Time-varying Background Noise Research Articles

Exoplanet imaging performance envelopes for starshade-based missions

We lay out the capabilities and limitations of starshade-based missions aiming to measure the reflected light spectra of temperate planets from an imaging perspective. We use the Starshade Imaging Simulation Toolkit for Exoplanet Reconnaissance to conduct high fidelity end-to-end optical simulations, taking a step forward from simplified analytical equations, exploring and quantifying the impact of an array of observational conditions, including natural parameters such as target star types, planet types, distances, planet phases, and exo-zodiacal dust, and starshade perturbations such as tilt, shift off line of sight, edge errors, and glint. We find that signal-to-noise ratio (SNR) requirements used for establishing detection and spectral characterization, is not suitable under realistic observation conditions for a wide range of targets. We show that even if we assume that the spatially distributed, time-varying background noise could be known and calibrated to a level of 1%, each target star will need its own SNR requirement based on its unique observation conditions, nearly always resulting in a higher threshold SNR, with values as high as X5 from currently established requirement, and in some cases impossible to detect. We conduct statistical analysis using end-to-end optical simulations, taking into account observationally based priors and update previously established completeness values for an array of target stars and mission configurations, accounting for starshade perturbations, and background knowledge at a level of one percent and find that completeness values are negatively impacted and reduced by up to 50% across targets even at ranges shorter than 10 pc. Finally, we utilize information from over hundreds of thousands of detailed imaging simulations to map accessible target stars for both optimistic and pessimistic scenarios, reassessing the expected capabilities of starshade-based high contrast direct imaging missions.

Seasonal variations of infrasound detections and their characteristics in the western US

Automatic infrasound detection is analyzed with infrasonic data from the western US for the time period of November 2010 to October 2012. Data from nine University of Utah Seismograph Stations (UUSS) infrasonic arrays are supplemented by three additional arrays in Nevada, operated by Southern Methodist University (SMU). In this study, the detection procedure is based on an adaptive F-detector (AFD) that accounts for both correlated and uncorrelated noise by capturing the time-varying background noise conditions. The adaptation of the detector, characterized by time varying linear remapping of the F-distribution (C-value), depends on the background noise level at the arrays including weather conditions with seasonal variations and local site effects. The infrasound detection catalog consists of 580,177 signals, depending on time and space as well as data availability at individual stations. Based on the Ground-to-Space (G2S) specifications, the number of detections is shown to be correlated with seasonal variations in the stratospheric winds, illustrating the time varying nature of infrasound propagation. These seasonal variations of infrasound detections include the number of detections, correlation value, occurrences in time, phase velocity, and azimuth. The majority of events occur during working hours, Monday through Friday, suggesting a human cause. Based on the automatic detection results, we reviewed the waveforms generated from the known repeated sources such as Dugway Testing Ground (DTG) and Utah Test and Training Range (UTTR) in Utah, and New Bomb (NB) in Nevada. In order to validate the automatic infrasound detection catalog, we document the celerities of automatic infrasound arrivals during the summer and winter using the seismic origin times of NB explosions. Infrasound propagation is dependent on the seasonal variation of wind conditions as well as source location and array distribution.

Speech Enhancement Based on Sequential Noise Estimation with a Masking Property

In this paper, we describe a method for enhancing speech that has been corrupted by additive background noise that varies in time. The proposed nonlinear spectral-subtraction approach is based on sequentially updating the estimated noise in each frame and then using an adaptive mask that is based on the properties of the human ear. The intent of this method is to use sequential estimation of the time-varying background noise in order to decrease the perceived noise in the speech segments. Furthermore, in order to enhance the speech elements obtained by using this masking effect, we use the noise-masking signal-to-noise ratio (NMSNR), which is calculated from the regression line, to adaptively control the scaling function. Results of experiments show that the proposed approach can efficiently remove additive noise due to various sources.

시변 잡음에 강인한 음성 인식을 위한 PCA 기반의 Variational 모델 생성 기법

본 논문에서는 시간에 따라 변하는 잡음 환경에 강인한 음성 인식을 위해 효과적인 특징 보상 기법을 제안한다. 제안하는 기법에서는 기존의 Variational 모델 생성 기법의 모델 정확도를 향상시키고자 PCA를 도입한다. 제안된 기법은 다중 모델을 사용하는 PCGMM 기반의 특징 보상에 적용된다. 실험 결과는 제안한 PCA 기반의 Variational 모델 생성 기법이 배경 음악 환경의 다양한 SNR 조건에서 기존의 전처리 기법에 비하여 음성 인식 성능을 향상 시키는데 우수함을 입증한다. 제안한 모델 생성 기법이 기존의 Variational 모델 생성 방법에 비해 배경 음악 환경에서 평균 12.14%의 상대적 인식 성능 향상률을 나타낸다. This paper proposes an effective feature compensation method to improve speech recognition performance in time-varying background noise condition. The proposed method employs principal component analysis to improve the variational model composition method. The proposed method is employed to generate multiple environmental models for the PCGMM-based feature compensation scheme. Experimental results prove that the proposed scheme is more effective at improving speech recognition accuracy in various SNR conditions of background music, compared to the conventional front-end methods. It shows 12.14% of average relative improvement in WER compared to the previous variational model composition method.

Variational noise model composition through model perturbation for robust speech recognition with time-varying background noise

This study proposes a novel model composition method to improve speech recognition performance in time-varying background noise conditions. It is suggested that each element of the cepstral coefficients represents the frequency degree of the changing components in the envelope of the log-spectrum. With this motivation, in the proposed method, variational noise models are formulated by selectively applying perturbation factors to the mean parameters of a basis model, resulting in a collection of noise models that more accurately reflect the natural range of spectral patterns seen in the log-spectral domain. The basis noise model is obtained from the silence segments of the input speech. The perturbation factors are designed separately for changes in the energy level and spectral envelope. The proposed variational model composition (VMC) method is employed to generate multiple environmental models for our previously proposed parallel combined gaussian mixture model (PCGMM) based feature compensation algorithm. The mixture sharing technique is integrated to reduce computational expenses, caused by employing the variational models. Experimental results prove that the proposed method is considerably more effective at increasing speech recognition performance in time-varying background noise conditions, with +31.31%, +10.65%, and +20.54% average relative improvements in word error rate for speech babble, background music, and real-life in-vehicle noise conditions respectively, compared to the original basic PCGMM method.

Feature compensation in the cepstral domain employing model combination

In this paper, we present an effective cepstral feature compensation scheme which leverages knowledge of the speech model in order to achieve robust speech recognition. In the proposed scheme, the requirement for a prior noisy speech database in off-line training is eliminated by employing parallel model combination for the noise-corrupted speech model. Gaussian mixture models of clean speech and noise are used for the model combination. The adaptation of the noisy speech model is possible only by updating the noise model. This method has the advantage of reduced computational expenses and improved accuracy for model estimation since it is applied in the cepstral domain. In order to cope with time-varying background noise, a novel interpolation method of multiple models is employed. By sequentially calculating the posterior probability of each environmental model, the compensation procedure can be applied on a frame-by-frame basis. In order to reduce the computational expense due to the multiple-model method, a technique of sharing similar Gaussian components is proposed. Acoustically similar components across an inventory of environmental models are selected by the proposed sub-optimal algorithm which employs the Kullback–Leibler similarity distance. The combined hybrid model, which consists of the selected Gaussian components is used for noisy speech model sharing. The performance is examined using Aurora2 and speech data for an in-vehicle environment. The proposed feature compensation algorithm is compared with standard methods in the field (e.g., CMN, spectral subtraction, RATZ). The experimental results demonstrate that the proposed feature compensation schemes are very effective in realizing robust speech recognition in adverse noisy environments. The proposed model combination-based feature compensation method is superior to existing model-based feature compensation methods. Of particular interest is that the proposed method shows up to an 11.59% relative WER reduction compared to the ETSI AFE front-end method. The multi-model approach is effective at coping with changing noise conditions for input speech, producing comparable performance to the matched model condition. Applying the mixture sharing method brings a significant reduction in computational overhead, while maintaining recognition performance at a reasonable level with near real-time operation.

Feature Compensation Employing Multiple Environmental Models for Robust In-Vehicle Speech Recognition

An effective feature compensation method is developed for reliable speech recognition in real-life in-vehicle environments. The CU-Move corpus, used for evaluation, contains a range of speech and noise signals collected for a number of speakers under actual driving conditions. PCGMM-based feature compensation, considered in this paper, utilizes parallel model combination to generate noise-corrupted speech model by combining clean speech and the noise model. In order to address unknown time-varying background noise, an interpolation method of multiple environmental models is employed. To alleviate computational expenses due to multiple models, an Environment Transition Model is employed, which is motivated from Noise Language Model used in Environmental Sniffing. An environment dependent scheme of mixture sharing technique is proposed and shown to be more effective in reducing the computational complexity. A smaller environmental model set is determined by the environment transition model for mixture sharing. The proposed scheme is evaluated on the connected single digits portion of the CU-Move database using the Aurora2 evaluation toolkit. Experimental results indicate that our feature compensation method is effective for improving speech recognition in real-life in-vehicle conditions. A reduction of 73.10% of the computational requirements was obtained by employing the environment dependent mixture sharing scheme with only a slight change in recognition performance. This demonstrates that the proposed method is effective in maintaining the distinctive characteristics among the different environmental models, even when selecting a large number of Gaussian components for mixture sharing.

Prediction of Intelligibility of Non-linearly Processed Speech.

Speech in various noise backgrounds was processed through four different non-linear devices and the intelligibility of the processed signals was predicted from the Speech Transmission Index (STI). A novel calculation method was applied in order to avoid artifacts. Running speech was used as input signal and STI was calculated from the envelopes of the squared, noise-free speech signal and of the processed, squared, noisy signal in 23 critical bands. In situations with linearly processed speech and a stationary background noise, this calculation method gives results identical with the procedure described by Steeneken & Houtgast (6). However, in a number of situations with non-linearly processed speech, or a time varying background noise level, the calculation method used here is preferable. The predictions were evaluated in a psycho-acoustic listening test and the predictions agreed well with the listening test results.