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Papers by Thanapant Raicharoen

Singing voice recognition is a difficult topic in music information retrieval research area. The ... more Singing voice recognition is a difficult topic in music information retrieval research area. The first approaches borrowed successful techniques widely used in automatic speech recognition (ASR) as speech and singing share similar acoustical feature since they are produced by the same apparatus. Moving from monophonic to polyphonic audio signal the problem become more complex as the background instrumental accompaniment is regarded as a noise source that has to be attenuated. This paper proposes a singing voice recognition algorithm that is able to automatically recognize the word in a singing signal with background music by using the concept of spectrogram pattern matching. The main idea is to apply both the spectrogram and the image processing methods to solve the problem of singing voice recognition. Each signal that accompanies music is analyzed and generated to its spectrogram that is used to train data for the classifier. Several classification functions are compared, such as Fisher classifier, feed-forward can effectively recognize the word in music with the accuracy rate more than 84%.

Pattern Recognition Letters, Jul 1, 2005

This paper presents a new method based on divide-and-conquer approach to the selection and replac... more This paper presents a new method based on divide-and-conquer approach to the selection and replacement of a set of prototypes from the training set for the nearest neighbor rule. This method aims at reducing the computational time and the memory space as well as the sensitivity of the order and the noise of the training data. A reduced prototype set contains Pairwise Opposite Class-Nearest Neighbor (POC-NN) prototypes which are close to the decision boundary and used instead of the training patterns. POC-NN prototypes are obtained by recursively iterative separation and analysis of the training data into two regions until each region is correctly grouped and classified. The separability is determined by the POC-NN prototypes essential to define the locations of all separating hyperplanes. Our method is fast and order independent. The number of prototypes and the overfitting of the model can be reduced by the user. The experimental results signify the effectiveness of this technique and its performance in both accuracy and prototype rate as well as in training time to those obtained by classical nearest neighbor techniques.

Information Sciences, Sep 1, 2013

Lecture notes in electrical engineering, 2010

The problem of automatic detecting impulsive sounds such as human sound (screams, shout), gun sho... more The problem of automatic detecting impulsive sounds such as human sound (screams, shout), gun shots, machine gun, thunder, fire alarm, and car horn are useful for hearing impairment person. In this paper, instead of filtering the frequency of each sound for identifying types of sound, the frequency of sound is transformed into a recognizable image. The transformation is based on

Singing voice recognition is a difficult topic in Music information retrieval research area. The ... more Singing voice recognition is a difficult topic in Music information retrieval research area. The first approaches borrowed successful techniques widely used in Automatic speech Recognition (ASR) as speech and singing share similar acoustical feature since they are produced by the same apparatus. Moving from monophonic to polyphonic audio signal the problem become more complex as the background instrumental accompaniment is regarded as a noise source that has to be attenuated. This paper proposes a singing voice recognition algorithm that is able to automatically recognize the word in a singing signal with background music by using the concept of spectrogram pattern matching. The main idea is to apply both the spectrogram and the image processing methods to solve the problem of singing voice recognition. Each signal that accompanies music is analyzed and generated to its spectrogram that is used to train data for the classifier. Several classification functions are compared, such as ...

Icarcv 2004 8th Control Automation Robotics and Vision Conference 2004, 2004

Invariant image recognition is one of the hardest problems in computer vision. The aim is to iden... more Invariant image recognition is one of the hardest problems in computer vision. The aim is to identify an image independently of its rotational orientation and size, as well as changing its color intensity. The current techniques such as high-ordered neural network and Zernike moments are not practical to apply to color images of size at least 256 × 256 pixels.

This paper proposes a new method of feature extraction for face recognition based on descriptive ... more This paper proposes a new method of feature extraction for face recognition based on descriptive statistics of a face image. Our method works by first converting the face image with all the corresponding face components such as eyes, nose, and mouth to a grayscale images. The features are then extracted from the grayscale image, based on a descriptive statistics of

This paper presents a new method based on divide-and-conquer approach to the selection of a set o... more This paper presents a new method based on divide-and-conquer approach to the selection of a set of prototypes from the training data by the nearest neighbor rule. The method aims at reducing computational time and memory space as well as sensitivity of the order and noise of the training data. A reduced prototype set contains Pairwise Opposite Class-Nearest Neighbor (POC-NN) prototypes, which are close to the decision boundary and used instead of the training patterns. POC-NN prototypes are obtained by recursively analysis and iterative separation of the training data into two regions until each region is correctly grouped and classified. The separability is determined by the POC-NN prototypes essential to define the locations of all separating hyperplanes. Our method is fast and order independent. The number of prototypes and the overfitting of the model can be reduced by the user. This method can be used to solve not only classification but also regression problems. The experimental results signify the effectiveness of this technique and its performance in both accuracy and prototype rate as well as in training time over those obtained by classical nearest neighbor techniques.

Information Sciences, 2013

Circuits and Systems, …, 2003

... vectol; 2. with respect to xjB). xy) is a vectol; such that minaj(lyiA) - yjB'I). such t... more ... vectol; 2. with respect to xjB). xy) is a vectol; such that minaj(lyiA) - yjB'I). such that minv,i(ly,(A) - yj'I). The leastness between any two xiA' and xy' is defined in terms of the absolute different target value y between them. x!~' and ...