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Thailand Research Fund (TRF

Multimodal Data Fusion of Electromyography and Acoustic Signals for Thai Syllable Recognition

Speech disorders such as dysarthria are common and frequent after suffering a stroke. Speech rehabilitation performed by a speech-language pathologist is needed to improve and recover. However, in Thailand, there is a shortage of speech-language pathologists. In this paper, we present a syllable recognition system, which can be deployable in a speech rehabilitation system to provide support to the limited speech-language pathologists available. The proposed system is based on a multimodal fusion of acoustic signal and surface electromyography (sEMG) collected from facial muscles. Multimodal data fusion is studied to improve signal collection under noisy situations while reducing the number of electrodes needed. The signals are simultaneously collected while articulating 12 Thai syllables designed for rehabilitation exercises. Several features are extracted from sEMG signals and five channels are studied. The best combination of features and channels is chosen to be fused with the mel-frequency cepstral coefficients extracted from the acoustic signal. The feature vector from each signal source is projected by spectral regression extreme learning machine and concatenated. Data from seven healthy subjects were collected for evaluation purposes. Results show that the multimodal fusion outperforms the use of a single signal source achieving up to 98% of accuracy. In other words, an accuracy improvement up to 5% can be achieved when using the proposed multimodal fusion. Moreover, its low standard deviations in classification accuracy compared to those from the unimodal fusion indicate the improvement in the robustness of the syllable recognition

Design of a quadratic filter for contrast - assisted ultrasonic imaging based on 2D gaussian filters

We present a novel design of quadratic filters (QFs) in the frequency domain in order to improve the quality of contrastassisted ultrasound images for medical diagnosis. The QF is designed as a 2D linear-phase filter. In addition, the magnitude is based on the sum of two 2D Gaussian filters. The centers of the Gaussian filters are placed at the locations where the power strength of signals from ultrasound contrast agent over surrounding tissue is maximal. The design parameters consist of two centers and a standard deviation (SD) of the Gaussian filters. The coefficients of the QF are obtained using the inverse discreteFourier transform. The QFs from the proposed design method are evaluated using in vivo ultrasound data, i.e., the kidney of aguinea pig. We find that the appropriate SD and two center points of the QF for the in vivo data are at 0.34, (3.30, 3.30) and (-3.30,-3.30) MHz, respectively. Results show that the images produced from the output signals of the new design are superior to theoriginal B-mode both in terms of contrast and spatial resolution. The quadratic image provides clear visualization of thekidney shape and large vascular structures inside the kidney. The contrast-to-tissue ratio value of quadratic image is 24.8 dBcompared to -1.5 dB from the B-mode image. In addition, we can use this new design approach as an efficient tool to furtherimprove the QF in producing better contrast-assisted ultrasound images for medical diagnostic purposes

Chromosome image classification using a two-step probabilistic neural network

Chromosome image analysis is composed of image preparation, image analysis, and image diagnosis. General procedureof chromosome image analysis includes of image preprocessing in the first step, image segmentation, feature extraction, andimage classification in the last step. This paper presents the preliminary results that use probabilistic neural network toclassify chromosome image into 24 classes. Features of chromosome which were used in this paper are area, perimeter, band’sarea, singular value decomposition, and band profile. Chromosome images were grouped in two steps by probabilistic neuralnetwork. Six groups and twenty four groups are in the first and the second step, respectively. The result from the secondstep is twenty four chromosome classes. Density profile sampled at 10, 30, 50 and 80 were tested. The best classificationresult of female is 68.19% when density profile at 30 samples was used, and that of male is 61.30% when density profile at50 samples was used

Self-Augmented Noisy Image for Noise2Noise Image Denoising

Image denoising is a critical task in image processing aimed at removing noise artifacts. Typically, supervised deep learning often necessitates a large number of pairs of noisy and noise-free images for training. Noise2Noise techniques have demonstrated efficiency in noise removal without relying on a noise-free ground truth. This is achieved through a learning process that approaches input to target points, balancing results across all training inputs. While Noise2Noise can be adapted for single image denoising, it still faces challenges in single image and blind noise scenarios. To address this issue, our research introduces the concept of self-augmented noisy images for self-supervised Noise2Noise single image denoising. The proposed method leverages the behavior of the training process, which strives to balance the loss values appropriately for each training set. By utilizing the same noisy image for both input and validation to learn self-identification, it produces another set of noisy images that mimic the input noisy images. From the experimental results, measured using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics, it is evident that the proposed self-augmented strategy enables Noise2Noise to remove noise in single image scenarios. Additionally, it achieves performance comparable to other unsupervised denoising methods without requiring additional augmentation manipulations