• Journal of Biomedical Engineering Research
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• v.15 no.2
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• pp.159-166
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• 1994

A new pattern classification algorithm using cepstrum to analyze lung sounds for the classification of pattern with pulmonary and bronchial disorders is proposed. To evaluate the perfomance of the proposed method, the results are compared to the pattern classification with the AR modeling method. In the experiment lung sounds recorded for the training of physician used. As a results, the accuracy of the cepstrum classification is 92.3 % and AR modeling is the 53.8 %, therefore cepstrum modeling method has very high performance than AR and it turned out to be a very efficient algorithm.

• MALSORI
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• no.49
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• pp.135-144
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• 2004

In this paper, we make a study of classification normal from abnormal - normal, asthma through analysis of thoracic sound to take use thoracic sound detection system. Thoracic sound detection system has a function to store thoracic sound and analyze the data. The wave shape of thoracic sound is similar to noise and is systematically generated by inhalation and exhalation breathing, therefore, in this paper, to classify asthma sound in thoracic sound, we could discriminate between normal and abnormal case using level crossing rate(LCR) and spectrogram energy rate.

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.32-43
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• 2009

In this paper, we improve the performance of cardiac disorder classification by continuous heart sound signals using automatic segmentation and extreme learning machine (ELM). The accuracy of the conventional cardiac disorder classification systems degrades because murmurs and click sounds contained in the abnormal heart sound signals cause incorrect or missing starting points of the first (S1) and the second heart pulses (S2) in the automatic segmentation stage, In order to reduce the performance degradation due to segmentation errors, we find the positions of the S1 and S2 pulses, modify them using the time difference of S1 or S2, and extract a single period of heart sound signals. We then obtain a feature vector consisting of the mel-scaled filter bank energy coefficients and the envelope of uniform-sized sub-segments from the single-period heart sound signals. To classify the heart disorders, we use ELM with a single hidden layer. In cardiac disorder classification experiments with 9 cardiac disorder categories, the proposed method shows the classification accuracy of 81.6% and achieves the highest classification accuracy among ELM, multi-layer perceptron (MLP), support vector machine (SVM), and hidden Markov model (HMM).

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2905-2907
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• 1999

The meaningful speech sound block classification provides very important information in the speech recognition. The following technique of the classification is based on the DWT (discrete wavelet transform), which will provide a more fast algorithm and a useful, compact solution for the pre-processing of speech recognition. The algorithm is implemented to the unvoiced/voiced classification and the denoising.

• Proceedings of the IEEK Conference
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• summer
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• pp.237-243
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• 2004

A multichannel smart sound sensor capable to detect and identify sound events in noisy conditions is presented in this paper. Sound information extraction is a complex task and the main difficulty consists is the extraction of high­level information from an one-dimensional signal. The input of smart sound sensor is composed of data collected by 5 microphones and its output data is sent through a network. For a real time working purpose, the sound analysis is divided in three steps: sound event detection for each sound channel, fusion between simultaneously events and sound identification. The event detection module find impulsive signals in the noise and extracts them from the signal flow. Our smart sensor must be capable to identify impulsive signals but also speech presence too, in a noisy environment. The classification module is launched in a parallel task on the channel chosen by data fusion process. It looks to identify the event sound between seven predefined sound classes and uses a Gaussian Mixture Model (GMM) method. Mel Frequency Cepstral Coefficients are used in combination with new ones like zero crossing rate, centroid and roll-off point. This smart sound sensor is a part of a medical telemonitoring project with the aim of detecting serious accidents.

• Speech Sciences
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• v.4 no.2
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• pp.41-54
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• 1998

Speech signals are, depending on the characteristics of waveform, classified as voiced sound, unvoiced sound, and silence. Voiced sound, produced by an air flow generated by the vibration of the vocal cords, is quasi-periodic, while unvoiced sound, produced by a turbulent air flow passed through some constriction in the vocal tract, is noise-like. Silence represents the ambient noise signal during the absence of speech. The need for deciding whether a given segment of a speech waveform should be classified as voiced, unvoiced, or silence has arisen in many speech analysis systems. In this paper, a voiced/unvoiced/silence classification algorithm using spectral change in the wavelet transformed signal is proposed and then, experimental results are demonstrated with our discussions.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.2
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• pp.81-86
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• 2014

In this paper, we present an effective sound classification system for recognizing the real-time context of a smartphone user. Our system avoids unnecessary consumption of limited computational resource by filtering both silence and white noise out of input sound data in the pre-processing step. It also improves the classification performance on low energy-level sounds by amplifying them as pre-processing. Moreover, for efficient learning and application of HMM classification models, our system executes the dimension reduction and discretization on the feature vectors through k-means clustering. We collected a large amount of 8 different type sound data from daily life in a university research building and then conducted experiments using them. Through these experiments, our system showed high classification performance.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.885-896
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• 2022

Motor failure in manufacturing plays an important role in future A/S and reliability. Motor failure is detected by measuring sound, current, and vibration. For the data used in this paper, the sound of the car's side mirror motor gear box was used. Motor sound consists of three classes. Sound data is input to the network model through a conversion process through MelSpectrogram. In this paper, various methods were applied, such as data augmentation to improve the performance of classifying fault motors and various methods according to class imbalance were applied resampling, reweighting adjustment, change of loss function and representation learning and classification into two stages. In addition, the curriculum learning method and self-space learning method were compared through a total of five network models such as Bidirectional LSTM Attention, Convolutional Recurrent Neural Network, Multi-Head Attention, Bidirectional Temporal Convolution Network, and Convolution Neural Network, and the optimal configuration was found for motor sound classification.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.568-573
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• 2019

In the diagnosis of pulmonary diseases, auscultation technique is simpler than the other methods, and lung sounds can be used for predicting the types of pulmonary diseases as well as identifying patients with pulmonary diseases. Therefore, in this paper, we identify patients with pulmonary diseases and classify lung sounds according to their sound characteristics using various convolutional neural networks, and compare the classification performance of each neural network method. First, lung sounds over affected areas of the chest with pulmonary diseases are collected by using a single-channel lung sound recording device, and spectral features are extracted from the collected sounds in time domain and applied to each neural network. As classification methods, we use general, parallel, and residual convolutional neural network, and compare lung sound classification performance of each neural network through experiments.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.845-853
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• 2020

In this paper, we propose a method for classifying environmental sound for selective noise cancellation in industrial sites. Noise in industrial sites causes hearing loss in workers, and researches on noise cancellation have been widely conducted. However, the conventional methods have a problem of blocking all sounds and cannot provide the optimal operation per noise type because of common cancellation method for all types of noise. In order to perform selective noise cancellation, therefore, we propose a method for environmental sound classification based on deep learning. The proposed method uses new sets of acoustic features consisting of temporal and statistical properties of Mel-spectrogram, which can overcome the limitation of Mel-spectrogram features, and uses convolutional neural network as a classifier. We apply the proposed method to five-class sound classification with three noise classes and two non-noise classes. We confirm that the proposed method provides improved classification accuracy by 6.6% point, compared with that using conventional Mel-spectrogram features.