In this paper, we propose a two-microphone noise reduction method to deal with non-stationary interfering noises in multiple-noise-source environments in which the traditional two-microphone algorithms cannot function well. In the proposed algorithm, multiple interfering noise sources are regarded as one virtually integrated noise source in each subband, and the spectrum of the integrated noise is then estimated using its virtual direction of arrival. To do this, we suggest a direction finder for the integrated noise using only two microphones that performs well even in speech active periods. The noise spectrum estimate is further improved by integrating a single-channel noise estimation approach and then subtracted from that of the noisy signal, finally enhancing the desired target signal. The performance of the proposed algorithm is evaluated and compared with the traditional algorithms in various conditions. Experimental results demonstrate that the proposed algorithm outperforms the traditional algorithms in various conditions in terms of objective and subjective speech quality measures.

This paper addresses a speech recognition problem in non-stationary noise environments: the estimation of noise sequences. To solve this problem, we present a particle filter-based sequential noise estimation method for front-end processing of speech recognition in noise. In the proposed method, a noise sequence is estimated in three stages: a sequential importance sampling step, a residual resampling step, and finally a Markov chain Monte Carlo step with Metropolis-Hastings sampling. The estimated noise sequence is used in the MMSE-based clean speech estimation. We also introduce Polyak averaging and feedback into a state transition process for particle filtering. In the evaluation results, we observed that the proposed method improves speech recognition accuracy in the results of non-stationary noise environments a noise compensation method with stationary noise assumptions.

The noise on power-lines is non-stationary, while the instantaneous noise power in different frequency bands are dependent. Under such noise environments, the instantaneous noise power in a frequency band can be estimated by observing the noise in other frequency bands. In this paper, we propose a receiver structure which uses the estimated instantaneous noise power in the decoding process and show its superiority in BER performance to conventional systems.

This paper presents an improved spectral subtraction method for speech enhancement. A new noise estimation method is derived in which the noise is assumed to be white. By using the property that a white noise spectrum is flat, high frequency components of a noisy speech spectrum are averaged and the standard deviation of the noise is estimated. This operation is performed in the analysis segment, thus the spectral subtraction method combined with the new noise estimation method does not need non-speech segments and as a result can adapt to non-stationary noise conditions. The effectiveness of the proposed spectral subtraction method is confirmed by experiments.

The noise level distribution owing to only a non-stationary working objective machine has been stochastically expressed by reflecting the temporal change of distribution parameters under a generalized regression model especially with aid of the vibration level observation. The proposed method has been applied to a noise evaluation of non-stationarily operated jigsaw.

Non-stationary glint noise is often observed in a radar tracking system. The distribution of glint noise is non-Gaussian and heavy-tailed. Conventional recursive identification algorithms use the stochastic approximation (SA) method. However, the SA method converges slowly and is invalid for non-stationary noise. This paper proposes an adaptive algorithm, which uses the stochastic gradient descent (SGD) method, to overcome these problems. The SGD method retains the simple structure of the SA method and is suitable for real-world implementation. Convergence behavior of the SGD method is analyzed and closed-form expressions for sufficient step size bounds are derived. Since noise data are usually not available in practice, we then propose a noise extraction scheme. Combining the SGD method, we can perform on-line adaptive noise identification directly from radar measurements. Simulation results show that the performance of the SGD method is comparable to that of the maximum-likelihood (ML) method. Also, the noise extraction scheme is effective that the identification results from the radar measurements are close to those from pure glint noise data.