Showing papers on "Noise published in 2000"

Stress and open-office noise.

Abstract: Forty female clerical workers were randomly assigned to a control condition or to 3-hr exposure to low-intensity noise designed to simulate typical open-office noise levels. The simulated open-office noise elevated workers' urinary epinephrine levels, but not their norepinephrine or cortisol levels, and it produced behavioral aftereffects (fewer attempts at unsolvable puzzles) indicative of motivational deficits. Participants were also less likely to make ergonomic, postural adjustments in their computer work station while working under noisy, relative to quiet, conditions. Postural invariance is a risk factor for musculoskeletal disorder. Although participants in the noise condition perceived their work setting as significantly noisier than those working under quiet conditions did, the groups did not differ in perceived stress. Potential health consequences of long-term exposure to low-intensity office noise are discussed.

Interior rearview mirror sound processing system

Abstract: A digital sound processor is provided to enhance the vocal to non-vocal noise ratio of the signal processed by a vehicle audio system such as a cellular telephone, emergency communication device, or other audio device. Optionally, an indicator is provided for use with the vehicular audio system in order to provide a user of the audio system with a status signal relating to a reception quality of a vocal signal from the user. The microphone of the audio system may be mounted within an accessory module, which may be mounted to an interior surface of a vehicle windshield. The accessory module provides a fixed orientation of the microphone and is easily installed to the vehicle as it is manufactured or as an aftermarket device. The indicator may be mounted at the accessory module or elsewhere at the mirror assembly.

Quantile based noise estimation for spectral subtraction and Wiener filtering

Abstract: Elimination of additive noise from a speech signal is a fundamental problem in audio signal processing. In this paper we restrict our considerations to the case where only a single microphone recording of the noisy signal is available. The algorithms which we investigate proceed in two steps. First, the noise power spectrum is estimated. A method based on temporal quantiles in the power spectral domain is proposed and compared with pause detection and recursive averaging. The second step is to eliminate the estimated noise from the observed signal by spectral subtraction or Wiener filtering. The database used in the experiments comprises 6034 utterances of German digits and digit strings by 770 speakers in 10 different cars. Without noise reduction, we obtain an error rate of 11.7%. Quantile based noise estimation and Wiener filtering reduce the error rate to 8.6%. Similar improvements are achieved in an experiment with artificial, non-stationary noise.

Can detectability be improved by adding noise

Abstract: It is shown that under certain conditions the performance of a suboptimal detector may be improved by adding noise to the received data. The reasons for this counterintuitive result are explained and a computer simulation example given.

System and method for dual microphone signal noise reduction using spectral subtraction

Abstract: Speech enhancement is provided in dual microphone noise reduction systems by including spectral subtraction algorithms using linear convolution, causal filtering and/or spectrum dependent exponential averaging of the spectral subtraction gain function. According to exemplary embodiments, when a far-mouth microphone is used in conjunction with a near-mouth microphone, it is possible to handle non-stationary background noise as long as the noise spectrum can continuously be estimated from a single block of input samples. The far-mouth microphone, in addition to picking up the background noise, also picks up the speaker's voice, albeit at a lower level than the near-mouth microphone. To enhance the noise estimate, a spectral subtraction stage is used to suppress the speech in the far-mouth microphone signal. To be able to enhance the noise estimate, a rough speech estimate is formed with another spectral subtraction stage from the near-mouth signal. Finally, a third spectral subtraction function is used to enhance the near-mouth signal by suppressing the background noise using the enhanced background noise estimate.

System and method for locating program boundaries and commercial boundaries using audio categories

Abstract: For use in a video signal processor, there is disclosed a system and method for locating program boundaries and commercial boundaries using audio categories. The system comprises an audio classifier controller that obtains information concerning the audio categories of the segments of an audio signal. Audio categories include such categories as silence, music, noise and speech. The audio classifier controller determines the rates of change of the audio categories. The audio classifier controller then compares each rate of change of the audio categories with a threshold value to locate the boundaries of the programs and commercials. The audio classifier controller is also capable of classifying at least one feature of an audio category change rate using a multifeature classifier to locate the boundaries of the programs and commercials.

Extending Spectral Modeling Synthesis with Transient Modeling Synthesis

Abstract: Sinusoidal modeling has enjoyed a rich history in both speech and music applications, including sound transformations, compression, denoising, and auditory scene analysis. For such applications, the underlying signal model must efficiently capture salient audio features (Goodwin 1998). In this article, we present an accurate, efficient, and flexible three-part model for audio signals consisting of sines, transients, and noise by extending spectral modeling synthesis (SMS) (Serra and Smith 1990) with an explicit flexible transient model called transient-modeling synthesis (TMS). The sinusoidal transformation system (STS) (McAulay and Quatieri 1986) and SMS find the slowly varying sinusoidal components in a signal using spectral-peak-picking algorithms. Subtracting the synthesized sinusoids from the original signal creates a residual consisting of transients and noise (Serra 1989; George and Smith 1992). However, sinusoids do not model this residual well. Although it is possible to model transients and noise by a sum of sinusoidal signals (as with the Fourier transform), it is neither efficient, because transient and noisy signals require many sinusoids for their description, nor meaningful, because transients are short-lived signals, while the sinusoidal model uses sinusoids that are active on a much larger time scale. In the STS system (generally applied to speech), the transient + noise residual is often masked sufficiently to be ignored (McAulay and Quatieri 1986). In music applications, this residual is often important to the integrity of the signal. The SMS system extends the sinusoidal model by explicitly modeling the residual as slowly filtered white noise. Although this technique has been very successful, transients do not fit well into this model, because transients modeled as filtered noise lose sharpness in their attack and tend to sound dull. Because transients are

Robust and efficient digital audio watermarking using audio content analysis

Abstract: Digital audio watermarking embeds inaudible information into digital audio data for the purposes of copyright protection, ownership verification, covert communication, and/or auxiliary data carrying. In this paper, we first describe the desirable characteristics of digital audio watermarks. Previous work on audio watermarking, which has primarily focused on the inaudibility of the embedded watermark and its robustness against attacks such as compression and noise, is then reviewed. In this research, special attention is paid to the synchronization attack caused by casual audio editing or malicious random cropping, which is a low-cost yet effective attack to watermarking algorithms developed before. A digital audio watermarking scheme of low complexity is proposed in this research as an effective way to deter users from misusing or illegally distributing audio data. The proposed scheme is based on audio content analysis using the wavelet filterbank while the watermark is embedded in the Fourier transform domain. A blind watermark detection technique is developed to identify the embedded watermark under various types of attacks.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Ausio-visual Segmentation and The Cocktail Party Effect

Abstract: Audio-based interfaces usually suffer when noise or other acoustic sources are present in the environment. For robust audio recognition, a single source must first be isolated. Existing solutions to this problem generally require special microphone configurations, and often assume prior knowledge of the spurious sources. We have developed new algorithms for segmenting streams of audio-visual information into their constituent sources by exploiting the mutual information present between audio and visual tracks. Automatic face recognition and image motion analysis methods are used to generate visual features for a particular user; empirically these features have high mutual information with audio recorded from that user. We show how audio utterances from several speakers recorded with a single microphone can be separated into constituent streams; we also show how the method can help reduce the effect of noise in automatic speech recognition.

Towards a perceptually optimal spectral amplitude estimator for audio signal enhancement

Abstract: We present a statistical model-based approach to signal enhancement in the case of additive broadband noise. Because broadband noise is localised in neither time nor frequency, its removal is one of the most pervasive and difficult signal enhancement tasks. In order to improve perceived signal quality, we take advantage of human perception and define a best estimate of the original signal in terms of a cost function incorporating perceptual optimality criteria. We derive the resultant signal estimator and implement it in a short-time spectral attenuation framework.

Copy-protected digital audio compact disc, and method and system for producing same

Abstract: A method for producing a copy-protected audio compact disc containing a plurality of symbols within error-correction codewords, representing audio data samples of an audio signal is provided. The method includes the steps of selecting at least one audio data sample of the audio signal (1415); locating the data symbols representing audio data sample (1420); overwriting symbols with erroneous symbols (1440); locating the error-correction codewords associated with the data symbols and disabling the error-correction of said error-correction codewords (1455).

Impulse noise reducer detecting impulse noise from an audio signal

Abstract: An impulse noise reducer detects impulse noise in an audio signal by detecting and smoothing the high-frequency amplitude of the audio signal, attenuating the non-smoothed amplitude according to the smoothed amplitude, and comparing the attenuated amplitude with a threshold. Impulse noise is discriminated from high-frequency audio components because the latter tend to occur in longer-lasting bursts and are therefore attenuated more strongly. The impulse noise reducer is simplified because it does not have to perform intermediate-frequency signal processing, and its sensitivity is not affected by adjacent-channel signals because these signals are substantially absent from the audio signal. The impulse noise reducer can be implemented by digital signal processing, and is suitable for use in a medium-wave AM audio broadcast receiver.

Improved noise reduction for hands-free car phones utilizing information on vehicle and engine speeds

Abstract: In this paper we present an improved method for the spectral estimation of car noise in order to enhance the performance of noise reduction systems. An algorithm is developed that allows us to track changes in the noise spectrum during speech activity. For this tracking, we use the knowledge of the speed of the car and the revolutions of the engine. The paper starts with a detailed analysis of the car noise. The proposed algorithm based on this analysis first removes the harmonic components of the engine noise by selective filtering in time. The remaining wind and tyre noise is predicted during speech activity, based on the last available estimate and the vehicle speed.

Adaptive noise cancellation in a multimicrophone system for distortion product otoacoustic emission acquisition

Abstract: This study focuses on adaptive noise cancellation (ANC) techniques for the acquisition of distortion product otoacoustic emissions (DPOAEs). Otoacoustic emissions (OAEs) are very low level sounds produced by the outer hair cells of normal cochleas, spontaneously or in response to sound stimulation as a byproduct of a frequency and threshold sensitivity increasing mechanism. Current OAE recording systems rely on test probe noise attenuation and synchronous ensemble averaging for increasing signal-to-noise ratios (SNRs). The efficiency of an ANC algorithm for noise suppression was investigated using three microphones: one placed in the test ear, one in the nontest ear for internal noise reference; one near the subject's head for external noise reference. The system proposed was tested with simulations, off-line averaging and real-time implementation of the ANC algorithm. Simulation results showed that the technique had a potential noise reduction capability of 24 dB for complex multifrequency noise signals. Off-line results mere positive, with a mean SNR improvement of 4.9 dB. Real-time results indicated that the use of an ANC algorithm in combination with standard averaging methods can reduce noise levels by as much as 10 dB beyond that obtained with standard noise reduction methods and probe attenuation alone.

Background noise adaptable speaker phone for use in a mobile communication device

Abstract: A mobile communication device (100) determines a background noise level at a microphone (104). If, after determining the noise floor, the input signal exceeds a threshold, the mobile communication device transmits the input signal. The threshold is established such that if it is exceeded, it is likely that voice energy is being received at the microphone, and the threshold, in a first embodiment, is increased as the background noise level increases. In a second embodiment the threshold is determined as the difference between the input signal and the background noise level. As background noise increases, the threshold actually decreases.

System for noise suppression, transceiver and method for noise suppression

Abstract: A system with an accompanying method is provided to improve the signal-to-noise ratio (SNR) of noisy speech by suppressing acoustic background noise. The background noise consists of narrow band noise from rotating machine, audio signals from stereo-loudspeakers of audio entertainment device, and other ambient noise. In this system/apparatus, a microphone senses the speech intermingled with the background noise, and another microphone senses the noisy background. In addition, a measurement sensor is used to measure RPM (revolutions-per-minutes) of the rotating machine and two wires are used to acquire audio signals from the stereo-loudspeakers of the audio entertainment device. Furthermore, to provide better suppression performance for the acoustic audio signals, the characteristics of these loudspeakers are used to compensate for the distortion caused by the loudspeakers. Adaptive comb filters and adaptive FIR filters are applied to estimate the ambient noise and suppress the background noise. After processing, the system outputs the enhanced speech signal with higher SNR.

Recognition of sentences in noise by normal-hearing listeners using simulations of speak-type cochlear implant signal processors.

Abstract: To assess whether more channels are needed to understand speech in noise than in quiet, we processed speech in a manner similar to that of spectral peak-like cochlear implant processors and presented it at a +2-dB signal-to-noise ratio to normal-hearing listeners for identification. The number of analysis filters varied from 8 to 16, and the number of maximum channel amplitudes selected in each cycle varied from 2 to 16. The results show that more channels are needed to understand speech in noise than in quiet, and that high levels of speech understanding can be achieved with 12 channels. Selecting more than 12 channel amplitudes out of 16 channels did not yield significant improvements in recognition performance.

Systems and methods for improving power spectral estimation of speech signals

Abstract: A system determines a power spectral density associated with an audio signal that includes a speech signal and/or a noise signal. The system updates an autocorrelation function of the audio signal from samples in the audio signal, estimates an autocorrelation function of the speech signal from the updated autocorrelation function of the audio signal, and calculates a power spectral density of the speech signal using the estimated autocorrelation function. The system then determines the power spectral density of the audio signal from the calculated power spectral density of the speech signal.

Low frequency noise and annoyance in classroom

Abstract: The most common method for noise assessment is the A-weighted sound pressure level. The question has been raised as to whether the frequency weighting with an A-filter gives a correct result when assessing the annoyance response to noise containing strong low frequency noise (LFN) components. One method suggested to identify LFN is the dB(C) - dB(A) difference. The aims of this study are to investigate if background noise in Swedish elementary schools is to be considered as LFN, further to test the hypothesis that students exposed to audible LFN at high levels are more annoyed than students exposed to LFN at lower levels. The results indicate that the noise in 16 out of 22 classrooms should be considered as LFN. The analysis did not show any difference in rated annoyance between students exposed to high LFN levels and students exposed to low LFN levels.

Digital filter design method and apparatus

Abstract: A digital filter design apparatus for noise suppression by spectral subtraction includes a first spectrum estimator for determining a high frequency resolution noisy speech power spectral density estimate from a noisy speech signal block A second spectrum estimator determines a high frequency resolution background noise power spectral density estimate from a background noise signal block Averaging units form a piece-wise constant noisy speech power spectral density estimate and a piece-wise constant background noise power spectral density estimate These averaging units are controlled by devices for adapting the length of individual segments to the shape of the high frequency-resolution noisy speech power spectral density estimate and for using the same segmentation in both piecewise constant estimates A piece-wise constant digital filter transfer function is determined using spectral subtraction-based on the piece-wise constant noisy speech power spectral density estimate and the piece-wise constant background noise power spectral density estimate

An improved secondary path modeling method for active noise control systems

Abstract: A method to improve the performance of the secondary path modeling for active noise control systems is proposed in this letter. The proposed method can further reduce the distortion introduced by the primary noise and the canceling signal to the secondary path modeling. Computer simulation shows that the proposed method can provide faster convergence and higher modeling accuracy.

Digital filter design method and apparatus for noise suppression by spectral substraction

Abstract: A digital filter design apparatus for noise suppression by spectral subtraction includes a first spectrum estimator (12) for determining a high frequency-resolution noisy speech power spectral density estimate from a noisy speech signal block. A second spectrum estimator (24) determines a high frequency-resolution background noise power spectral density estimate from a background noise signal block. Averaging units (20, 26) form a piece-wise constant noisy speech power spectral density estimate and a piece-wise constant background noise power spectral density estimate. These averaging units are controlled by means (14, 16, 18) for adapting the length of individual segments to the shape of the high frequency-resolution noisy speech power spectral density estimate and for using the same segmentation in both piece-wise constant estimates. Means (28) determine a piece-wise constant digital filter transfer function using spectral subtraction based on the piece-wise constant noisy speech power spectral density estimate and the piece-wise constant background noise power spectral density estimate.

Integrated noise reduction and echo cancellation for IS-136 systems

Abstract: In this paper, we present a combined speech quality enhancement solution for IS-136 systems. Since echo and background noise are the two major factors that adversely affect the speech quality in most transmission systems, our solution consists of echo cancellation and noise reduction elements. These are used in conjunction with the IS-641 (enhanced fullrate standard for IS-136 systems) coder to form an integrated speech-processing unit. The echo canceller uses the normalized least mean square (NLMS) method. Because of the existence of high levels of background noise, variable step-size techniques are employed. The noise reduction consists of a single microphone method and it uses a spectral amplitude enhancement gain function with minimal spectral distortion. The noise reduction is utilized in the pre-compression configuration, and it comes after the echo canceller on the send path reducing the residual echo as well as noise.

Noise attenuation of hearing protectors against heavy weapon noise.

Abstract: This study evaluated the noise attenuation of earplugs and earmuffs or their combined use against heavy weapon noise in field conditions for military personnel. The noise attenuation was measured with a miniature microphone inserted into the ear canal. The subjects (13) were tested against pink noise and against the noise of explosions and bazooka, mortar, cannon, and howitzer. The attenuation (insertion loss) was 16 to 23 dB for earplugs, 10 to 20 dB for earmuffs, and 24 to 34 dB for the combined use of plugs and muffs. The transfer function of an open ear was 5 to 7 dB when measured as the C-weighted peak level. The combined use of earplugs and earmuffs gave smaller attenuation values than expected. If the limit for the C-weighted peak level is 140 dB for unprotected ears, then protection against low-frequency noise is provided for up to 156 dB by earplugs, up to 150 dB by earmuffs, and up to 165 dB by the combined use of plugs and muffs.

Effects of noise and music on EEG power spectrum

Abstract: Objective. To observe the effect of noise and music on EEG power spectrum. Method. 12 healthy male pilots aged 30 +/- 0.58 years served as the subjects. Dynamic EEG from 16 regions was recorded during quiet, under noise or when listening to music using Oxford MR95 Holter recorder. Changes of EEG power spectrum of delta, theta, alpha1, alpha2, beta1 and beta2, frequency components in 16 regions were analyzed. Result. The total alpha1 power was significantly decreased, while the total theta power was significantly increased when listening to music; It implies that the interhemispheric transmission of information in the frontotemporal areas might be involved. Conclusion. The changes of the EEG power spectrum were closely related to man's emotions; relaxation was associated with music; Individual difference exists in the influence of sound on EEG.

Device and method for reading image

Abstract: PROBLEM TO BE SOLVED: To provide an image reader capable of precisely detecting and removing a stripe-shaped noise caused by dust sticking to a reading part, even when there is the difference of reading density between reading positions SOLUTION: A CCD 1 reads an original at respective reading positions, and image data A and B at the respective reading positions can be obtained from an output delay circuit 18 and a shading correction circuit 17 While using a threshold corrected by the average density difference of the respective image data A and B, a stripe detecting circuit 19 discriminates a difference A-B of the respective image data and outputs black stripe detection data according to the discriminated result A stripe removing circuit 20 ordinarily outputs the image data A, but when the black stripe detection data are outputted, in place of the image data A, the image data B corrected by the average density difference are outputted

Single-channel noise reduction with pitch-adaptive post-filtering

Abstract: Single-channel noise reduction for speech enhancement is often applied in cellular and in hands-free telephones. For speech distortions to be minimal, single-channel systems based on spectral subtraction cannot entirely eliminate environmental noise. A relatively high spectral noise floor has to remain in the speech signal. For further reduction of annoying noise components, pitch-adaptive post-filtering is investigated in this paper. The basic idea is to attenuate during voiced parts of speech the spectral valleys between the pitch frequency and the harmonics. Simulation results for a spectral-subtraction scheme in conjunction with a pitch-adaptive post-filter are given. A frequency-domain subband filtering scheme is shown to be capable of enhancing speech signals disturbed by car noise.

Audio component system

Abstract: In a system which comprises a plurality of audio components 2 to 5 connected to an amplifier unit 1 and wherein an audio signal from one component selected by a selector 17 is fed to a speaker 6 for the production of sound, a control circuit 15 of the amplifier unit 1 and each of control circuits 22, 32, 43, 53 of the respective components are connected to each other by a control bus 7. The amplifier unit 1 transmits a call signal to each of the components 2 to 5 and checks whether an answer signal is received therefrom in response to the call signal. When the component failing to return the answer signal is selected by the selector 17, the unit 1 turns on a muting circuit 16. This eliminates the likelihood that the speaker will produce noise when the sound volume is increased while a nonconnected component is selected.