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This paper presents a new impulsive noise detection algorithm in speech signal. The proposed method employs the frequency domain characteristic of the impulsive noise to improve the detection accuracy while avoiding the false-alarm problem by the pitch of the speech signal. Furthermore, we proposed time-frequency domain impulsive noise detector tha...
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This paper presents a novel iterative receiver used to mitigate the impact of impulsive noise (IN) on orthogonal frequency-division multiplexing (OFDM)-based baseband power-line communications. An adaptive threshold is mathematically derived for the detection of IN under a desired false alarm probability. This detection mechanism is then used to mi...
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... Now the investigation effects the changing the sizes of half-overlapping information buffer and Hanning window for time improving transients removal design, The overlying of data buffers was changed between half and quarter overlapping and Hanning window span was speckled beginning 256 points to half of it, and double of 256 points. This process is revealed with in Figure 3. [4,15,16]. Experimental results utilizing the noisy transient detector basic in [16] are almost same as results with the hand marked noise detection result shown in this article. (7). ...
... This process is revealed with in Figure 3. [4,15,16]. Experimental results utilizing the noisy transient detector basic in [16] are almost same as results with the hand marked noise detection result shown in this article. (7). ...
... denotes the detection flag of noisy transient presence which becomes one when the noise exists and vice versa. It can be determined by comparing time domain energy, the frequency-domain energy, or the cross-correlation of input signal[4,6,15,16]. For example, a time-frequency domain transient noise detector basic in[16] shows 99.3% of detection accuracy while making only 1.49% of false-alarm. ...
... H T (n) in Eq. (3) denotes the detection flag of transient noise presence which becomes one when the noise exists and vice versa. It can be determined by comparing the timedomain energy, the frequency-domain energy, or the cross-correlation of input signal [4,6,15,16]. For example, a time-frequency domain transient noise detector proposed in [16] shows 99.3% of detection accuracy while making only 1.49% of false-alarm. ...
... It can be determined by comparing the timedomain energy, the frequency-domain energy, or the cross-correlation of input signal [4,6,15,16]. For example, a time-frequency domain transient noise detector proposed in [16] shows 99.3% of detection accuracy while making only 1.49% of false-alarm. Employing the transient noise detection result, the median filter can be applied only to the noise presence region. ...
... The median filter and the LTP filter are applied only at transient noise presence region by utilizing the handmarked result of the noise presence. However, the transient noise presence region can be detected by measuring the time-or the frequency-domain energy of the input signal with a certain threshold [4,15,16]. Experimental results utilizing the transient noise detector proposed in [16] are almost same as results with the handmarked noise detection result shown in this article. The length of the median filter, 2w + 1, used for the experiments is 101 samples, and the frame size for the LTP, M, is 32 samples. ...
This article proposes an efficient median filter based algorithm to remove transient noise in a speech signal. The proposed algorithm adopts a modified long-term predictor (LTP) as the pre-processor of the noise reduction process to reduce speech distortion caused by the nonlinear nature of the median filter. This article shows that the LTP analysis does not modify to the characteristic of transient noise during the speech modeling process. Oppositely, if a short-term linear prediction (STP) filter is employed as a pre-processor, the enhanced output includes residual noise because the STP analysis and synthesis process keeps and restores transient noise components. To minimize residual noise and speech distortion after the transient noise reduction, a modified LTP method is proposed which estimates the characteristic of speech more accurately. By ignoring transient noise presence regions in the pitch lag detection step, the modified LTP successfully avoids being affected by transient noise. A backward pitch prediction algorithm is also adopted to reduce speech distortion in the onset regions. Experimental results verify that the proposed system efficiently eliminates transient noise while preserving desired speech signal.
The speech recording of the National Archives of Korea contains very important traces which represent modern times of Korea. But the way to be recorded by analogue is easily contaminated as time goes by. So it has to be digitalized for management and services. Consequently, restoration method of distorted speech is needed. We propose the four classes for each distortion kind and apply restoration algorithms for the cases of speech level, stationary noise and abrupt noise. As a result, speech volume adjusts to -26 dBov for only on the speech region and SNR improves above 10dB. Especially, conventional way to remove the noise is almost impossible because we need to listen to all of them but it can be more effective by adaptation of auto restoration algorithm.
