• Phonetics and Speech Sciences
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• v.1 no.4
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• pp.105-109
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• 2009

This thesis describes the method for well voice announcement using the TTS(Text-To-Speech) technology in the shop music broadcasting service. Offering a high quality TTS sound service for each shop requires a great expense. According to a report on the architectural acoustics the room acoustic indexes such as reverberation time and early decay time are closely connected with a subjective awareness about acoustics. By using the result the customers will be able to recognize better the voice announcement by applying sound effect to speech files made by TTS. The result of an aural comprehension examination has shown better about almost all of the parameters by applying reverb effect to TTS sound.

• The Journal of the Acoustical Society of Korea
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• v.39 no.2
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• pp.120-130
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• 2020

This paper solves the problem in underdetermined convolutive mixture by improving the disadvantages of the multichannel nonnegative matrix factorization technique widely used in blind source separation. In conventional researches based on Spatial Covariance Matrix (SCM), each element composed of values such as power gain of single channel and correlation tends to degrade the quality of the separated sources due to high variance. In this paper, level and frequency normalization is performed to effectively cluster the estimated sources. Therefore, we propose a novel SCM and an effective distance function for cluster pairs. In this paper, the proposed SCM is used for the initialization of the spatial model and used for hierarchical agglomerative clustering in the bottom-up approach. The proposed algorithm was experimented using the 'Signal Separation Evaluation Campaign 2008 development dataset'. As a result, the improvement in most of the performance indicators was confirmed by utilizing the 'Blind Source Separation Eval toolbox', an objective source separation quality verification tool, and especially the performance superiority of the typical SDR of 1 dB to 3.5 dB was verified.