Decomposed Temporal Dynamic CNN: Efficient Time-Adaptive Network for Text-Independent Speaker Verification Explained with Speaker Activation Map

Temporal dynamic models for text-independent speaker verification extract consistent speaker information regardless of phonemes by using temporal dynamic CNN (TDY-CNN) in which kernels adapt to each time bin. However, TDY-CNN shows limitations that the model is too large and does not guarantee the diversity of adaptive kernels. To address these limitations, we propose decomposed temporal dynamic CNN (DTDY-CNN) that makes adaptive kernel by combining static kernel and dynamic residual based on matrix decomposition. The baseline model using DTDY-CNN maintained speaker verification performance while reducing the number of model parameters by 35% compared to the model using TDY-CNN. In addition, detailed behaviors of temporal dynamic models on extraction of speaker information was explained using speaker activation maps (SAM) modified from gradient-weighted class activation mapping (Grad-CAM). In DTDY-CNN, the static kernel activates voiced features of utterances, and the dynamic residual activates unvoiced high-frequency features of phonemes. DTDY-CNN effectively extracts speaker information from not only formant frequencies and harmonics but also detailed unvoiced phonemes' information, thus explaining its outstanding performance on text-independent speaker verification.Comment: Submitted to InterSpeech 202

Joint Channel Estimation with Phase Noise Suppression and Soft Decision Decoding Scheme for OFDM-based WLANs

In orthogonal frequency-division multiplexing (OFDM)-based wireless local area networks (WLANs), phase noise (PHN) and channel estimation errors can degrade the performance of the system. This letter provides a soft decision decoding scheme analysis for OFDM-based WLANs in the presence of PHN and channel estimation errors. Basing on this analysis, we propose a novel iterative scheme for joint channel estimation with PHN suppression and soft decision decoding. In addition, the soft decision decoding metric for QAM OFDM systems is modified to mitigate the effects of PHN and channel estimation errors. The simulation results show that the proposed scheme mitigates the performance degradation due to PHN and channel estimation errors effectively

Design and Implementation of Analyzing Instrument for Broadband Powerline Communications

This paper deals with the design and implementation of the analyzing instrument for the broadband powerline communication. This instrument has integrated functions for channel estimation, noise power spectrum measurement, and impedance measurement. It consists of a digital board with high speed DSP and FPGA chips, an analog board as a front-end adaptation to the powerline medium, and a Windows GUI application for presenting measurement results. This paper gives measurement algorithms used in this system and a description of the hardware and software prototype

Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event Detection

2D convolution is widely used in sound event detection (SED) to recognize 2D patterns of sound events in time-frequency domain. However, 2D convolution enforces translation-invariance on sound events along both time and frequency axis while sound events exhibit frequency-dependent patterns. In order to improve physical inconsistency in 2D convolution on SED, we propose frequency dynamic convolution which applies kernel that adapts to frequency components of input. Frequency dynamic convolution outperforms the baseline model by 6.3% in DESED dataset in terms of polyphonic sound detection score (PSDS). It also significantly outperforms dynamic convolution and temporal dynamic convolution on SED. In addition, by comparing class-wise F1 scores of baseline model and frequency dynamic convolution, we showed that frequency dynamic convolution is especially more effective for detection of non-stationary sound events. From this result, we verified that frequency dynamic convolution is superior in recognizing frequency-dependent patterns as non-stationary sound events show more intricate time-frequency patterns.Comment: Submitted to INTERSPEECH 202

Epitheliotropic cutaneous lymphoma (mycosis fungoides) in a dog

A seven-year-old castrated male Yorkshire terrier dog was presented for a recurrent skin disease. Erythematous skin during the first visit progressed from multiple plaques to patch lesions and exudative erosion in the oral mucosa membrane. Biopsy samples were taken from erythematous skin and were diagnosed with epitheliotropic T cell cutaneous lymphoma by histopathology and immunochemical stain. In serum chemistry, the dog had a hypercalcemia (15.7 mg/dl) and mild increased alkaline phosphatase (417 U/l). Immunohistochemistry was performed to detect parathyroid hormone-related peptide (PTH-rP) in epitheliotropic cutaneous lymphoma tissues but the neoplastic cells were not labeled with anti-PTH-rP antibodies. The patient was treated with prednisolone and isotretinoin. However, the dog died unexpectedly

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash???Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application

Učinci gama-zračenja na folikule jajnika

In order to observe the morphological and endocrinological changes of the rat and mouse ovarian follicles by gamma-radiation, rats were whole-body irradiated with doses of 3.2 Gy and 8.0 Gy and mice with 2.9 Gy and 7.2 Gy. Sections of ovaria were examined by light microscopy. Concentrations of progesterone, testosterone, and estradiol in ovarian homogenate were determined by radioimmunoassay techniques. Gamma-radiation resulted in the increased percentage of atretic follicles in the groups killed on day 0, day 4, and day 8 after irradiation. The decrease in granulosa cell viability was found in animals killed on day 4 after irradiation. The finding of the high ratio of testosterone to estradiol compared to that of progesterone to testosterone suggests that aromatase activity — steroid biosynthesis from testosterone to estradiol — in granulosa cell could be affected by gamma-radiation.U radu su procjenjivane strukture i endokrinološke promjene u folikulima jajnika štakorica i mišica izazvane gama-zračenjem. Štakorice su bile izložene zračenju od 3,2 Gy ili 8,0 Gy, a mišice od 2,9 Gy ili 7,2 Gy. Životinje su usmrćene dana 0, dana 4, odnosno dana 8 nakon ozračenja. Rezovi debljine 7 µm pripremljeni su za mikroskopiranje. Koncentracije progesterona, testosterona i estradiola u homogenatu jajnika određene su specifičnim radioimunoesejem. Gama-zračenje uzrokovalo je povećanje broja atretičnih folikula u obje skupine životinja usmrćenih 4 odnosno 8 dana nakon ozračivanja. Gama-zračenje također je smanjilo životni vijek granuloza stanica u skupinama usmrćenim 4. dan nakon ozračivanja. Utvrđeno povećanje omjera testosterona prema estradiolu u usporedbi s omjerom progesterona prema testosteronu upućuje na to da gama-zračenje utječe na aktivnost aromataze u steroidnoj biosintezi testosterona u estradiol u granuloza stanicama