• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.293-296
• /
• 2016

Speech Signals consist of signals of consonants and vowels, but the lasting time of vowels is much longer than that of consonants. It can be assumed that the correlations between signal blocks in speech signal is very high. But the correlations between signal blocks in various frequency bands can be quite different. Each speech signal is divided into blocks which have 128 speech data. FFT is applied to each block. Various frequency areas of the results of FFT are taken and Covariance matrix between blocks in a speech signal is extracted and finally eigenvalues of those matrix are obtained. It is studied that in the eigenvalues of various frequency bands which band can be used to get more reliable result.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.5
• /
• pp.1076-1082
• /
• 2013

The frequency bands were discovered which maximize the slopes of autocovariances of speech signals in frequency domain to increase the possibility of segregation between speech signals and background noise signal. A speech signal is divided into blocks which include multiples of sampled data, then those blocks are transformed to frequency domain using Fast Fourier Transform(FFT). To find linear equation by Linear Regression, the coefficients of autocovariance within blocks of some frequency band are used. The slope of the linear equation which is called the slope of autocovariance is varied from band to band according to the characteristics of the speech signal. Using speech signals of a man which consist of 200 files, the coefficients of the slopes of autocovariances are analyzed and compared from band to band.

• Proceedings of the IEEK Conference
• /
• 2000.09a
• /
• pp.755-758
• /
• 2000

본 논문에서는 AMR(Adaptive Multi-Rate)를 이용하여 광대역 음성부호화기를 설계하였다. 16kHz로 샘플링 된 입력 신호를 QMF 필터에 의해 두 개의 대역으로 나누어, 각각 decimation하여 두 개의 8kHz 샘플링 신호로 변환시킨 후 저대역(0Hz-3400Hz)의 신호와 고대역(3400Hz -7000Hz)의 신호로 나누어 각각 부호화한다. 나누어진 두 개의 협대역 음성신호는 AMR(Adaptive Multi-Rate)과 ATC(Adaptive Transform Coding)을 사용하여 각각 부호화되어 전송된다. 두 대역으로부터 부호화된 정보는 20.2kbps에서 12.75kbps까지의 전송률을 갖고, 수신단에서는 각 대역을 AMR과ATC방법으로 역부호화하여 음성신호를 합성한다. 설계된 광대역 음성부호화기의 성능을 평가하기 위해 ITU-T의 표준안인 G.722를 포함하여 MOS 시험을 하였다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.26 no.5B
• /
• pp.632-638
• /
• 2001

본 논문에서는 AMR(Adaptive Multi-Rate)를 이용하여 광대역 음성부호화기를 설계하였다. 16kHz로 샘플링된 입력 신호를 QMF 필터에 의해 두 개의 대역으로 나누어, 각각 decimation하여 두 개의 8kHz 샘플링 신호로 변환시킨 후 저대역(0Hz-3400Hz)의 신호와 고대역(3400Hz∼7000Hz)의 신호로 나누어 각각 부호화한다. 나누어진 두 개의 협대역 음성신호는 AMR(Adaptive Multi-Rate)과 ATC(Adaptive Transform Coding)을 사용하여 각각 부호화되어 전송된다. 두 대역으로부터 부호화된 정보는 20.2kbps에서 12.75kbps까지의 전송률을 갖고, 수신단에서는 각 대역을 AMR과 ATC 방법으로 역부호화하여 음성신호를 합성한다. 설계된 광대역 음성부호화기의 성능을 평가하기 위해 ITU-T의 표준안인 G.722를 포함하여 MOS 시험을 하였다.

• Proceedings of the IEEK Conference
• /
• 2001.09a
• /
• pp.809-812
• /
• 2001

본 논문에서는 AMR(Adaptive Multi-Rate)과 MLT (Modulated Lapped Transform) 벡터 양자화 방법을 이용하여 광대역 음성부호화기를 설계하였다. 제안한 음성부호화 알고리즘은 split-band 구조를 가지고 있으며 16kHz로 샘플링 된 신호를 입력받아 QMF 필터에 의해 두 개의 대역으로 나누어, 각각 8kHz 샘플링 신호로 변환시킨 후 저대역(0Hz-3400Hz)의 신호와 고대역(3400Hz -7000Hz)의 신호로 나누어 각각 부호화한다. 나누어진 두 개의 협대역 음성신호는 AMR(Adaptive Multi-Rate)부호화기와 MLT (Modulated Lapped Transform)벡터 양자화 방법을 사용하여 각각 부호화되어 전송된다. 수신단에서는 각 대역을 AMR과 IMLT(Inverse MLT) 벡터 양자화 방법으로 역부호화하여 음성신호를 합성한다. 제안한 음성부호화기는 20.2kbps에서 12.15kbps까지의 다전송률로 동작된다. 설계된 광대역 음성부호화기는 MOS시험 결과로부터 G.722의 56 kbps 음성이 설계된 코더의 20.2 kbps와 비슷한 음질을 갖음을 확인할 수 있었다.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.2
• /
• pp.81-89
• /
• 2008

Wideband speech, characterized by a bandwidth of about 7 kHz (50-7000 Hz), provides a substantial quality improvement in terms of naturalness and intelligibility. Although higher data rates are required, it has extended its application to audio and video conferencing, high-quality multimedia communications in mobile links or packet-switched transmissions, and digital AM broadcasting. In this paper, we present a new bandwidth-scalable coder for wideband speech and audio signals. The proposed coder spits 8kHz signal bandwidth into two narrow bands, and different coding schemes are applied to each band. The lower-band signal is coded using the ITU-T G.729/G.729E coder, and the higher-band signal is compressed using a new algorithm based on the gammatone filter bank with an invertible auditory model. Due to the split-band architecture and completely independent coding schemes for each band, the output speech of the decoder can be selected to be a narrowband or wideband according to the channel condition. Subjective tests showed that, for wideband speech and audio signals, the proposed coder at 14.2/18 kbit/s produces superior quality to ITU-T 24 kbit/s G.722.1 with the shorter algorithmic delay.

• The Journal of the Acoustical Society of Korea
• /
• v.24 no.7
• /
• pp.395-401
• /
• 2005

In this paper, the high-band codec for bandwidth scalable wideband speech codec is proposed. The wideband input speech signal is separated into low-band signal and high-band signal, and the low-band signal is encoded by the standard narrow-band speech codec and the high-band signal is encoded by the proposed codec. In the high-band codec. the signal is transformed into frequency domain by MLT on a subframe basis, and MLT coefficients are splitted into magnitude and sign for quantization. The magnitudes of MLT coefficients are arranged into several time-frequency bands and each band is quantized in 2D-DCT domain, where the low-band information is utilized for better performance. The sign of MLT coefficient is quantized based on a priority selection process with the weighting measurement. The objective and subjective performance of wideband speech codec including the proposed high-band codec is measured, and it is confirmed that the proposed codec has better performance than 32kbps G.722.1.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.5
• /
• pp.811-816
• /
• 2019

This paper first proposes a technique to separate clean speech signals and mixed noise signals by using an independent vector analysis algorithm of frequency band for 4 channel speech source signals with a noise. An improved output speech signal from the proposed independent vector analysis algorithm is obtained by using the cross-correlation between the signal outputs from the frequency domain delay-sum beamforming and the output signals separated from the proposed independent vector analysis algorithm. In the experiments, the proposed algorithm improves the maximum SNRs of 10.90dB and the segmental SNRs of 10.02dB compared with the frequency domain delay-sum beamforming algorithm for the input mixed noise speeches with 0dB and -5dB SNRs including white noise, respectively. Therefore, it can be seen from this experiment and consideration that the speech quality of this proposed algorithm is improved compared to the frequency domain delay-sum beamforming algorithm.

• Proceedings of the IEEK Conference
• /
• 2000.09a
• /
• pp.783-786
• /
• 2000

본 논문에서는 웨이브렛 변환을 적용한 광대역 음성부호화 알고리즘을 제안하였다. 제안한 음성부호화 알고리즘은 split-band 구조를 가지며, 16 kHz로 sampling된 입력신호를 QMF를 이용해서 동일한 대역폭을 갖는 두 개의 subband 신호로 나누고 이를 8kHz의 sampling율을 갖도록 downsampling 한다. 그리고 저대역 신호는 GSM-EFR 음성부호화 알고리즘을 이용하여 부호화하고, 고대역 신호는 DWT(Discrete Wavelet Transform)을 적용하여 subband로 나누어 부호화하였다. 각 subband에서 양자화 된 파라미터는 IDWT(Inverse DWT)과정을 거쳐서 upsampling되고 합성 QMF를 통과시켜 최종 합성음을 구하였다. 제안한 음성부호화기는 저대역 신호의 GSM-EFR 부호화에 12.2 kbps, 웨이브렛 변환을 이용한 고대역 신호의 부호화에 7.8 kbps로 전체 20 kbps의 전송율을 가지면서 G.722 표준안의 56 kbps에서의 합성음과 비슷한 음질을 나타내었다.

• The Journal of the Acoustical Society of Korea
• /
• v.29 no.2
• /
• pp.148-153
• /
• 2010

In speech signal processing, speech signal corrupted by noise should be enhanced to improve quality. Usually noise estimation methods need flexibility for variable environment. Noise profile is renewed on silence region to avoid effects of speech properties. So we have to preprocess finding voice region before noise estimation. However, if received signal does not have silence region, we cannot apply that method. In this paper, we proposed SNR estimation method for continuous speech signal. A Speech signal consists of Voice and Unvoiced Band in The MBE excitation model. And the energy of speech signal is mostly distributed on voiced region, so we can estimate SNR by the ratio of voiced region energy to unvoiced. We use the IMBE vocoder for the Voice or Unvoice band of segmented speech signal. Continuously we calculate the segmented SNR using that information and the energy of each band. And we estimate the SNR of continuous speech signal.