Speech enhancement has been proposed to reduce the impulsive noise whose frequency characteristic is wideband. On the other hand, it is challenging to reduce the ringing sound, which is narrowband in impulsive noise. Therefore, we propose the modeling of the ringing sound and its estimation by a linear predictor (LP). However, it is difficult to estimate the ringing sound only in noisy speech due to the auto-correlation property of speech. The proposed system adopts the 4th order moment-based adaptive algorithm by noticing the difference between the 4th order statistics of speech and impulsive noise. The brief analysis and simulation results show that the proposed system has the potential to reduce ringing sound while keeping the quality of enhanced speech.

We propose an impact and high-pitch noise-suppression method based on spectral entropy. Spectral entropy takes a large value for flat spectral amplitude and a small value for spectra with several lines. We model the impact noise as a flat spectral signal and its damped oscillation as a high-pitch periodic signal consisting of spectra with several lines. We discriminate between the current noise situations by using spectral entropy and adaptively change the noise-suppression parameters used in a zero phase-based impact-noise-suppression method. Simulation results show that the proposed method can improve the perceptual evaluation of the speech quality and speech-recognition rate compared to conventional methods.

We propose an auxiliary-noise power-scheduling method for a pre-inverse active noise control (PIANC) system. Conventional methods cannot reduce the power of auxiliary-noise due to the use of the filtered-x least mean square (FxLMS) algorithm. We developed our power-scheduling method for a PIANC system to solve this problem. Since a PIANC system uses a delayed input signal for a control filter, the proposed method delivers stability even if the acoustic path fluctuates. The proposed method also controls the gain of the auxiliary-noise based on the secondary-path-modeling state. The proposed method determines this state by the variation in the power of the secondary-path-modeling-error signal. Thus, the proposed method changes the power-scheduling of the auxiliary-noise. When the adaptive algorithm does not sufficiently converge, the proposed method injects auxiliary-noise. However, auxiliary-noise stops when the adaptive algorithm sufficiently converges. Therefore, the proposed method improves noise reduction performance.

In orthogonal frequency division multiplexing (OFDM) system, a guard interval (GI) is used to remove the inter-symbol interference (ISI) due to a multipath channel. It is difficult to set an optimal GI length in the environment whose multipath varies. In this paper, we propose a variable guard interval based on the estimated maximum delay of a multipath channel. The maximum delay is estimated from a channel impulse response (CIR), which is estimated by a preamble symbol. However, since the estimated CIR includes the noise, it is difficult to decide the optimal GI. In order to solve the problem, we introduce the method which selects the path whose signal to noise ratio is high. Additionally, the information of the optimal GI length is required to be transmitted from a receiver to a transmitter. In this paper, we use an acknowledgment (ACK) frame for the feedback of the GI information.

We propose online feedback path modeling with a pre-inverse type active noise control (PIANC) system to track the fluctuation stably in the feedback path. The conventional active noise control (ANC) system with online feedback path modeling (FBPM) filter bases filtered-x least mean square (FxLMS) algorithm. In the FxLMS algorithm, the error of FBPM influences a control filter, which generates an anti-noise, and secondary path modeling (SPM) filter. The control filter diverges when the error is too large. Therefore, it is difficult for the FxLMS algorithm to track the feedback path without divergence. On the other hand, the proposed approach converges stably because the FBPM filter's error does not influence a control filter on the PIANC system. Thus, the proposed method can reduce noise while tracking the feedback path. This paper verified the effectiveness of the proposed method by convergence analysis, computer simulation, and implementation of a digital signal processor.

In a speech enhancement system for impact noise, it is important for any impact noise activity to be detected. However, because impact noise occurs suddenly, it is not always easy to detect. We propose a method for impact noise activity detection based on the kurtosis of an instantaneous power spectrum. The continuous duration of a generalized impact noise is shorter than that of speech, and the power of such impact noise varies dramatically. Consequently, the distribution of the instantaneous power spectrum of impact noise is different from that of speech. The proposed detection takes advantage of kurtosis, which depends on the sharpness and skirt of the distribution. Simulation results show that the proposed noise activity detection improves the performance of the speech enhancement system.

Conventional adaptive notch filter based on an infinite impulse response (IIR) filter is well known. However, this kind of adaptive notch filter has a problem of stability due to its adaptive IIR filter. In addition, tap coefficients of this notch filter converge to solutions with bias error. In order to solve these problems, an adaptive notch filter using Fourier sine series (ANFF) is proposed. The ANFF is stable because an adaptive IIR filter is not used as an all-pass filter. Further, the proposed adaptive notch filter is robust enough to overcome effects of a disturbance signal, due to a structure of the notch filter based on an exponential filter and line symmetry of auto correlation.

We have proposed a noise reduction method based on a noise reconstruction system (NRS). The NRS uses a linear prediction error filter (LPEF) and a noise reconstruction filter (NRF) which estimates background noise by system identification. In case a fixed step size for updating tap coefficients of the NRF is used, it is difficult to reduce background noise while maintaining the high quality of enhanced speech. In order to solve the problem, a variable step size is proposed. It makes use of cross-correlation between an input signal and an enhanced speech signal. In a speech section, a variable step size becomes small so as not to estimate speech, on the other hand, large to track the background noise in a non-speech section.

We propose a pre-filtering system for blind equalization in order to separate orthogonal frequency division multiplexing (OFDM) symbols in a multiple-input multiple-output (MIMO) - OFDM system. In a conventional blind MIMO-OFDM equalization without the pre-filtering system, there is a possibility that originally transmitted streams are permutated, resulting in the receiver being unable to retrieve desired signals. We also note that signal permutation is different for each subcarrier. In order to solve this problem, each transmitted stream of the proposed MIMO-OFDM system is pre-filtered by a unique allpass filter. In this paper, the pre-filter is referred to as transmit tagging filter (TT-Filter). At a receiver, an inverse filter of the TT-filter is used to blindly equalize a MIMO channel without permutation problem. Further, in order to overcome the issue of phase ambiguity, this paper introduces blind phase compensation.

A noise reduction technique to reduce wideband and sinusoidal noise in a noisy speech is proposed. In an actual environment, background noise includes not only wideband noise but also sinusoidal noise, such as ventilation fan and engine noise. In this paper, we propose a new noise reduction system which uses two types of adaptive line enhancers (ALE) and a noise estimation filter (NEF). First, the two ALEs are used to estimate speech components. The first ALE is used to reduce sinusoidal noise superposed on speech and wideband noise, while the second ALE is used to reduce wideband noise superposed on speech. However, since the quality of the speech enhanced by two ALEs is not good enough due to the difficulty in estimating unvoiced sound using the two ALEs, the NEF is used to improve on noise reduction capability. The NEF accurately estimates the background noise from the signal occupied by noise components, which is obtained by subtracting the speech enhanced by two ALEs from noisy speech. The enhanced speech is obtained by subtracting the estimated noise from noisy speech. Furthermore, the noise reduction system with feedback path is proposed to improve further the quality of enhanced speech.

A noise reduction technique to reduce background noise in noisy speech is proposed. We have proposed the noise reduction method which uses a noise reconstruction system. However, since a residual speech signal is included in the input signal of a noise reconstruction filter (NRF) used for reconstructing the background noise, the long time average value of error signal for estimating the background noise is needed not to estimate the speech signal. Therefore, the ability of tracking the non-stationary noise is decreased. In order to solve this problem, we propose the noise reconstruction system with adaptive line enhancer (ALE). Since ALE works to obtain the signal occupied by noise components, the input signal of the NRF includes only a few speech components. Therefore, we can give the high tracking ability to NRF.