Earticle

복층이 있는 공간의 음향 특성을 계산식, 시뮬레이션 및 실측을 통해 비교해 보았다. 언더발코니 스피커의 바로 뒤 지역은 메인스피 커와 딜레이 차이가 생기며, 중/저음 부분에서 충분한 음압저하가 이뤄지지 않아 컴필터링(Comb-Filtering) 현상이 크게 발생하게 된다. 또한 언더발코니 스피커의 바로 하부 지역은 스피커와 거리가 2～3m 내외로 언더발코니 스피커의 음압이 메인 스피커보다 커져 강대상과 음상일치가 깨지게 된다. 언더발코니 스피커에서 5～6m 이상 벗어나는 지역은 음의 역자승법칙에 따라 언더발코니 스피커 하부 지역보다 10dB이상 차이가 발생하며, 메인스피커에서도 중/고역 주파수(Mid/High Frequency) 음이 유입되지 않아 주 파수별 음압 편차가 크게 발생하여 집중도가 떨어지게 된다. 이러한 음향 문제를 최소화하기 위해서 메인스피커의 위치 및 출력에 따른 언더발코니 스피커의 적절한 배치와 출력이 요청된다. 발코니 하부 지역을 더 쾌적한 음향 환경으로 개선하기 위해서는 언더발 코니 스피커에 대한 적절한 음향설계 방향이 제시되어야 한다.

Formula acoustic characteristics of the room with a double layer , are compared through simulation and actual measurement. The rear area of the under- balcony speakers will cause a delay difference between the main speaker. In the mid / bass parts do not generate sufficient pressure is lowered and comb-Filtering phenomenon occurs significantly. The lower right area of the under- balcony speakers and speaker distance is the sound pressure of the under- balcony speakers to around 2 ~ 3m bigger than the main speakers and the sound image matches the pulpit is broken. Also, under area is more than 5 ~ 6m from the balcony outside speakers and causes differ by more than 10dB lower than the under- balcony speakers depending on the local laws of Translator wins Well, the main speaker at mid / high frequency sounds do not enter the sound pressure variations will drop by a significant. Appropriate arrangement and the output of the speaker according to the position under the balcony , and output of the main speakers are requested to minimize this problem sound . The proper sound design direction for the under- balcony speakers must be presented in order to improve the lower balcony area more pleasant acoustic environment.

In this paper, we employ the adaptive comb filtering for effective noise reduction in mobile communication environment. Adaptive comb filtering is a well- known method for noise reduction, but requires the correct pitch period and must be applied just in voiced speech frames. To satisfy these requirements we use two kinds of information extracted from speech packets, one of which is the pitch period information measured precisely by a speech coder and the other is the frame rate information related to a decision on speech or silence frame. Experiments on speech recognition system confirm the efficiency of this method. Feature parameters employing this method give superior performance in noise environment to those extracted directly from output speech.

In this paper, we employ the adaptive comb filtering for effective noise reduction in mobile communication environment. Adaptive comb filtering is a well-known method for noise reduction, but requires correct pitch period and must be applied just in voiced speech frames. To satisfy these requirements we use two kinds of information extracted from speech packets, one of which is the pitch period information measured precisely by a speech coder and the other is the frame rate information related to a decision on speech or silence frame. Experiments on speech recognition system confirm the efficiency of this method. Feature parameters employing this method give superior performance in noise environment to those extracted directly from output speech.

This paper describes a speech enhancement scheme that leads to significant improvements in recognition performance when used in the ASR front-end. The proposed approach is based on adaptive comb filtering and an MMSE-related parameter estimator. While adaptive comb filtering reduces noise components remarkably, it is rarely effective in reducing non-stationary noises. Furthermore, due to the uniformly distributed frequency response of the comb-filter, it can cause serious distortion to clean speech signals. This paper proposes an improved comb-filter that adjusts its spectral magnitude to the original speech, based on the speech absence probability and the gain modification function. In addition, we introduce the modified comb filtering-based speech enhancement scheme for ASR in mobile environments. Evaluation experiments carried out using the Aurora 2 database demonstrate that the proposed method outperforms conventional adaptive comb filtering techniques in both clean and noisy environments.

In this paper, the combinations of speech enhancement techniques are experimented. Specifically, the spectral subtraction, KLT based comb-filtering, and their combinations are applied to the Aurora2 database. The results show that recognition accuracy is improved when KLT based comb-filtering is applied after spectral subtraction.