As analog audio systems are changing to digital systems, the DAB (Digital Audio Broadcasting) is expected to provide CD quality audio, various data services with interactiveness and excellent mobile reception ability. The DMB (Digital Multimedia Broadcasting), as more advanced successor of the DAB, adds video capability on the audio and data services. The DAB system assures high quality audio services even when the reception is through portable and mobile receivers. In this paper, USB-DAB receiver and PCI-DMB receiver are designed and implemented. The DAB receiver and the DMB receiver incorporate with PC to make use of computational power and application software of Pc. This enables the developed system to be more flexible and to meet various applications easier.

In this paper, the AEC(acoustic echo canceller) is designed and implemented using VHDL(VHSIC hardware description language). The designed Echo Canceller employs the pipeline and the master-slave structure, and is realized with FPGA. As an adaptive algorithm, the Normalized LMS algorithm is used. For the coefficient adjustment, the Stochastic Iteration Algorithm(SIA) which uses only current residual values is used and the number of registers are evidently reduced and convergence speed is also much improved comparing to existing methods by using EAB of FPGA for FIR filter structure of transceiver. The designed Echo Canceller is verified with the test board implemented for this paper. From the timing simulation echo signals at about 1500 sampling data are converged and ERLE is improved by about 42-dB.

This paper presents two kinds of new approaches, one of which is concerned with recognition of emotional speech such as anger, happiness, normal, sadness, or surprise. The other is concerned with emotion recognition in speech. For the proposed speech recognition system handling human speech with emotional states, total nine kinds of prosodic features were first extracted and then given to prosodic identifier. In evaluation, the recognition results on emotional speech showed that the rates using proposed method increased more greatly than the existing speech recognizer. For recognition of emotion, on the other hands, four kinds of prosodic parameters such as pitch, energy, and their derivatives were proposed, that were then trained by discrete duration continuous hidden Markov models(DDCHMM) for recognition. In this approach, the emotional models were adapted by specific speaker's speech, using maximum a posteriori(MAP) estimation. In evaluation, the recognition results on emotional states showed that the rates on the vocal emotions gradually increased with an increase of adaptation sample number.

In this paper, we propose a classification system of TV program scenes based on audio information. The system classifies the video scene into six categories of commercials, basketball games, football games, news reports, weather forecasts and music videos. Two type of audio feature set are extracted from each audio frame-timbral features and coefficient domain features which result in 58-dimensional feature vector. In order to reduce the computational complexity of the system, 58-dimensional feature set is further optimized to yield l0-dimensional features through Sequential Forward Selection (SFS) method. This down-sized feature set is finally used to train and classify the given TV program scenes using κ -NN, Gaussian pattern matching algorithm. The classification result of 91.6% reported here shows the promising performance of the video scene classification based on the audio information. Finally, the system stability problem corresponding to different query length is investigated.

In 1896, Rayleigh was appointed as Scientific Advisor of the Trinity House. Rayleigh applied his knowledge of sound to developing effective fog signals during his IS-year tenure at the maritime organization. Rayleigh's expertise on acoustics met an appropriate field for its application during his improvement of the fog signal for the institution. Rayleigh's activities at the Trinity House were motivated by his desire to make contribution to the public. During his research on the fog signal, Rayleigh effectively employed his expertise acquired through his mathematical and experimental research on sound and vibration since the 1860s. Rayleigh developed effective horns for emitting fog signals and proposed various ways of overcoming the weaknesses of sound signals available at that time. While attempting to solve the problem of the attenuation of sound signals disseminating through the air, Rayleigh put foundations of atmospheric acoustics by developing new scientific theories about it.