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    The Trapezoidal Khim: Sound Radiation of G Notes
    (2025-01-01)
    Techaaphonchai, Natcha
    ;
    Thida, Worakrit
    ;
    Meesawat, Kittipitch
    ;
    Ayuthaya, Phonlasit Thinnakorn Na
    ;
    Chitsakul, Pakaphol
    This research investigates the sound characteristics of the trapezoidal Khim, a traditional Thai hammered dulcimer, focusing on the G 196, G 392 Hz and G 784 Hz notes played with different hammers. Two types of wooden hammers were used, that is, covered with flannel and uncovered. Measurements were conducted in a room measuring 10 by 5 by 3 meters using two microphones, one for capturing the sound field directionality and the other for reference sound. The measurement microphone was placed 2 meters away from the instrument at various angles, while the reference microphone was positioned above the center of the instrument. Analysis of the recordings reveals the directivity profiles for each frequencies, showing that the Khim exhibits relatively high directivity at 90 and 270 degrees, corresponding to its open channels of the instrument. This suggests that the instrument's construction significantly influences its sound radiation. Hammer type was found to affect overall directivity, i.e., fabric-covered hammers resulted in lower directivity compared to uncovered hammers. Furthermore, the hitting point on the key significantly affects the Khim's directivity, indicating that playing technique plays a crucial role in shaping the instrument's sound projection. This study contributes to a deeper understanding of the unique sound production of the trapezoidal Khim and its role in traditional Thai music. It offers valuable information for musicians, instrument makers, and researchers interested in the acoustics of cultural instruments in realistic performance environments.
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    BAMS: Binary Sequence-Augmented Spectrogram with Self-Attention Deep Learning for Human Activity Recognition
    (2024-01-01)
    Sricom, Natchaya
    ;
    Charakorn, Rujikorn
    ;
    Manoonpong, Poramate
    ;
    Limpiti, Tulaya
    Human Activity Recognition (HAR) has rapidly gained interest over the years due to its wide range of applications in AI-based systems, particularly healthcare monitoring. HAR methods typically involve extracting relevant features from data provided by wearable sensors, smartphone sensors, cameras, or their combinations to classify different activities. Nevertheless, a major challenge lies in achieving high classification accuracy with limited data samples, particularly when distinguishing between activities with similar signal attributes. To address this challenge, we propose a novel HAR method called BinAry sequence-augmented spectrograM with Self-attention deep learning (BAMS). Our proposed method leverages only basic wearable sensor data. It utilizes short-time Fourier transform spectrograms to extract spatio-temporal sensor information. The spectrogram is integrated with a binary sequence that captures movement direction. We integrate a scaled dot-product self-attention mechanism into the model to prioritize data from wearable sensors, thereby enhancing the model's performance. The proposed method is evaluated on a public dataset using leave-one-subject-out cross-validation for efficacy and robustness. The method is found to achieve significant improvement over other state-of-the-art methods with the classification accuracy percentage and weighted F-1 scores of 88.06±5.11 and 87.36±5.96, respectively, for a twelve-activity classification.
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    A Low-Cost Digital Stethoscope for Normal and Abnormal Heart Sound Classification
    (2022-01-01)
    Khoruamkid, Sorawit
    ;
    Visitsattapongse, Sarinporn
    Heart disease is a major problem in most deaths. To conquer this situation, heartbeat sound analysis is a convenient method for diagnosing heart disease. Heartbeat sound classification remains a challenging problem in heart sound division and feature extraction. A stethoscope is a medical device widely used by physicians to listen to the heartbeat. An acoustic stethoscope operates on the chest piece to the ears of the listener. The main problem is in listening to heart sounds that the low signal level and are difficult to be analyzed. Adding electronic circuitry and software to acoustic stethoscopes will strengthen the heart rate signal and can minimize error analysis of the state of the patient's heart. Machine learning is used to efficiently analyze and classify heart sounds. Convolutional Neural Network (CNN) models and Support Vector Machine (SVM) with feature extractors were effective methods and were used in this research. First, the Phonocardiogram (PCG) files are fragmented into pieces of equivalent length. Then, we convert the PCG files to a spectrogram. The spectrogram images are fed into a convolutional neural network and support vector machine. The best result is using an Inception V3 model with the CNN classifier which has an accuracy of 0.909, with 0.948 sensitivity and 0.869 specificity.
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    Doppler radar for dynamic hand gesture recognition based on signal image processing
    (2019-07-01)
    Arthamanolap, Kongphum
    ;
    Gabbualoy, Somprasong
    ;
    Phasukkit, Pattarapong
    From previous researches, Doppler radar was used to detect signal for implement with many applications. Nevertheless, it is difficult to analyze for recognize object. At present, technique of deep learning in terms of signal processing and image processing are using in many researches to classify categories of data. In this paper, signal image was used by deep learning to classify hand gesture by receiving signals from 24GHz transceiver: BGT24MTR11. We transformed the signals to images for 3 categories including Spectrogram, Time domain from original signal and feature MFCC graph. After that those of converted image will be trained by Deep learning for classify the hand gesture types. From the result of this experiment has been shown that signal image can be used to recognize hand gesture and Spectrogram graph makes the highest accuracy as 94%.
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    Comparison of feature extraction for accent dependent Thai speech recognition system
    (2018-09-13)
    Tantisatirapong, Suchada
    ;
    Prasoproek, Chalisa
    ;
    Phothisonothai, Montri
    This paper aims to compare the feature extraction methods for accent dependent Thai speech from three regions including central, southern and northeastern regions. We investigate four frequency analysis methods: i.e., Energy Spectral Density (ESD), Power Spectral Density (PSD), Mel-Frequency Cepstral Coefficients (MFCC) and Spectrogram (SPT). Radial basis function kernel based on support vector machine is used as a classifier with 5-fold cross validation. The isolated speech data sets are recorded from 30 male and 30 female participants speaking the 10 Thai digits from 0 to 9. The MFCC-based feature gives better accuracy than ESD, PSD and SPT respectively. For within the same region, the MFCC-based feature provides average accuracy of 94.9% and 99.1% for male and female voices respectively. For the three regions, the MFCC-based feature provides average accuracy of 89.34% and 93.81% for male and female voices, respectively.