In this paper, we propose a low-complexity and accurate noise suppression based on an a priori SNR (Speech to Noise Ratio) model for greater robustness w.r.t. short-term noise-fluctuation. The a priori SNR, the ratio of speech spectra and noise spectra in the spectral domain, represents the difference between speech features and noise features in the feature domain, including the mel-cepstral domain and the logarithmic power spectral domain. This is because logarithmic operations are used for domain conversions. Therefore, an a priori SNR model can easily be expressed in terms of the difference between the speech model and the noise model, which are modeled by the Gaussian mixture models, and it can be generated with low computational cost. By using a priori SNRs accurately estimated on the basis of an a priori SNR model, it is possible to calculate accurate coefficients of noise suppression filters taking into account the variance of noise, without serious increase in computational cost over that of a conventional model-based Wiener filter (MBW). We have conducted in-car speech recognition evaluation using the CENSREC-2 database, and a comparison of the proposed method with a conventional MBW showed that the recognition error rate for all noise environments was reduced by 9%, and that, notably, that for audio-noise environments was reduced by 11%. We show that the proposed method can be processed with low levels of computational and memory resources through implementation on a digital signal processor.

Hum noise such as power line interference is one of the critical problems in the biomedical signal acquisition. Various techniques have been proposed to suppress power line interference. However, some of the techniques require more components and power consumption. The notch depth in the conventional N-path notch filter circuits needs a higher number of paths and switches off-resistance. It makes the conventional N-path notch filter less of efficiency to suppress hum noise. This work proposed the new N-path notch filter to hum noise suppression in biomedical signal acquisition. The new N-path notch filter achieved notch depth above 40dB with sampling frequency 50Hz and 60Hz. Although the proposed circuits use less number of path and switches off-resistance. The proposed circuit has been verified using artificial ECG signal contaminated by hum noise at frequency 50Hz and 60Hz. The output of N-path notch filter achieved a noise-free signal even if the sampling frequency changes.

Saliency detection for videos has been paid great attention and extensively studied in recent years. However, various visual scene with complicated motions leads to noticeable background noise and non-uniformly highlighting the foreground objects. In this paper, we proposed a video saliency detection model using spatio-temporal cues. In spatial domain, the location of foreground region is utilized as spatial cue to constrain the accumulation of contrast for background regions. In temporal domain, the spatial distribution of motion-similar regions is adopted as temporal cue to further suppress the background noise. Moreover, a backward matching based temporal prediction method is developed to adjust the temporal saliency according to its corresponding prediction from the previous frame, thus enforcing the consistency along time axis. The performance evaluation on several popular benchmark data sets validates that our approach outperforms existing state-of-the-arts.

We propose an impact and high-pitch noise-suppression method based on spectral entropy. Spectral entropy takes a large value for flat spectral amplitude and a small value for spectra with several lines. We model the impact noise as a flat spectral signal and its damped oscillation as a high-pitch periodic signal consisting of spectra with several lines. We discriminate between the current noise situations by using spectral entropy and adaptively change the noise-suppression parameters used in a zero phase-based impact-noise-suppression method. Simulation results show that the proposed method can improve the perceptual evaluation of the speech quality and speech-recognition rate compared to conventional methods.

The expressions for the reset noise in capacitive-transimpedance-amplifier (CTIA) readout circuits are theoretically derived and confirmed experimentally. The contributions to the reset noise from the thermal current and amplifier noise are considered. The thermal reset noise is found to depend only on the feedback capacitance among the circuit parameters.

Impulsive noise interference is a significant problem for the Integrated Services Digital Broadcasting for Terrestrial (ISDB-T) receivers due to its effect on the orthogonal frequency division multiplexing (OFDM) signal. In this paper, an adaptive scheme to suppress the effect of impulsive noise is proposed. The impact of impulsive noise can be detected by using the guard band in the frequency domain; furthermore the position information of the impulsive noise, including burst duration, instantaneous power and arrived time, can be estimated as well. Then a time-domain window function with adaptive parameters, which are decided in terms of the estimated information of the impulsive noise and the carrier-to-noise ratio (CNR), is employed to suppress the impulsive interference. Simulation results confirm the validity of the proposed scheme, which improved the bit error rate (BER) performance for the ISDB-T receivers in both AWGN channel and Rayleigh fading channel.

To reduce the error of channel estimation caused by noise, a novel noise suppression method based on the degree of confidence is proposed in this paper. The false alarm and false dismissal probabilities, corresponding to noise being taken as part of channel impulse response (CIR) and part of the CIR being mis-detected as noise, respectively, are also investigated. A false alarm reduction method is therefore presented to reduce the false alarms in the estimated CIR while the mis-detection ratio still remains low. Simulation results show the effectiveness of the proposed method.

Numerous noise suppression methods for speech signals have been developed up to now. In this paper, a new method to suppress noise in speech signals is proposed, which requires a single microphone only and doesn't need any priori-information on both noise spectrum and pitch. It works in the presence of noise with high amplitude and unknown direction of arrival. More specifically, an adaptive noise suppression algorithm applicable to real-life speech recognition is proposed without assuming the Gaussian white noise, which performs effectively even though the noise statistics and the fluctuation form of speech signal are unknown. The effectiveness of the proposed method is confirmed by applying it to real speech signals contaminated by noises.

An area efficiency hybrid decoupling scheme is proposed to suppress the charge pump noise during F-N tunneling program in non-volatile memory (NVM). The proposed scheme is focused on suppressing the average noise power in frequency domain aspect, which is more suitable for the program error reduction in NVMs. The concept of active capacitor is utilized. Feed forward effect of the amplifier is firstly considered in the impedance analysis, and a trade-off relation between in-band and out-band frequency noise decoupling performance is shown. A fast optimization based on average noise power is made to achieve minimum error in the F-N tunneling program. Simulation results show very stable output voltage in different load conditions, the average ripple voltage is 17 mV with up to 20 dB noise-suppression-ratio (NSR), and the F-N tunneling program error is less than 5 mV for a 800 µs program pulse. A test chip is also fabricated in 0.18 µm technology. The area overhead of the proposed scheme is 2%. The measurement results show 24.4 mV average ripple voltage compared to 72.3 mV of the conventional one with the same decoupling capacitance size, while the noise power suppression achieves 15.4 dB.

A novel approach is proposed for miniaturizing the unit cell size of electromagnetic bandgap (EBG) structures that suppress power plane noise. In this approach, open stubs are introduced into the shunt circuits of these EBG structures. Since the stub length determines the resonant frequencies of the shunt circuit, the proposed structures can maintain the bandgaps at lower frequencies without increasing the unit cell size. The bandgap frequencies were estimated by dispersion analysis based on the Bloch theorem and full-wave simulations. Sample boards of the proposed EBG structures were fabricated with a unit cell size of 2.1 mm. Highly suppressed noise propagation over the estimated frequency range of 1.9-3.6 GHz including the 2.4-GHz wireless-LAN band was experimentally demonstrated.

This paper proposes a low complexity noise suppressor with hybrid filterbanks and adaptive time-frequency tiling. An analysis hybrid filterbank provides efficient transformation by further decomposing low-frequency bins after a coarse transformation with a short frame size. A synthesis hybrid filterbank also reduces computational complexity in a similar fashion to the analysis hybrid filterbank. Adaptive time-frequency tiling reduces the number of spectral gain calculations. It adaptively generates tiling information in the time-frequency plane based on the signal characteristics. The average number of instructions on a typical DSP chip has been reduced by 30% to 7.5 MIPS in case of mono signals sampled at 44.1 kHz. A Subjective test result shows that the sound quality of the proposed method is comparable to that of the conventional one.

This paper proposes an enhanced method for estimating the a priori Signal-to-Disturbance Ratio (SDR) to be employed in the Acoustic Echo and Noise Suppression (AENS) system for full-duplex hands-free communications. The proposed a priori SDR estimation technique is modified based upon the Two-Step Noise Reduction (TSNR) algorithm to suppress the background noise while preserving speech spectral components. In addition, a practical approach to determine accurately the Echo Spectrum Variance (ESV) is presented based upon the linear relationship assumption between the power spectrum of far-end speech and acoustic echo signals. The ESV estimation technique is then employed to alleviate the acoustic echo problem. The performance of the AENS system that employs these two proposed estimation techniques is evaluated through the Echo Attenuation (EA), Noise Attenuation (NA), and two speech distortion measures. Simulation results based upon real speech signals guarantee that our improved AENS system is able to mitigate efficiently the problem of acoustic echo and background noise, while preserving the speech quality and speech intelligibility.

The present paper describes quality enhancement of speech corrupted by an additive background noise in a single-channel system. The proposed approach is based on the introduction of a perceptual criterion using a frequency-weighting filter in a subtractive-type enhancement process. Although this subtractive-type method is very attractive because of its simplicity, it produces an unnatural and unpleasant residual noise. Thus, it is difficult to select fixed optimized parameters for all speech and noise conditions. A new and effective algorithm is thus developed based on the masking properties of the human ear. This newly developed algorithm allows for an automatic adaptation in the time and frequency of the enhancement system and determines a suitable noise estimate according to the frequency of the noisy input speech. Experimental results demonstrate that the proposed approach can efficiently remove additive noise related to various kinds of noise corruption.

We introduce the Rank M-type L (RM L)-filter to remove impulsive and speckle noise from corrupted images by means of use of DSP TMS320C6701.

To achieve high recognition performance for a wide variety of noise and for a wide range of signal-to-noise ratio, this paper presents methods for integration of four noise reduction algorithms: spectral subtraction with smoothing of time direction, temporal domain SVD-based speech enhancement, GMM-based speech estimation and KLT-based comb-filtering. In this paper, we proposed two types of combination methods of noise suppression algorithms: selection of front-end processor and combination of results from multiple recognition processes. Recognition results on the CENSREC-1 task showed the effectiveness of our proposed methods.

We propose a noise suppression method based on multi-model compositions and multi-pass search. In real environments, input speech for speech recognition includes many kinds of noise signals. To obtain good recognized candidates, suppressing many kinds of noise signals at once and finding target speech is important. Before noise suppression, to find speech and noise label sequences, we introduce multi-pass search with acoustic models including many kinds of noise models and their compositions, their n-gram models, and their lexicon. Noise suppression is frame-synchronously performed using the multiple models selected by recognized label sequences with time alignments. We evaluated this method using the E-Nightingale task, which contains voice memoranda spoken by nurses during actual work at hospitals. The proposed method obtained higher performance than the conventional method.

We propose a noise suppression algorithm with the Kalman filter theory. The algorithm aims to achieve robust noise suppression for the additive white and colored disturbance from the canonical state space models with (i) a state equation composed of the speech signal and (ii) an observation equation composed of the speech signal and additive noise. The remarkable features of the proposed algorithm are (1) applied to adaptive white and colored noises where the additive colored noise uses babble noise, (2) realization of high performance noise suppression without sacrificing high quality of the speech signal despite simple noise suppression using only the Kalman filter algorithm, while many conventional methods based on the Kalman filter theory usually perform the noise suppression using the parameter estimation algorithm of AR (auto-regressive) system and the Kalman filter algorithm. We show the effectiveness of the proposed method, which utilizes the Kalman filter theory for the proposed canonical state space model with the colored driving source, using numerical results and subjective evaluation results.

A method for extracting a sound signal from a particular area that is surrounded by multiple ambient noise sources is proposed. This method performs several fixed beamformings on a pair of small microphone arrays separated from each other to estimate the signal and noise power spectra. Noise suppression is achieved by applying spectrum emphasis to the output of fixed beamforming in the frequency domain, which is derived from the estimated power spectra. In experiments performed in a room with reverberation, this method succeeded in suppressing the ambient noise, giving an SNR improvement of more than 10 dB, which is better than the performance of the conventional fixed and adaptive beamforming methods using a large-aperture microphone array. We also confirmed that this method keeps its performance even if the noise source location changes continuously or abruptly.

A robust microphone array for speech enhancement and noise suppression is studied in this paper. To overcome target signal cancellation problem of conventional beamformer caused by array imperfections or reverberation effects of acoustic enclosure, the proposed microphone array adopts an arbitrary model of channel transfer function (TF) relating microphone and speech source. Since the estimation of channel TF itself is often intractable, herein, transfer function ratio (TFR) is estimated instead and used to form a suboptimal beamformer. A robust TFR estimation method is proposed based on signal subspace analysis technique against stationary or slowly varying noise. Experiments using simulated signal and actual signal recorded in a real room illustrate that the proposed method has high performance in adverse environment.

The partial projection filter gives optimal signal restoration in the presence of both the signal space and the observation space noises. In this paper, the filter has been characterized from the point of view of its signal restoration and noise suppression capabilities. The filter is shown to suppress the noise component in the restored signal while retaining the signal component, thus maximizing the signal-to-noise ratio. Further, a digital implementation of the filter is presented in matrix form in contrast to its original operator based derivation, for practical applications.