Noise reduction

About: Noise reduction is a research topic. Over the lifetime, 25121 publications have been published within this topic receiving 300815 citations. The topic is also known as: denoising & noise removal.

Robust Speaker Recognition in Noisy Conditions

Abstract: This paper investigates the problem of speaker identification and verification in noisy conditions, assuming that speech signals are corrupted by environmental noise, but knowledge about the noise characteristics is not available. This research is motivated in part by the potential application of speaker recognition technologies on handheld devices or the Internet. While the technologies promise an additional biometric layer of security to protect the user, the practical implementation of such systems faces many challenges. One of these is environmental noise. Due to the mobile nature of such systems, the noise sources can be highly time-varying and potentially unknown. This raises the requirement for noise robustness in the absence of information about the noise. This paper describes a method that combines multicondition model training and missing-feature theory to model noise with unknown temporal-spectral characteristics. Multicondition training is conducted using simulated noisy data with limited noise variation, providing a ldquocoarserdquo compensation for the noise, and missing-feature theory is applied to refine the compensation by ignoring noise variation outside the given training conditions, thereby reducing the training and testing mismatch. This paper is focused on several issues relating to the implementation of the new model for real-world applications. These include the generation of multicondition training data to model noisy speech, the combination of different training data to optimize the recognition performance, and the reduction of the model's complexity. The new algorithm was tested using two databases with simulated and realistic noisy speech data. The first database is a redevelopment of the TIMIT database by rerecording the data in the presence of various noise types, used to test the model for speaker identification with a focus on the varieties of noise. The second database is a handheld-device database collected in realistic noisy conditions, used to further validate the model for real-world speaker verification. The new model is compared to baseline systems and is found to achieve lower error rates.

Analysis of noise reduction and dereverberation techniques based on microphone arrays with postfiltering

Abstract: In teleconferencing systems, the use of hands-free sound pick-up reduces speech quality. This is due to ambient noise, acoustic echo, and the reverberation produced by the acoustical environment. This paper sets out to present a theoretical analysis of noise reduction and dereverberation algorithms based on a microphone array combined with a Wiener postfilter. It is shown that the transfer function of the postfilter depends on the input signal-to-noise ratio (SNR) and on the noise reduction yielded by the array. The use of a directivity-controlled array instead of a conventional beam-former is proposed to improve the performance of the whole system. Examples in real room environments are provided, which confirm the theoretical results, It is observed that the postfilter gives a limited reduction of the reverberation. On the contrary, an appreciable reduction of acoustic echo and localized noise is obtained and makes the whole system highly attractive for hands-free communication systems.

Signal, Noise and Resolution in Position-Sensitive Detectors

Abstract: An analysis is presented of signal, noise and position resolution relations for some of the most interesting position-sensing methods. "Electronic cooling" of delay line terminations is introduced in order to reduce noise in the position-sensing with delay lines. A new method for terminating transmission lines and for "noiseless" damping which employs a capacitance in feedback is presented. It is shown that the position resolution for the charge division method with resistive electrodes is determined only by the electrode capacitance and not by the electrode resistance, if optimum filtering is used.

An Efficient Threshold Prediction Scheme for Wavelet Based ECG Signal Noise Reduction Using Variable Step Size Firefly Algorithm

Abstract: Electrocardiographic (ECG) signal is significant to diagnose cardiac arrhythmia among various biological signals. The accurate analysis of noisy Electrocardiographic (ECG) signal is very motivating challenge. Prior to automated analysis, the noises present in ECG signal need to be eliminated for accurate diagnosis. Many researchers have been reported different methods for denoising the ECG signal in recent years. In this paper, an optimized threshold mechanism is proposed for wavelet based medical signal noise reduction. This scheme is based on a variable step size firefly algorithm (VSSFA) in dual tree complex wavelet scheme, in which the VSSFA is utilized for threshold optimization. This approach is evaluated on several normal and abnormal ECG signals of MIT/BIH arrhythmia database, by artificially adding white Gaussian noises with variation of 5dB and 10dB. Simulation result illustrate that the proposed system is well performance in various noise level, and obtains better visual quality compare with other methods.

Wind Turbine Noise

Abstract: 1 Introduction.- 1.1 Current Situation of Wind Energy and Perspectives.- 1.2 Advantages of Wind Energy.- 1.3 Current Problems of Wind Energy.- 1.4 Road Map of the Book.- 2 Noise and its Effects.- 2.1 Sound and Noise.- 2.2 Definitions.- 2.3 Noise Regulations.- 3 Introduction to Aeroacoustics.- 3.1 Introduction.- 3.2 Definitions.- 3.3 The Linear Wave Equation.- 3.4 Elementary Solutions of the Wave Equation.- 3.5 Lighthill's Acoustic Analogy.- 3.6 The Influence of Boundaries.- 3.7 Application of Aeroacoustic Theory.- 3.8 Conclusions.- 4 Noise Mechanisms of Wind Turbines.- 4.1 Classification of Noise Mechanisms.- 4.2 Low-Frequency Noise.- 4.3 Inflow-Turbulence Noise.- 4.4 Airfoil Self-Noise.- 4.5 Summary.- 5 Noise Prediction.- 5.1 Introduction.- 5.2 Low-Frequency Noise.- 5.3 High-Frequency Noise.- 5.4 Summary.- 6 Noise Propagation.- 6.1 Introduction.- 6.2 Mechanisms.- 6.3 Prediction.- 6.4 Results.- 6.5 Summary.- 7 Measurement of Noise and Flow Field.- 7.1 Acoustic Measurement in the Wind Tunnel.- 7.2 Acoustic Measurements on Operating Turbines.- 7.3 Flow Visualization on Operating Turbines.- 8 Noise Reduction.- 8.1 Introduction.- 8.2 Reduction of Tip Speed and Angle of Attack.- 8.3 Reduction of Trailing-Edge Noise.- 8.4 Reduction of Tip Noise.- 8.5 Reduction of Inflow-Turbulence Noise.- 8.6 Reduction of Blunt-Trailing-Edge Noise.- 8.7 Conclusions.- 9 Future Work.- 10 References.- 10.1 Recommended References.- 10.2 List of References.- Annex: Description of Tip Planform Shapes.