Crystal data: a = 8.152(1), b = 8.889(4), c = 10.700(3) Å , α = 81.59(3), β = 87.99(5), γ = 78.48(6)°, V = 752(1) Å3, Z = 2 , space group P-1. The structure was refined to R = 0.039 and Rw = 0.046 from 1853 reflections (I > 3σ(I)). This compound, named PtC4Gua, does not exhibit a center of symmetry and the center linker chain C(2) - C(3) - C(4) - C(5) is in gauche conformation. The cation is bisprotonated with the H+ attached to the imine group of each terminal guanidinium function. The presence of the platinum moiety reinforces the binding of the butane(bis)guanidinium structure with double stranded DNA as judged from thermal denaturation studies and DNA unwinding experiments