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.

Reducing MOSFET 1/f noise and power consumption by switched biasing

Abstract: Switched biasing is proposed as a technique for reducing the 1/f noise in MOSFET's. Conventional techniques, such as chopping or correlated double sampling, reduce the effect of 1/f noise in electronic circuits, whereas the switched biasing technique reduces the 1/f noise itself. Whereas noise reduction techniques generally lead to more power consumption, switched biasing can reduce the power consumption. It exploits an intriguing physical effect: cycling a MOS transistor from strong inversion to accumulation reduces its intrinsic 1/f noise. As the 1/f noise is reduced at its physical roots, high frequency circuits, in which 1/f noise is being upconverted, can also benefit. This is demonstrated by applying switched biasing in a 0.8 /spl mu/m CMOS sawtooth oscillator. By periodically switching off the bias currents, during time intervals that they are not contributing to the circuit operation, a reduction of the 1/f noise induced phase noise by more than 8 dB is achieved, while the power consumption is also reduced by 30%.

2-D companding for noise reduction in strain imaging

Abstract: Companding is a signal preprocessing technique for improving the precision of correlation-based time delay measurements. In strain imaging, companding is applied to warp 2-D or 3-D ultrasonic echo fields to improve coherence between data acquired before and after compression. It minimizes decorrelation errors, which are the dominant source of strain image noise. The word refers to a spatially variable signal scaling that compresses and expands waveforms acquired in an ultrasonic scan plane or volume. Temporal stretching by the applied strain is a single-scale (global), 1-D companding process that has been used successfully to reduce strain noise. This paper describes a two-scale (global and local), 2-D companding technique that is based on a sum-absolute-difference (SAD) algorithm for blood velocity estimation. Several experiments are presented that demonstrate improvements in target visibility for strain imaging. The results show that, if tissue motion can be confined to the scan plane of a linear array transducer, displacement variance can be reduced two orders of magnitude using 2-D local companding relative to temporal stretching.

ICRA noises: artificial noise signals with speech-like spectral and temporal properties for hearing instrument assessment. International Collegium for Rehabilitative Audiology.

Abstract: Current standards involving technical specification of hearing aids provide limited possibilities for assessing the influence of the spectral and temporal characteristics of the input signal, and these characteristics have a significant effect on the output signal of many recent types of hearing aids. This is particularly true of digital hearing instruments, which typically include non-linear amplification in multiple channels. Furthermore, these instruments often incorporate additional non-linear functions such as "noise reduction" and "feedback cancellation". The output signal produced by a non-linear hearing instrument relates to the characteristics of the input signal in a complex manner. Therefore, the choice of input signal significantly influences the outcome of any acoustic or psychophysical assessment of a non-linear hearing instrument. For this reason, the International Collegium for Rehabilitative Audiology (ICRA) has introduced a collection of noise signals that can be used for hearing aid testing (including real-ear measurements) and psychophysical evaluation. This paper describes the design criteria, the realisation process, and the final selection of nine test signals on a CD. Also, the spectral and temporal characteristics of these signals are documented. The ICRA noises provide a well-specified set of speech-like noises with spectra shaped according to gender and vocal effort, and with different amounts of speech modulation simulating one or more speakers. These noises can be applied as well-specified background noise in psychophysical experiments. They can also serve as test signals for the evaluation of digital hearing aids with noise reduction. It is demonstrated that the ICRA noises show the effectiveness of the noise reduction schemes. Based on these initial measurements, some initial steps are proposed to develop a standard method of technical specification of noise reduction based on the modulation characteristics. For this purpose, the sensitivity of different noise reduction schemes is compared by measurements with ICRA noises with a varying ratio between unmodulated and modulated test signals: a modulated-unmodulated ratio. It can be anticipated that this information is important to understand the differences between the different implementations of noise reduction schemes in different hearing aid models and makes.

A comparative intelligibility study of single-microphone noise reduction algorithms.

Abstract: The evaluation of intelligibility of noise reduction algorithms is reported. IEEE sentences and consonants were corrupted by four types of noise including babble, car, street and train at two signal-to-noise ratio levels (0 and 5 dB), and then processed by eight speech enhancement methods encompassing four classes of algorithms: spectral subtractive, sub-space, statistical model based and Wiener-type algorithms. The enhanced speech was presented to normal-hearing listeners for identification. With the exception of a single noise condition, no algorithm produced significant improvements in speech intelligibility. Information transmission analysis of the consonant confusion matrices indicated that no algorithm improved significantly the place feature score, significantly, which is critically important for speech recognition. The algorithms which were found in previous studies to perform the best in terms of overall quality, were not the same algorithms that performed the best in terms of speech intelligibility. The subspace algorithm, for instance, was previously found to perform the worst in terms of overall quality, but performed well in the present study in terms of preserving speech intelligibility. Overall, the analysis of consonant confusion matrices suggests that in order for noise reduction algorithms to improve speech intelligibility, they need to improve the place and manner feature scores.