Arata Kawamura

A Noise Reduction Method Based on Linear Prediction with Variable Step-Size

A noise reduction technique that uses the linear prediction to remove noise components in speech signals has been proposed previously. The noise reduction works well for additive white noise signals, because the coefficients of the linear predictor converge such that the prediction error becomes white. In this method, the linear predictor is updated by a gradient-based algorithm with a fixed step-size. However, the optimal value of the step-size changes with the values of the prediction coefficients. In this paper, we propose a noise reduction system using the linear predictor with a variable step-size. The optimal value of the step-size depends also on the variance of the white noise, however the variance is unknown. We therefore introduce a speech/non-speech detector, and estimate the variance in non-speech segments where the observed signal includes only noise components. The simulation results show that the noise reduction capability of the proposed system is better than that of the conventional one with a fixed step-size.

A new noise reduction method using linear prediction error filter and adaptive digital filter

A technique that uses a linear prediction error filter (LPEF) and an adaptive digital filter (ADF) to achieve noise reduction in a speech degraded by additive background noise is proposed. Since a speech signal can be represented as the stationary signal over a short interval of time, most of speech signal can be predicted by the LPEF. On the other hand, when the input signal of the LPEF is a background noise, the prediction error signal becomes white. Assuming that the background noise is generated by exciting a linear system with a white noise, then we can reconstruct the background noise from the prediction error signal by estimating the transfer function of noise generation system. This estimation is performed by the ADF which is used as system identification. Noise reduction is achieved by subtracting the reconstructed noise from the speech degraded by additive background noise.

Speech Enhancement Based on MAP Estimation Using a Variable Speech Distribution

A novel speech enhancement algorithm based on MAP estimation is proposed in this paper. The proposed speech enhancer uses a variable speech spectral distribution adjusted by the sum of power spectral densities. In a speech segment, the variable speech spectral distribution approaches to a Rayleigh density to keep the quality of the enhanced speech. While in a non-speech segment, it approaches to an exponential density so that the proposed speech enhancer reduces the noise strongly. Simulation results show the effectiveness of the proposed method

An Adaptive Algorithm with Variable Step-Size for Parallel Notch Filter

A parallel notch filter (PNF) for eliminating a sinusoidal signal whose frequency and phase are unknown, has been proposed previously. The PNF achieves both fast convergence and high estimation accuracy when the step-size for adaptation is appropriately determined. However, there has been no discussion of how to determine the appropriate step-size. In this paper, we derive the convergence condition on the step-size, and propose an adaptive algorithm with variable step-size so that convergence of the PNF is automatically satisfied. Moreover, we present a new filtering structure of the PNF that increases the convergence speed while keeping the estimation accuracy. We also derive a variable step-size scheme for the new PNF to guarantee the convergence. Simulation results show the effectiveness of the proposed method.

Noise suppression based on replacement of zero phase signal

This paper proposes a wide-band noise reduction method using a zero phase (ZP) signal which is defined as IDFT of a spectral amplitude. When a speech signal has periodicity in a short observation, the corresponding ZP signal becomes also periodic. On the other hand, when a noise spectral amplitude is approximately flat, its ZP signal takes nonzero values only around the origin. Hence, when a periodic speech signal is embedded in a flat spectral noise in an analysis frame, its ZP signal becomes a periodic signal except around the origin. In the proposed noise reduction method, we replace the ZP signal around the origin with the ZP signal in the second or latter period. The major advantages of this method are that it can reduce not only stationary wide-band noises but also non-stationary wide-band noises and does not require a prior estimation of the noise spectral amplitude. Simulation results show that the proposed noise reduction method improves the SNR more than 5dB for a tunnel noise and 15dB for a clap noise in a low SNR environment.

Speech Enhancement Based on MAP Estimation with a Variable Speech Distribution

A novel speech enhancement algorithm based on MAP estimation is proposed in this paper. The proposed speech enhancer uses a variable speech spectral distribution adjusted by the sum of power spectral densities. In a speech segment, the variable speech spectral disribution approaches to a Rayleigh density to keep the quality of the enhanced speech. While in a non-speech segment, it approaches to an exponential density so that the proposed speech enhancer reduces the noise strongly. Simulation results show the effectiveness of the proposed method.

A Noise Reduction System for Wideband and Sinusoidal Noise Based on Adaptive Line Enhancer and Inverse Filter

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.

An efficient zero phase noise reduction method for impact noise with damped oscillation

This paper proposes a noise reduction method for reducing impact noise. The proposed method is based on a zero phase (ZP) signal which is defined as the IDFT of a spectral amplitude. The ZP signal of the impact noise has zero values except of around the origin, since its spectral amplitude is almost flat. When a speech signal has periodicity, its ZP signal has also periodicity. Hence, when the speech signal mixed with the impact noise, the noise can be reduced by replacing samples around the origin with samples around the second period of the ZP signal. A practical impact noise often has damped oscillation which is similar to a periodic signal. Under the assumption that the pitch of the damped oscillation is higher than one of speech, we detect and reduce the damped oscillation by introducing an additional pitch estimator into the ZP replacement method.

A comb filter with adaptive notch bandwidth for periodic noise reduction

This paper proposes a comb filter with adaptive notch bandwidth. The comb filter is used to reduce a periodic noise from an observed signal. To extract the desired signal completely, we should appropriately design the notch bandwidth of the comb filter. Specifically, we should design the notch bandwidth to be equal to the fluctuation bandwidth. In this paper, to automatically reduce only the periodic noise with the frequency fluctuation, we propose the comb filter which achieves the adaptive notch bandwidth. Simulation results show the effectiveness of the proposed comb filter.

Sinusoidal Noise Reduction Method Using Leaky LMS Algorithm

A technique that uses a prediction error filter for reducing sinusoidal noises from a noisy speech has been proposed previously. Since the prediction error filter can estimate the sinusoidal noise completely, the output becomes zero in a non-speech segment. After the prediction error filter converges, the update of the filter coefficients is stopped. Then the fixed prediction error filter can cancel the sinusoidal noises except for a speech signal in a speech segment. However, frequency characteristics of the filter depend on its prediction algorithm, and the coefficients may converge the values which gives degradation of the speech. In this paper, we propose a new noise reduction algorithm which is a kind of leaky LMS algorithm, so that the prediction error filter removes only the sinusoidal line spectrum without speech degradation