Various Types Of Background Noise Research Articles

Active acoustic scene monitoring through spectro-temporal modulation filtering for intruder detection.

An indoor acoustic scene monitoring system using a periodic impulse signal was previously developed. Compared with the impulse signal, the chirp signal is more robust against environmental noise due to its specific spectro-temporal structure. Such specific structure makes the chirp sound easily detected using a spectro-temporal modulation filtering mechanism. In this paper, we demonstrated a system that employs a two-dimensional spectro-temporal filtering mechanism on a Fourier spectrogram to measure the total energy of the reverberations of the chirp signal as the representation of the acoustic scene. The system compares the reverberation energy difference between consecutive chirps with a predefined threshold to automatically detect the change in the acoustic scene. Simulations were conducted in real living rooms with various types of background noise. Test results demonstrated that the proposed system is much more effective than previously developed systems for detecting the acoustic scene changes due to the intruder silently walking in the rooms. In the biggest test room (18 × 9.8 × 2.5 m3) with heavy background noise, the proposed system can still yield a correct identification rate higher than 80% to the intruder walking at 7 m from the microphone without producing any false alarms.

Influence of surgical and N95 face masks on speech perception and listening effort in noise.

Daily-life conversation relies on speech perception in quiet and noise. Because of the COVID-19 pandemic, face masks have become mandatory in many situations. Acoustic attenuation of sound pressure by the mask tissue reduces speech perception ability, especially in noisy situations. Masks also can impede the process of speech comprehension by concealing the movements of the mouth, interfering with lip reading. In this prospective observational, cross-sectional study including 17 participants with normal hearing, we measured the influence of acoustic attenuation caused by medical face masks (mouth and nose protection) according to EN 14683 and of N95 masks according to EN 1149 (EN 14683) on the speech recognition threshold and listening effort in various types of background noise. Averaged over all noise signals, a surgical mask significantly reduced the speech perception threshold in noise was by 1.6 dB (95% confidence interval [CI], 1.0, 2.1) and an N95 mask reduced it significantly by 2.7 dB (95% CI, 2.2, 3.2). Use of a surgical mask did not significantly increase the 50% listening effort signal-to-noise ratio (increase of 0.58 dB; 95% CI, 0.4, 1.5), but use of an N95 mask did so significantly, by 2.2 dB (95% CI, 1.2, 3.1). In acoustic measures, mask tissue reduced amplitudes by up to 8 dB at frequencies above 1 kHz, whereas no reduction was observed below 1 kHz. We conclude that face masks reduce speech perception and increase listening effort in different noise signals. Together with additional interference because of impeded lip reading, the compound effect of face masks could have a relevant impact on daily life communication even in those with normal hearing.

Envelope and intensity based prediction of psychoacoustic masking and speech intelligibility.

Human auditory perception and speech intelligibility have been successfully described based on the two concepts of spectral masking and amplitude modulation (AM) masking. The power-spectrum model (PSM) [Patterson and Moore (1986). Frequency Selectivity in Hearing, pp. 123-177] accounts for effects of spectral masking and critical bandwidth, while the envelope power-spectrum model (EPSM) [Ewert and Dau (2000). J. Acoust. Soc. Am. 108, 1181-1196] has been successfully applied to AM masking and discrimination. Both models extract the long-term (envelope) power to calculate signal-to-noise ratios (SNR). Recently, the EPSM has been applied to speech intelligibility (SI) considering the short-term envelope SNR on various time scales (multi-resolution speech-based envelope power-spectrum model; mr-sEPSM) to account for SI in fluctuating noise [Jørgensen, Ewert, and Dau (2013). J. Acoust. Soc. Am. 134, 436-446]. Here, a generalized auditory model is suggested combining the classical PSM and the mr-sEPSM to jointly account for psychoacoustics and speech intelligibility. The model was extended to consider the local AM depth in conditions with slowly varying signal levels, and the relative role of long-term and short-term SNR was assessed. The suggested generalized power-spectrum model is shown to account for a large variety of psychoacoustic data and to predict speech intelligibility in various types of background noise.

The Effects of Noise Reduction Technologies on the Acceptance of Background Noise

Directional microphones (D-Mics) and digital noise reduction (DNR) algorithms are used in hearing aids to reduce the negative effects of background noise on performance. Directional microphones attenuate sounds arriving from anywhere other than the front of the listener while DNR attenuates sounds with physical characteristics of noise. Although both noise reduction technologies are currently available in hearing aids, it is unclear if the use of these technologies in isolation or together affects acceptance of noise and/or preference for the end user when used in various types of background noise. The purpose of the research was to determine the effects of D-Mic, DNR, or the combination of D-Mic and DNR on acceptance of noise and preference when listening in various types of background noise. An experimental study in which subjects were exposed to a repeated measures design was utilized. Thirty adult listeners with mild sloping to moderately severe sensorineural hearing loss participated (mean age 67 yr). Acceptable noise levels (ANLs) were obtained using no noise reduction technologies, D-Mic only, DNR only, and the combination of the two technologies (Combo) for three different background noises (single-talker speech, speech-shaped noise, and multitalker babble) for each listener. In addition, preference rankings of the noise reduction technologies were obtained within each background noise (1 = best, 3 = worst). ANL values were significantly better for each noise reduction technology than baseline; and benefit increased significantly from DNR to D-Mic to Combo. Listeners with higher (worse) baseline ANLs received more benefit from noise reduction technologies than listeners with lower (better) baseline ANLs. Neither ANL values nor ANL benefit values were significantly affected by background noise type; however, ANL benefit with D-Mic and Combo was similar when speech-like noise was present while ANL benefit was greatest for Combo when speech spectrum noise was present. Listeners preferred the hearing aid settings that resulted in the best ANL value. Noise reduction technologies improved ANL for each noise type, and the amount of improvement was related to the baseline ANL value. Improving an ANL with noise reduction technologies is noticeable to listeners, at least when examined in this laboratory setting, and listeners prefer noise reduction technologies that improved their ability to accept noise.

ICA-based artifact correction improves spatial localization of adaptive spatial filters in MEG

Beamformers are one of the most common inverse models currently used in the estimation of source activity from magnetoencephelography (MEG) data. They rely on a minimization of total power while constraining the gain in the voxel of interest, resulting in the suppression of background noise. Nonetheless, in cases where background noise is strong compared to the source of interest, or when many sources are present, the ability of the beamformer to detect and accurately localize weak sources is reduced. In visual paradigms, two main background sources can substantially impact an accurate estimation of weaker sources. Ocular artifacts are orders of magnitude higher than neural sources making it difficult for the beamformer to effectively suppress them. Primary visual activations also result in strong signals that can impede localization of weak sources. In this paper, we systematically evaluated how neural (visual) and non-neural (eye, heart) sources affect the localization accuracy of frontal and medial temporal sources in visual tasks. These sources are of tremendous interest in learning and memory studies as well as in clinical settings (Alzheimer's/epilepsy) and are typically difficult to localize robustly in MEG. Empirical data from two tasks - active learning and control - were used to evaluate our analysis techniques. Global field power calculations showed multiple time periods where active learning was significantly different from response selection with dominant sources converging to the eyes. Extensive leakage of eye activity into frontal and visual that evoked responses into parietal cortices was also observed. Contributions from ocular activity to the reconstructed time series were indiscernible from task-based recruitment of frontal sources in the original data. Removing artifacts (eye movements, cardiac, and muscular) by means of independent component analysis (ICA) led to a significant improvement in detection and localization of frontal and medial temporal sources. We verified our results by using simulations of sources placed in frontal and medial temporal regions with various types of background noise (eye, heart, and visual). We report that the detection and localization accuracy of frontal and medial temporal sources with beamformer techniques is highly dependent on the magnitude and location of background sources and that removing artifacts can substantially improve the beamformer's performance.

Characteristics of noise-canceling headphones to reduce the hearing hazard for MP3 users

Three pairs of headphones [standard iPod ear buds and two noise-canceling headphones (NCHs)] were chosen to investigate frequency characteristics of noise reduction, together with their attenuation effects on preferred listening levels (PLLs) in the presence of various types of background noise. Twenty-six subjects with normal hearing chose their PLLs in quiet, street noise, and subway noise using the three headphones and with the noise-canceling system on/off. Both sets of NCHs reduced noise levels at mid- and high-frequencies. Further noise reductions occurred in low frequencies with the noise canceling system switched on. In street noise, both NCHs had similar noise reduction effects. In subway noise, better noise reduction effects were found in the expensive NCH and with noise-canceling on. A two way repeated measures analysis of variance showed that both listening conditions and headphone styles were significant influencing factors on the PLLs. Subjects tended to increase their PLLs as the background noise level increased. Compared with ear buds, PLLs obtained from NCHs-on in the presence of background noise were reduced up to 4 dB. Therefore, proper selection and use of NCHs appears beneficial in reducing the risk of hearing damage caused by high music listening levels in the presence of background noise.

Robust Detection of Underwater Transient Signals Using EVRC Noise Suppression Module

Detection of transient signals is generally done by examining power and spectral variation of the received signal, but it becomes a difficult task when the background noise gets large. In this paper, we propose a robust transient detection algorithm using the EVRC noise suppression module. We define new parameters from the outputs of the EVRC noise suppression module for transient detection. Experimental results with various types of underwater transients have shown that the proposed method outperforms the conventional energy-based method and achieved performance improvement of detection rate by 7% to 15% for various types of background noise.

Human Tracking System Integrating Sound and Face Localization Using an Expectation-Maximization Algorithm in Real Environments

We have developed a human tracking system for use by robots that integrate sound and face localization. Conventional systems usually require many microphones and/or prior information to localize several sound sources. Moreover, they are incapable of coping with various types of background noise. Our system, the cross-power spectrum phase analysis of sound signals obtained with only two microphones, is used to localize the sound source without having to use prior information such as impulse response data. An expectation-maximization (EM) algorithm is used to help the system cope with several moving sound sources. The problem of distinguishing whether sounds are coming from the front or back is also solved with only two microphones by rotating the robot's head. A developed method that uses facial skin colors classified by another EM algorithm enables the system to detect faces in various poses. It can compensate for the error in the sound localization for a speaker and also identify noise signals entering from undesired directions by detecting a human face. A developed probability-based method is used to integrate the auditory and visual information in order to produce a reliable tracking path in real-time. Experiments using a robot showed that our system can localize two sounds at the same time and track a communication partner while dealing with various types of background noise.

Objective Quality Evaluation Method for Noise-Reduced Speech

We present a method for the objective quality evaluation of noise-reduced speech in wideband speech communication services, which utilize speech with a wider bandwidth (e. g., 7kHz) than the usual telephone bandwidth. Experiments indicate that the amount of residual noise and the distortion of speech and noise, which are quality factors, influence the perceived quality degradation of noise-reduced speech. From the results, we observe the principal relationships between these quality factors and perceived speech quality. On the basis of these relationships, we propose a method that quantifies each quality factor in noise-reduced speech by analyzing signals that can be measured and assesses the over-all perceived quality of noise-reduced speech using values of these quality factors. To verify the validity of the method, we perform a subjective listening test and compare subjective quality of noise-reduced speech with its estimation. In the test, we use various types of background noise and noise-reduction algorithms. The verification results indicate that the correlation between subjective quality and its objective estimation is sufficiently high regardless of the type of background noise and noise-reduction algorithm.

Sentence recognition in native- and foreign-language multi-talker background noise

Studies of speech perception in various types of background noise have shown that noise with linguistic content affects listeners differently than nonlinguistic noise [e.g., Simpson, S. A., and Cooke, M. (2005). "Consonant identification in N-talker babble is a nonmonotonic function of N," J. Acoust. Soc. Am. 118, 2775-2778; Sperry, J. L., Wiley, T. L., and Chial, M. R. (1997). "Word recognition performance in various background competitors," J. Am. Acad. Audiol. 8, 71-80] but few studies of multi-talker babble have employed background babble in languages other than the target speech language. To determine whether the adverse effect of background speech is due to the linguistic content or to the acoustic characteristics of the speech masker, this study assessed speech-in-noise recognition when the language of the background noise was either the same or different from the language of the target speech. Replicating previous findings, results showed poorer English sentence recognition by native English listeners in six-talker babble than in two-talker babble, regardless of the language of the babble. In addition, our results showed that in two-talker babble, native English listeners were more adversely affected by English babble than by Mandarin Chinese babble. These findings demonstrate informational masking on sentence-in-noise recognition in the form of "linguistic interference." Whether this interference is at the lexical, sublexical, and/or prosodic levels of linguistic structure and whether it is modulated by the phonetic similarity between the target and noise languages remains to be determined.

The Effect of Body-Worn Stereophonic Compression Hearing AIDS on Severely Hearing-Impaired Adults

A group of 16 severely hearing-impaired adults compared two types of body-worn hearing aids: A stereophonic hearing aid with compression (SC-aid) and a monophonic aid without compression (dummy) in a double-blind experiment. Each subject was given a hearing aid of each type and compared them in some test situations in the clinic and during a period of one month in some daily life situations. The results show no effect from the stereophonic feature of the SC-aid. With regard to the compression feature, the subjects performed better with the SC-aid in a complete speech audiometric test in noise-free environments. However, in situations with various types of background noise the subjects generally performed better with the dummy aid. Also, the subjects generally preferred the dummy aid and claimed that background noise appeared louder in the SC-aid. On the basis of these results it is proposed that the “knee point” of the SC-aid should be adjustable and furthermore that these results can hardly be generalize...