• The Journal of the Acoustical Society of Korea
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• v.30 no.3
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• pp.142-148
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• 2011

The final aim of the present study is to develop the intelligent robot, emulating human synesthetic skills which make it possible to associate a color image with a specific sound. This can be done on the basis of the mutual conversion between color image and sound. As a first step of the final goal, this study focused on a basic system using a conversion of color image into sound. This study describes a proposed method to convert color image into sound, based on the likelihood in the physical frequency information between light and sound. The method of converting color image into sound was implemented by using HSI histograms through RGB-to-HSI color model conversion, which was done by Microsoft Visual C++ (ver. 6.0). Two different color images were used on the simulation experiments, and the results revealed that the hue, saturation and intensity elements of each input color image were converted into fundamental frequency, harmonic and octave elements of a sound, respectively. Through the proposed system, the converted sound elements were then synthesized to automatically generate a sound source with wav file format, using Csound.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.101-107
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• 2012

This study describes a proposed method of converting an input sound signal into a color image by emulating human synesthetic skills which make it possible to associate an sound source with a specific color image. As a first step of sound-to-image conversion, features such as fundamental frequency(F0) and energy are extracted from an input sound source. Then, a musical scale and an octave can be calculated from F0 signals, so that scale, energy and octave can be converted into three elements of HSI model such hue, saturation and intensity, respectively. Finally, a color image with the BMP file format is created as an output of the process of the HSI-to-RGB conversion. We built a basic system on the basis of the proposed method using a standard C-programming. The simulation results revealed that output color images with the BMP file format created from input sound sources have diverse hues corresponding to the change of the F0 signals, where the hue elements have different intensities depending on octaves with the minimum frequency of 20Hz. Furthermore, output images also have various levels of chroma(or saturation) which is directly converted from the energy.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.251-256
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• 2010

This paper aims for developing the intelligent robot emulating human synesthetic skills which associate a color image with sound, so that we are able to build an application system based on the principle of mutual conversion between color image and sound. As the first step, in this study, we have tried to realize a basic system using the color image to sound conversion. This study describes a new conversion method to convert color image into sound, based on the likelihood in the physical frequency information between light and sound. In addition, we present the method of converting color image into sound using color model conversion as well as histograms in the converted color model. In the basis of the method proposed in this study, we built a basic system using Microsoft Visual C++(ver. 6.0). The simulation results revealed that the hue, saturation and intensity elements of a input color image were converted into F0, harmonic and octave elements of a sound, respectively. The converted sound elements were synthesized to generate a sound source with WAV file format using Csound toolkit.

• Science of Emotion and Sensibility
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• v.15 no.2
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• pp.231-238
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• 2012

This study aims for building an application system of converting sound into color image based on synesthetic perception. As the major features of input sound, both scale and octave elements extracted from F0(fundamental frequency) were converted into both hue and intensity elements of HSI color model, respectively. In this paper, we used the fixed saturation value as 0.5. On the basis of color model conversion theory, the HSI color model was then converted into the RGB model, so that a color image of the BMP format was finally created. In experiments, the basic system was implemented on both software and hardware(TMS320C6713 DSP) platforms based on the proposed sound-color image conversion method. The results revealed that diverse color images with different hues and intensities were created depending on scales and octaves extracted from the F0 of input sound signals. The outputs on the hardware platform were also identical to those on the software platform.

• Science of Emotion and Sensibility
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• v.16 no.2
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• pp.187-194
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• 2013

The final aim of the present study is to build a system of converting a color image into musical elements based on a synesthetic perception, emulating human synesthetic skills, which make it possible to associate a color image with a specific sound. This can be done on the basis of the similarities between physical frequency information of both light and sound. As a first step, an input true color image is converted into hue, saturation, and intensity domains based on a color model conversion theory. In the next step, musical elements including note, octave, loudness, and duration are extracted from each domain of the HSI color model. A fundamental frequency (F0) is then extracted from both hue and intensity histograms. The loudness and duration are extracted from both intensity and saturation histograms, respectively. In experiments, the proposed system on the conversion of a color image into musical elements was implemented using standard C and Microsoft Visual C++(ver. 6.0). Through the proposed system, the extracted musical elements were synthesized to finally generate a sound source in a WAV file format. The simulation results revealed that the musical elements, which were extracted from an input RGB color image, reflected in its output sound signals.

• Journal of Korea Multimedia Society
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• v.23 no.6
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• pp.765-771
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• 2020

To enable AI(artificial intelligence) to realize visual emotions, it attempts to create music centered on color, an element that causes emotions in paintings. Traditional image-based music production studies have a limitation in playing notes that are unrelated to the picture because of the absence of musical elements. In this paper, we propose a new algorithm to set the group of music through the average color of the picture, and to produce music after adding diatonic code progression and deleting sound using median value. And the results obtained through the proposed algorithm were analyzed.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.108-114
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• 2017

Lip reading is a field of image processing to assist the process of sound recognition. In some environment, the capture of sound signal usually has significant noise and therefore, the recognition rate of sound signal decreases. Lip reading can be a good feature for the increase of recognition rates. Conventional lip extraction methods have been proposed widely. Maia et. al. proposed a method by the sum of Cr and Cb. However, there are two problems as follows: the point with maximum saturation is not always regarded as lips region and the inner part of lips such as oral cavity and teeth can be classified as lips. To solve these problems, this paper proposes a method which adopts the histogram-based classifier for the extraction of lips region. The proposed method consists of two stages, learning and test. The amount of computation is minimized because this method has no color conversion. The performance of proposed method gives 66.8% of detection rate compared to 28% of conventional ones.

• Journal of IKEEE
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• v.24 no.1
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• pp.254-259
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• 2020

This paper is the implementation of the conversion system from image to music based on intentional synesthesia. The input image based on color, texture, and shape was converted into melodies, harmonies and rhythms of music, respectively. Depending on the histogram of colors, the melody can be selected and obtained probabilistically to form the melody. The texture in the image expressed harmony and minor key with 7 characteristics of GLCM, a statistical texture feature extraction method. Finally, the shape of the image was extracted from the edge image, and using Hough Transform, a frequency component analysis, the line components were detected to produce music by selecting the rhythm according to the distribution of angles.

• Journal of Korea Multimedia Society
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• v.8 no.12
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• pp.1649-1657
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• 2005

In this paper, a fast implementation of JPEG is discussed and its application to multimedia service is presented for mobile wireless internet. A fast JPEG player is developed based on several fast algorithms for mobile handset. In the color transformation, RCT is adopted instead of ICT for JPEG source. For the most time-consuming DCT part, the binDCT can reduce the decoding time. In upsampling and RGB conversion, the transformation from YCbCr to RGB 16 bit is made at one time. In some parts, assembly language is applied for high-speed. Also, an implementation of multimedia in mobile handset is described using MJPEG (Motion JPEG) and QCELP(Qualcomm Code Excited Linear Prediction Coding). MJPEG and QCELP are used for video and sound, which are synchronized in handset. For the play of MJPEG, the decoder is implemented as a S/W upon the MSM 5500 baseband chip using the fast JPEG decoder. For the play of QCELP, the embedded QCELP player in handset is used. The implemented multimedia player has a fast speed preserving the image quality.