Recently, several digital watermarking techniques have been proposed for hiding data in the frequency domain of audio files in order to protect their copyrights. In general, there is a tradeoff between the quality of watermarked audio and the tolerance of watermarks to signal processing methods, such as compression. In previous research, we simultaneously improved the performance of both by developing a multipurpose optimization problem for deciding the positions of watermarks in the frequency domain of audio data and obtaining a near-optimum solution to the problem. This solution was obtained using a wavelet transform and a genetic algorithm. However, obtaining the near-optimum solution was very time consuming. To overcome this issue essentially, we have developed an authentication method for digital audio using a discrete wavelet transform. In contrast to digital watermarking, no additional information is inserted into the original audio by the proposed method, and the audio is authenticated using features extracted by the wavelet transform and characteristic coding in the proposed method. Accordingly, one can always use copyright-protected original audio. The experimental results show that the method has high tolerance of authentication to all types of MP3, AAC, and WMA compression. In addition, the processing time of the method is acceptable for every-day use
We developed a method for pattern recognition of baby's emotions (discomfortable, hungry, or sleepy) expressed in the baby's cries. A 32-dimensional fast Fourier transform is performed for sound form clips, detected by our reported method and used as training data. The power of the sound form judged as a silent region is subtracted from each power of the frequency element. The power of each frequency element after the subtraction is treated as one of the elements of the feature vector. We perform principal component analysis (PCA) for the feature vectors of the training data. The emotion of the baby is recognized by the nearest neighbor criterion applied to the feature vector obtained from the test data of sound form clips after projecting the feature vector on the PCA space from the training data. Then, the emotion with the highest frequency among the recognition results for a sound form clip is judged as the emotion expressed by the baby's cry. We successfully applied the proposed method to pattern recognition of baby's emotions. The present investigation concerns the first stage of the development of a robotics baby caregiver that has the ability to detect babyʹs emotions. In this first stage, we have developed a method for detecting babyʹs emotions. We expect that the proposed method could be used in robots that can help take care of babies.
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