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In hands-free mobile communication, speech quality is often degraded due to presence of surrounding noise. This paper introduces an improved version of Minimum Mean Square Error (MMSE) noise estimator. Noise spectrum estimation is a crucial element used in speech recognition systems. Our proposed noise estimation method is based on a popular searching algorithm used in software engineering called...
The paper reports on the objective evaluation and comparison of the two noise estimation algorithms for noisy speech signals. Both algorithms are based on observation that local minima in noisy speech spectrogram are close to the power level of the noise signal. The first algorithm directly searches spectrogram for the local minima and those values use to update noise power spectrum density (psd)...
In this paper, a new approach is proposed to improve a sigmoid and conditional smoothing-based speech presence probability (SPP) method for noise power spectral density (PSD) estimation. In this approach, the a posteriori speech absence probability (SAP) is adapted with a sigmoid function mapped to the normalised spectral average variance in the consecutive frames that can effectively characterise...
In this paper, a noisy speech enhancement method based on modified spectral subtraction performed on short time magnitude spectrum is presented. Here the cross-terms containing spectra of noise and clean signals are taken into consideration which are neglected in the traditional spectral subtraction method on the basis of the assumption that clean speech and noise signals are completely uncorrelated...
In this paper, a speech enhancement method based on noise compensation performed on short time magnitude spectra is presented following the geometric approach. Here the noise estimate to be subtracted from the noisy speech spectrum is proposed to be determined by exploiting the low frequency regions of noisy speech of the current frame rather than depending only on the initial silence frames. We argue...
Existing speech enhancement methods can improve speech quality but not speech intelligibility, especially in low SNR conditions. To solve this problem, an algorithm for improving speech intelligibility using the Constraints on Speech Distortion and Noise Over-estimation (CSDNO) is proposed in this paper. Based on the fact that the attenuation distortion and amplification distortion have different...
This paper proposes a novel speech enhancement approach for a single-microphone system to meet the demand of quality noise reduction algorithms. The proposed system incorporates a perceptually motivated stationary wavelet packet filter-bank (PM-SWPFB) and improved spectral over-subtraction (I-SOS) algorithm together to enhance the speech degraded by non-stationary or colored noise environment. The...
This paper describes an algorithm for estimating breath noise in deep water diving helmet, which is the primary factor responsible for speech degradation. The algorithm consists of two parts: speech front-end detection based on pitch tracking, and noise estimation combined with energy criterion. Experimental results are reported and confirm the algorithm in efficiency and robustness.
How to utilize the time correlation of speech/nonspeech presence is a crucial problem faced by noise estimators. The popular technique of exploiting such correlation is to smooth noisy spectra by using a temporal recursive filter with a time-varying smoothing factor. But this technique cannot warrant the statistical optimality. In theory, hidden Markov model (HMM) is more desirable than this technique...
The spectral subtraction is a traditional approach for enhancing the quality of speech degraded by environmental noise. This algorithm is based on the subtraction of the estimated noise spectrum from the noisy speech spectrum and combines it with the phase of the noisy speech. Besides suppressing the noise, this method introduces an unnatural and unpleasant remnant noise. Several variants of this...
A noise estimation algorithm is proposed for single channel speech enhancement. By comparing the noise estimate with the short term noise and speech at every time frame, the noise estimate is efficiently updated by using a fixed step-size. The step size is optimized based on the speech quality performance and the noise tracking capability. The proposed technique is capable of tracking noise spectrum...
We propose an adaptive noise estimation and reduction algorithm which is capable of reducing additive noise from the noisy speech signals with low SNR values. The algorithm uses Modulated Complex Lapped Transform (MCLT) to estimate the power spectrum of input signal. The noise is estimated continuously from the spectrum using time-frequency dependent smoothing factor and tracking spectral minima....
a major part of the interaction between humans takes place via speech communication. It is very difficult to understand speech signals in presence of background noise for the normal listeners and hearing impaired persons. The human speech and hearing organ is inherently sensitive to interfering noise. Interfering noise decreases speech intelligibility and quality. Speech enhancement algorithms reduces...
This paper presents a noise estimation technique based on knowledge of pitch information for robust speech recognition. In the first stage the noise is estimated by means of extrapolating the noise from frames where speech is believed to be absent. These frames are detected with a proposed pitch based VAD (Voice Activity Detector). In the second stage the noise estimation is revised in voiced frames...
Noise statistics estimation is a paramount issue in the design of reliable noise-reduction algorithms. Although significant efforts have been devoted to this problem in the literature, most developed methods so far have focused on the single-channel case. When multiple microphones are used, it is important that the data from all the sensors are optimally combined to achieve judicious updates of the...
We propose a second-order-statistics-based approach to online multichannel noise tracking and reduction. We combine the multichannel speech presence probability (MC-SPP) that we proposed in with an alternative formulation of the minima-controlled recursive averaging (MCRA) technique that we generalize from the single- to the multichannel case. Then, we demonstrate the effectiveness of the proposed...
In this paper, noise estimation based on series expansion of orthogonal functions is proposed. The proposed method searches speechless frequency regions and estimates the noise spectrum from the searched speechless frequency regions. The proposed method can adapt to changing of noise power without estimation delay. Experimental results show that the proposed method provides a good performance against...
Frequency domain speech enhancement algorithm used in the single-microphone mobile phone usually needs a noise estimator. Minimum Statistics Method can track noise robustly. However, many algorithms based on minimum statistics method do not optimize their tracking factor. In this paper, we derive a new global optimal noise tracking factor controlled by another two new parameters. By combining with...
We propose an improved spectral subtraction method for the reduction of colored acoustic noise added to the speech. Our implementation uses a time-recursive algorithm for estimation of the noise power spectral density and a multi-band spectral over subtraction method to reduce the colored noise. Simulations show a better quality in terms of Itakura Saito distance and perceptual evaluation of quality...
This paper presents a novel speech enhancement algorithm that can substantially improve the signal-to-residual spectrum ratio by combining statistical estimators of the spectral magnitude of the speech and noise. The noise spectral magnitude estimator is derived from the speech magnitude estimator, by appropriately transforming the a priori and the a posteriori SNR values. By expressing the signal-to-residual...
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