Noise Power Estimation Research Articles

Extension and evaluation of a near-end listening enhancement algorithm for listeners with normal and impaired hearing

In many applications in which speech is played back via a sound reinforcement system such as public address systems and mobile phones, speech intelligibility is degraded by additive environmental noise. A possible solution to maintain high intelligibility in noise is to pre-process the speech signal based on the estimated noise power at the position of the listener. The previously proposed AdaptDRC algorithm [Schepker, Rennies, and Doclo (2015). J. Acoust. Soc. Am. 138, 2692-2706] applies both frequency shaping and dynamic range compression under an equal-power constraint, where the processing is adaptively controlled by short-term estimates of the speech intelligibility index. Previous evaluations of the algorithm have focused on normal-hearing listeners. In this study, the algorithm was extended with an adaptive gain stage under an equal-peak-power constraint, and evaluated with eleven normal-hearing and ten mildly to moderately hearing-impaired listeners. For normal-hearing listeners, average improvements in speech reception thresholds of about 4 and 8 dB compared to the unprocessed reference condition were measured for the original algorithm and its extension, respectively. For hearing-impaired listeners, the average improvements were about 2 and 6 dB, indicating that the relative improvement due to the proposed adaptive gain stage was larger for these listeners than the benefit of the original processing stages.

FPGA Implementation of a Phase-Aware Single-Channel Speech Enhancement System

This paper presents a real-time architecture of an improved single-channel speech enhancement system based on phase-aware multi-band complex spectral subtraction. Using the proposed technique, the short-time spectral magnitude of the clean speech signal is estimated by considering the spectral phase of the speech and noise signal components. Moreover, the estimated spectral phase of the clean speech signal is also utilized for signal reconstruction in the time domain. The proposed system is made of the basic preprocessing module followed by an short-time Fourier transform analyzer, a noise power estimator based on improved minima controlled recursive array, a phase estimator unit and an overlap-add synthesis unit. The proposed architecture is implemented on a Field Programmable Gate Array (FPGA) using the Xilinx ISE tool. The overall resource utilization and the maximum operating frequency are also computed for a Virtex-6 FPGA chip. It has been experimentally shown that the proposed speech enhancement framework performs better than the other existing standard benchmark methods in terms of various quality and intelligibility assessment metrics.

On the Identification of SM and Alamouti-Coded SC-FDMA Signals: A Statistical-Based Approach

Signal identification represents the task of a receiver to identify the signal type and its parameters, with applications to both military and commercial communications. In this paper, we investigate the identification of spatial multiplexing (SM) and Alamouti (AL) space-time block code (STBC) with single carrier frequency division multiple access (SC-FDMA) signals, when the receiver is equipped with a single antenna. We develop a discriminating feature based on a fourth-order statistic of the received signal, as well as a constant false alarm rate decision criterion which relies on the statistical properties of the feature estimate. Furthermore, we present the theoretical performance analysis of the proposed identification algorithm. The algorithm does not require channel or noise power estimation, modulation classification, and block synchronization. Simulation results show the validity of the proposed algorithm, as well as a very good agreement with the theoretical analysis.

Noise estimation for speech enhancement algorithms with post-smoothness processor incorporating global posterior SNR

Performances of speech enhancement algorithms depend greatly on the accuracy of the estimated noise. In this paper, we explain in details the relationship between noise estimation and denoised speech quality. We particularly show the importance of noise smoothing over frames on denoising quality. This study leads to the development of a new technique to smooth the estimated noise power spectrum over frequency bins of the same frame. Compared to inter-frame smoothing, experimental results show that the proposed intra-frame smoothing has a good impact on the denoised speech. Quality is evaluated over three dimensions: speech distortion, residual background noise and overall quality.

A priori SNR estimation and noise estimation for speech enhancement.

A priori signal-to-noise ratio (SNR) estimation and noise estimation are important for speech enhancement. In this paper, a novel modified decision-directed (DD) a priori SNR estimation approach based on single-frequency entropy, named DDBSE, is proposed. DDBSE replaces the fixed weighting factor in the DD approach with an adaptive one calculated according to change of single-frequency entropy. Simultaneously, a new noise power estimation approach based on unbiased minimum mean square error (MMSE) and voice activity detection (VAD), named UMVAD, is proposed. UMVAD adopts different strategies to estimate noise in order to reduce over-estimation and under-estimation of noise. UMVAD improves the classical statistical model-based VAD by utilizing an adaptive threshold to replace the original fixed one and modifies the unbiased MMSE-based noise estimation approach using an adaptive a priori speech presence probability calculated by entropy instead of the original fixed one. Experimental results show that DDBSE can provide greater noise suppression than DD and UMVAD can improve the accuracy of noise estimation. Compared to existing approaches, speech enhancement based on UMVAD and DDBSE can obtain a better segment SNR score and composite measure covl score, especially in adverse environments such as non-stationary noise and low-SNR.

Robust acoustic bird recognition for habitat monitoring with wireless sensor networks

The key solution to study birds in their natural habitat is the continuous survey using wireless sensors networks (WSN). The final objective of this study is to conceive a system for monitoring threatened bird species using audio sensor nodes. The principal feature for their recognition is their sound. The main limitations encountered with this process are environmental noise and energy consumption in sensor nodes. Over the years, a variety of birdsong classification methods has been introduced, but very few have focused to find an adequate one for WSN. In this paper, a tonal region detector (TRD) using sigmoid function is proposed. This approach for noise power estimation offers flexibility, since the slope and the mean of the sigmoid function can be adapted autonomously for a better trade-off between noise overvaluation and undervaluation. Once the tonal regions in the noisy bird sound are detected, the features gammatone teager energy cepstral coefficients (GTECC) post-processed by quantile-based cepstral normalization were extracted from the above signals for classification using deep neural network classifier. Experimental results for the identification of 36 bird species from Tonga lake (northeast of Algeria) demonstrate that the proposed TRD–GTECC feature is highly effective and performs satisfactorily compared to popular front-ends considered in this study. Moreover, recognition performance, noise immunity and energy consumption are considerably improved after tonal region detection, indicating that it is a very suitable approach for the acoustic bird recognition in complex environments with wireless sensor nodes.

Noise Estimation and Suppression Using Nonlinear Function withA PrioriSpeech Absence Probability in Speech Enhancement

This paper proposes a noise-biased compensation of minimum statistics (MS) method using a nonlinear function anda priorispeech absence probability (SAP) for speech enhancement in highly nonstationary noisy environments. The MS method is a well-known technique for noise power estimation in nonstationary noisy environments; however, it tends to bias noise estimation below that of the true noise level. The proposed method is combined with an adaptive parameter based on a sigmoid function anda prioriSAP for residual noise reduction. Additionally, our method uses an autoparameter to control the trade-off between speech distortion and residual noise. We evaluate the estimation of noise power in highly nonstationary and varying noise environments. The improvement can be confirmed in terms of signal-to-noise ratio (SNR) and the Itakura-Saito Distortion Measure (ISDM).

Real-Time Noise Estimation and Correction in Dual-Polarization Radar Systems

Accurate noise-power estimation in dual-polarization weather radar is necessary for the correction of all power-based moments in low signal-to-noise ratio environments. A method of real-time noise-power estimation for dual-polarization radar systems using copolar correlation and receiver power is introduced. This real-time noise-power estimation algorithm is implemented in the NASA dual-frequency dual-polarization Doppler radar, and the performance results are presented.

Adaptive energy detector for spectrum sensing in cognitive radio networks

The traditional approach for energy detection (ED) consists in the comparison of the energy received against a fixed detection threshold, estimated according to an expected noise level. However, the noise power is not a static parameter since it varies as a function of the random sources of noise and interference present in the network. This random nature of the noise power originates the so-called noise uncertainty problem which adversely affects the performance of the ED. In this paper, we propose and evaluate by means of computer simulation an adaptive energy detector that incorporates a noise power estimation strategy for adjusting the detection threshold according to the noise power present at each sensing epoch. As it can be proved by simulation results, our strategy helps in reducing the sensing errors and to improve the ED's sensitivity by alleviating the negative effects of the noise uncertainty.

Block‐flow noise power estimation algorithm for pulsed interference detection of GNSS receivers

The noise power estimation for pulsed interference detection in global navigation satellite system (GNSS) receivers is considered. Previous effective consecutive mean excision (CME) related algorithms in this area work in an inter-block iterative fashion and the convergent process is usually quite time-consuming and needs a large storage space. A block-flow manner based on the backward CME (BCME) algorithm to overcome these problems is proposed. Numerical results show that the method decreases the computation and storage requirements of BCME, and possesses closer convergence properties.

Low-Complexity Direction-of-Arrival Estimation Based on Wideband Co-Prime Arrays

A class of low-complexity compressive sensing-based direction-of-arrival (DOA) estimation methods for wideband co-prime arrays is proposed. It is based on a recently proposed narrowband estimation method, where a virtual array model is generated by directly vectorizing the covariance matrix and then using a sparse signal recovery method to obtain the estimation result. As there are a large number of redundant entries in both the auto-correlation and cross-correlation matrices of the two sub-arrays, they can be combined together to form a model with a significantly reduced dimension, thereby leading to a solution with much lower computational complexity without sacrificing performance. A further reduction in complexity is achieved by removing noise power estimation from the formulation. Then, the two proposed low-complexity methods are extended to the wideband realm utilizing a group sparsity based signal reconstruction method. A particular advantage of group sparsity is that it allows a much larger unit inter-element spacing than the standard co-prime array and therefore leads to further improved performance.

Noise Power Estimation Using Wavelet Packet-Adaptive Thresholding

We describe a method to estimate noise power using the minimum statistics approach, which was originally proposed for audio processing. The proposed minimum statistics-based method separates a noisy image into multiple frequency bands using the wavelet packet transform. By assuming that the output of the high-pass filter contains both signal detail and noise, the proposed algorithm extracts the region of pure noise from the high frequency band using an appropriate threshold. The region of pure noise, which is free from the signal detail and the DC component, is well suited for minimum statistics conditions, in which the noise power can easily be extracted. The proposed algorithm significantly reduces the computational load by using a simple, iteration-free processing architecture, and provides an estimation accuracy rate of over 96 % for strong noise at an SNR of 0 to 30 ㏈ in the input image. Experimental results show that the proposed algorithm can accurately estimate noise power, and is particularly suitable for fast, low-cost image restoration or enhancement applications.

Capacity-Aware Cooperative Spectrum Sensing Based on Noise Power Estimation

Most cooperative spectrum sensing research activities focus on maximizing the primary user detection and neglect the achieved secondary system performance. This paper proposes a novel capacity-aware cooperative spectrum sensing optimization method, capable of computing the optimal values for the cooperative sensing parameters (e.g., the number of sensed samples, the number of cooperating devices, control channel bandwidth) so that the secondary system capacity is maximized. The proposed method utilizes energy detection with estimated noise power (ENP). We derive and experimentally validate the analytical models for cooperative spectrum sensing based on the ENP method. Additionally, we evaluate the performance of the proposed capacity-aware spectrum sensing described in this paper. We show that the proposed capacity-aware cooperative spectrum sensing based on noise power estimation achieves enhanced secondary system capacity compared to the previous commonly proposed sensing processes that maximize the detection performance. Moreover, the results give insight into the optimal behavior of the proposed capacity-aware cooperative spectrum sensing based on noise power estimation regarding the common cooperative spectrum sensing parameters.

Noise Power Estimation in SC-FDMA Systems

This article addresses the problem of noise power estimation for single-carrier frequency-division-multiple-access (SC-FDMA), which is used in long-term evolution (LTE). If channels are known, signal-to-noise ratio (SNR) can be derived from the noise power. A maximum-likelihood (ML) estimator is first derived to obtain a temporal noise power and then a minimum-variance unbiased (MVU) estimator is adopted to obtain an average noise power over the time-frequency plane. Since there is no analytical solution for the MVU estimator, an iterative approach based on fixed-point iteration (FPI) is employed to solve MVU estimation.

Wideband Spectrum Sensing for Cognitive Radios: A Multistage Wiener Filter Perspective

A wideband spectrum sensing scheme for cognitive radios is put forward in this letter. The proposed method operates over the total frequency bands simultaneously rather than a single band each time. It first estimates the number of occupied subbands, and then determines the accurate locations of occupied subbands as well as the vacant ones. Applying the ideas of multi stage Wiener filter to Gerschgorin disk estimator, this approach jointly makes the decision. In this way, the detection performance is enhanced because of capturing the signal information and suppressing the additive noise. Meanwhile, this approach avoids estimating the covariance matrix as well as the eigenvalue decomposition, and thus achieves a low computational complexity. Distinct from the conventional algorithms, neither noise power estimation nor prior knowledge of primary user signal is required, making the proposed algorithm robust to noise uncertainty and suitable for blind detection. Simulations under various conditions are presented to demonstrate the benefits of this approach and the results indicate that it surpasses the existing sensing algorithms.

Effects of Radial-Based Noise Power Estimation on Spectral Moment Estimates

AbstractA radar antenna intercepts thermal radiation from various sources, including the ground, the sun, the sky, precipitation, and man-made radiators. In the radar receiver, this external radiation produces noise that constructively adds to the receiver internal noise and results in the overall system noise. Consequently, the system noise power is dependent on the antenna position and needs to be estimated accurately. Inaccurate noise power measurements may lead to a reduction of coverage if the noise power is overestimated or to radar data images cluttered by noise speckles if the noise power is underestimated. Moreover, when an erroneous noise power is used at low to moderate signal-to-noise ratios, estimators can produce biased meteorological variables. Therefore, to obtain the best quality of radar products, it is desirable to compute meteorological variables using the noise power measured at each antenna position. An effective technique that achieves this by estimating the noise power in real time from measured powers at each scan direction and in parallel with weather data collection has been proposed. Herein, the effects of such radial-based noise power estimation on spectral moment estimates are investigated.

Multiband Detection for Spectrum Sensing: A Multistage Wiener Filter Perspective

In the recent years, spectrum scarcity becomes an urgent issue due to the emergence of wireless services. The effective utilization of spectrum white space has gained significant research interests. Cognitive radio techniques have been paid much attention to the television white space. This paper raises a multiband spectrum sensing scheme to detect the spectrum white space which is not limited to television bands. When performing spectrum sensing, our approach operates over the total frequency bands simultaneously rather than a single band each time. By applying the idea of multistage Wiener filter to Gerschgorin disk estimator, our approach jointly makes the decision. In this way, the proposed method is able to capture the signal information and suppress the additive noise, which brings about an enhanced detection performance. Distinct from the classical methods, the proposed scheme requires neither noise power estimation nor prior knowledge of primary user signal, thereby being robust to noise uncertainty and suitable for blind detection. On the contrary, in the context of noise uncertainty, noise variance has no access to accurate estimation, inducing an imprecise decision threshold, which severely deteriorates the detection performance. Besides, our method avoids the estimation of covariance matrix as well as eigenvalue decomposition, and thus achieves a low computational complexity. This paper presents simulations under various conditions to verify the performance of the proposed scheme and the results show that it is superior to the existing sensing algorithms.

Energy Detection of DVB-T Signals Against Noise Uncertainty

In this letter, we consider the spectrum sensing of digital video broadcasting-terrestrial (DVB-T) signals with energy detection (ED). ED delivers a superior performance among low computational complexity detectors, but it is vulnerable to noise uncertainty. By exploiting the pilot periodicity in DVB-T signals, we propose a low complexity noise power estimator which works in multipath channels, and does not require time and frequency synchronization. Simulations show that, with the noise power estimator, ED significantly outperforms the pilot correlation-based detection which is often used in DVB-T spectrum sensing.

Subspace based adaptive denoising of surface EMG from neurological injury patients

Objective: After neurological injuries such as spinal cord injury, voluntary surface electromyogram (EMG) signals recorded from affected muscles are often corrupted by interferences, such as spurious involuntary spikes and background noises produced by physiological and extrinsic/accidental origins, imposing difficulties for signal processing. Conventional methods did not well address the problem caused by interferences. It is difficult to mitigate such interferences using conventional methods. The aim of this study was to develop a subspace-based denoising method to suppress involuntary background spikes contaminating voluntary surface EMG recordings. Approach: The Karhunen–Loeve transform was utilized to decompose a noisy signal into a signal subspace and a noise subspace. An optimal estimate of EMG signal is derived from the signal subspace and the noise power. Specifically, this estimator is capable of making a tradeoff between interference reduction and signal distortion. Since the estimator partially relies on the estimate of noise power, an adaptive method was presented to sequentially track the variation of interference power. The proposed method was evaluated using both semi-synthetic and real surface EMG signals. Main results: The experiments confirmed that the proposed method can effectively suppress interferences while keep the distortion of voluntary EMG signal in a low level. The proposed method can greatly facilitate further signal processing, such as onset detection of voluntary muscle activity. Significance: The proposed method can provide a powerful tool for suppressing background spikes and noise contaminating voluntary surface EMG signals of paretic muscles after neurological injuries, which is of great importance for their multi-purpose applications.

On the Use of a Radial-Based Noise Power Estimation Technique to Improve Estimates of the Correlation Coefficient on Dual-Polarization Weather Radars

Abstract A weather surveillance radar antenna intercepts thermal radiation from various sources, including the ground, the sun, the sky, and precipitation. In the radar receiver, this external radiation produces noise that adds to the receiver internal noise and results in the system noise power varying with the antenna position. If these variations are not captured, they translate into erroneous signal powers because these are computed via subtraction of noise power measurements from the overall power estimates. This may lead to biased meteorological variables at low to moderate signal-to-noise ratios if those are computed using signal power estimates. In dual-polarization radars, this problem is even more pronounced, particularly for correlation coefficient estimates that use noise power measurements from both the horizontal and vertical channels. An alternative is to use estimators that eliminate the need for noise corrections but require sufficient correlation of signals in sample time, which limits their applicability. Therefore, when the use of the latter is inappropriate, the quality of correlation coefficient estimates can be improved by computing them using sufficiently accurate noise powers measured at each antenna position. An effective technique that estimates the noise powers in real time at each scan direction and in parallel with weather data collection has been proposed. Herein, the impacts of such a technique on the estimation of the correlation coefficient are investigated. The results indicate that the use of more accurate noise power estimates can significantly reduce the bias of correlation coefficient estimates, thus visibly improving the correlation coefficient fields. This is expected because the correlation coefficient is computed using noise power measurements from both the horizontal and vertical channels.