Types Of Background Noise Research Articles

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 Effect of Age and Type of Noise on Speech Perception under Conditions of Changing Context and Noise Levels

Objective: Everyday life includes fluctuating noise levels, resulting in continuously changing speech intelligibility. The study aims were: (1) to quantify the amount of decrease in age-related speech perception, as a result of increasing noise level, and (2) to test the effect of age on context usage at the word level (smaller amount of contextual cues). Patients and Methods: A total of 24 young adults (age 20-30 years) and 20 older adults (age 60-75 years) were tested. Meaningful and nonsense one-syllable consonant-vowel-consonant words were presented with the background noise types of speech noise (SpN), babble noise (BN), and white noise (WN), with a signal-to-noise ratio (SNR) of 0 and -5 dB. Results: Older adults had lower accuracy in SNR = 0, with WN being the most difficult condition for all participants. Measuring the change in speech perception when SNR decreased showed a reduction of 18.6-61.5% in intelligibility, with age effect only for BN. Both young and older adults used less phonemic context with WN, as compared to other conditions. Conclusion: Older adults are more affected by an increasing noise level of fluctuating informational noise as compared to steady-state noise. They also use less contextual cues when perceiving monosyllabic words. Further studies should take into consideration that when presenting the stimulus differently (change in noise level, less contextual cues), other perceptual and cognitive processes are involved.

The Role of Age-Related Declines in Subcortical Auditory Processing in Speech Perception in Noise.

Older adults, even those without hearing impairment, often experience increased difficulties understanding speech in the presence of background noise. This study examined the role of age-related declines in subcortical auditory processing in the perception of speech in different types of background noise. Participants included normal-hearing young (19 – 29 years) and older (60 – 72 years) adults. Normal hearing was defined as pure-tone thresholds of 25 dB HL or better at octave frequencies from 0.25 to 4 kHz in both ears and at 6 kHz in at least one ear. Speech reception thresholds (SRTs) to sentences were measured in steady-state (SS) and 10-Hz amplitude-modulated (AM) speech-shaped noise, as well as two-talker babble. In addition, click-evoked auditory brainstem responses (ABRs) and envelope following responses (EFRs) in response to the vowel /ɑ/ in quiet, SS, and AM noise were measured. Of primary interest was the relationship between the SRTs and EFRs. SRTs were significantly higher (i.e., worse) by about 1.5 dB for older adults in two-talker babble but not in AM and SS noise. In addition, the EFRs of the older adults were less robust compared to the younger participants in quiet, AM, and SS noise. Both young and older adults showed a “neural masking release,” indicated by a more robust EFR at the trough compared to the peak of the AM masker. The amount of neural masking release did not differ between the two age groups. Variability in SRTs was best accounted for by audiometric thresholds (pure-tone average across 0.5–4 kHz) and not by the EFR in quiet or noise. Aging is thus associated with a degradation of the EFR, both in quiet and noise. However, these declines in subcortical neural speech encoding are not necessarily associated with impaired perception of speech in noise, as measured by the SRT, in normal-hearing older adults.

Echo threshold between passive and electro-acoustic transmission paths in digital hearing protection devices

Electronic hearing protection devices are increasingly used in noisy environments. Theses devices feature a miniaturized external microphone and internal loudspeaker in addition to an analog or digital electronic circuit. They can transmit useful audio signals such as speech and warning signals to the protected ear and can reduce the sound pressure level using dynamic range compression. In the case of a digital electronic circuit, the transmission of audio signals may be noticeably delayed because of the latency introduced by the digital signal processor and by the analog-to-digital and digital-to-analog converters. These delayed audio signals will hence interfere with the audio signals perceived naturally through the passive acoustical path of the device. The proposed study presents an original procedure to evaluate, for two representative passive earplugs, the shortest delay at which human listeners start to perceive two sounds composed of the signal transmitted through the electronic circuit and the passively transmitted signal. This shortest delay is called the echo threshold and represents the delay between the time of perception of one fused sound from two separate sounds. In this study, a transient signal, a clean speech signal, a speech signal corrupted by factory noise, and a speech signal corrupted by babble noise are used to determine the echo thresholds of the two earplugs. Twenty untrained listeners participated in this study, and were asked to determine the echo thresholds using a test software in which attenuated signals are delayed from the original signals in real-time. The findings show that when using hearing devices, the echo threshold depends on four parameters: (a) the attenuation function of the device, (b) the duration of the signal, (c) the level of the background noise and (d) the type of background noise. Defined here as the shortest time delay at which at least 20% of the participants noticed an echo, the echo threshold was found to be 8 ms for a bell signal, 16 ms for clean speech and 22 ms for speech corrupted by babble noise when using a shallow earplug fit. When using a deep fit, the echo threshold was found to be 18 ms for a bell signal and 26 ms for clean speech and 68 ms for speech in factory. No echo threshold could be clearly determined for the speech signal in babble noise with a deep earplug fit.

Scalable identification of mixed environmental sounds, recorded from heterogeneous sources

Sound events can be used to establish context to assist a user to perform context-dependent tasks. The state of the art methods allow the identification of isolated sound events, even with background noise when it can be modeled, however for mixed sound recognition the challenge still stands. The problem consist in identifying all the sounds occurring in a stream. In this paper we propose an audio representation suitable for mixed sounds identification without background/foreground modeling. Our approach is also lightweight, both in computational and space complexity and the final representation does not depend on the length of the input sound. We extract spectral, band-split, frame level features and their first and second derivatives in each band. The final representation is a set of histograms, one for each band. We proved experimentally that this representation is robust and allows the identification of overlapped sound events. We compared our approach against a representation based on the Mel Frequency Cepstral Coefficients and Non Negative Matrix Factorization for blind source separation using a single microphone, this was the only approach comparable to ours. For testing we conducted two different set of experiments. In the first one we collected poor quality audio recordings using a low-end smartphone for training. Without further enhancing or processing we were able to identify the components of classes of sound mixtures, even with sounds downloaded from the Internet where we had no control on the recording conditions or the foreground noise. In the second set of experiments we recorded 15 challenging sound classes of similar spectrum, from an application scenario and identified them in a continuous recording with three types of background noise. Our results outperform the state of the art in speed, precision and recall.

Measurement of Acceptable Noise Level with Background Music.

Background and ObjectivesAcceptable noise level (ANL) is a measure of the maximum background noise level (BNL) that a person is willing to tolerate while following a target story. Although researchers have used various sources of target sound in ANL measures, a limited type of background noise has been used. Extending the previous study of Gordon-Hickey & Moore (2007), the current study determined the effect of music genre and tempo on ANLs as possible factors affecting ANLs. We also investigated the relationships between individual ANLs and the familiarity of music samples and between music ANLs and subjective preference.Subjects and MethodsForty-one participants were seperated into two groups according to their ANLs, 29 low-ANL listeners and 12 high-ANL listeners. Using Korean ANL material, the individual ANLs were measured based on the listeners' most comfortable listening level and BNL. The ANLs were measured in six conditions, with different music tempo (fast, slow) and genre (K-pop, pop, classical) in a counterbalanced order.ResultsOverall, ANLs did not differ by the tempo of background music, but music genre significantly affected individual ANLs. We observed relatively higher ANLs with K-pop music and relatively lower ANLs with classical music. This tendency was similar in both low-ANL and high-ANL groups. However, the subjective ratings of music familiarity and preference affected ANLs differently for low-ANL and high-ANL groups. In contrast to the low-ANL listeners, the ANLs of the high-ANL listeners were significantly affected by music familiarity and preference.ConclusionsThe genre of background music affected ANLs obtained using background music. The degree of music familiarity and preference appears to be associated with individual susceptibility to background music only for listeners who are greatly annoyed by background noise (high-ANL listeners).

Sound reflection measurements on noise barriers in critical conditions

It is often necessary to check the intrinsic acoustic characteristics of installed noise barriers, like sound reflection and airborne sound insulation, to verify their compliance to design specifications or their quality after some years of life. These characteristics may be measured in-situ following CEN/TS 1793-5. These guidelines have been substantially improved in the frame of the European project QUIESST (2009–2012), which is now under consideration by the relevant CEN working groups to produce new European standards. The new method for measuring sound reflection specifies the usage of an electroacoustic sound source and a microphone grid, in order to obtain a set of impulse responses; these are processed by means of improved algorithms to compute the required results. The impulse responses are acquired using MLS (Maximum Length Sequence) or ESS (Exponential Swept-Sine) as test signals. While the acoustical characteristics of a noise barrier obtained using the two signals are generally equivalent, in critical conditions – e.g. excessive background noise or local meteorological variability – some discrepancies may occur. Moreover, different type of background noise (broadband or impulsive) give different effects on the final result, using MLS or ESS test signals. This paper presents a series of experiments designed to put in evidence the differences between Reflection Index measurements performed in the mentioned critical conditions, according to the QUIESST guidelines, done using MLS or ESS signals. The relative advantages and drawbacks are analysed and discussed in detail. Conclusions are drawn on the selection of the best test signal for each situation.

ViSQOL: an objective speech quality model

This paper presents an objective speech quality model, ViSQOL, the Virtual Speech Quality Objective Listener. It is a signal-based, full-reference, intrusive metric that models human speech quality perception using a spectro-temporal measure of similarity between a reference and a test speech signal. The metric has been particularly designed to be robust for quality issues associated with Voice over IP (VoIP) transmission. This paper describes the algorithm and compares the quality predictions with the ITU-T standard metrics PESQ and POLQA for common problems in VoIP: clock drift, associated time warping, and playout delays. The results indicate that ViSQOL and POLQA significantly outperform PESQ, with ViSQOL competing well with POLQA. An extensive benchmarking against PESQ, POLQA, and simpler distance metrics using three speech corpora (NOIZEUS and E4 and the ITU-T P.Sup. 23 database) is also presented. These experiments benchmark the performance for a wide range of quality impairments, including VoIP degradations, a variety of background noise types, speech enhancement methods, and SNR levels. The results and subsequent analysis show that both ViSQOL and POLQA have some performance weaknesses and under-predict perceived quality in certain VoIP conditions. Both have a wider application and robustness to conditions than PESQ or more trivial distance metrics. ViSQOL is shown to offer a useful alternative to POLQA in predicting speech quality in VoIP scenarios.

Matrix sentence intelligibility prediction using an automatic speech recognition system

Objective: The feasibility of predicting the outcome of the German matrix sentence test for different types of stationary background noise using an automatic speech recognition (ASR) system was studied. Design: Speech reception thresholds (SRT) of 50% intelligibility were predicted in seven noise conditions. The ASR system used Mel-frequency cepstral coefficients as a front-end and employed whole-word Hidden Markov models on the back-end side. The ASR system was trained and tested with noisy matrix sentences on a broad range of signal-to-noise ratios. Study sample: The ASR-based predictions were compared to data from the literature (Hochmuth et al, 2015) obtained with 10 native German listeners with normal hearing and predictions of the speech intelligibility index (SII). Results: The ASR-based predictions showed a high and significant correlation (R² = 0.95, p < 0.001) with the empirical data across different noise conditions, outperforming the SII-based predictions which showed no correlation with the empirical data (R² = 0.00, p = 0.987). Conclusions: The SRTs for the German matrix test for listeners with normal hearing in different stationary noise conditions could well be predicted based on the acoustical properties of the speech and noise signals. Minimum assumptions were made about human speech processing already incorporated in a reference-free ordinary ASR system.

Multiple topic identification in human/human conversations

The paper deals with the automatic analysis of real-life telephone conversations between an agent and a customer of a customer care service (ccs). The application domain is the public transportation system in Paris and the purpose is to collect statistics about customer problems in order to monitor the service and decide priorities on the intervention for improving user satisfaction.Of primary importance for the analysis is the detection of themes that are the object of customer problems. Themes are defined in the application requirements and are part of the application ontology that is implicit in the ccs documentation.Due to variety of customer population, the structure of conversations with an agent is unpredictable. A conversation may be about one or more themes. Theme mentions can be interleaved with mentions of facts that are irrelevant for the application purpose. Furthermore, in certain conversations theme mentions are localized in specific conversation segments while in other conversations mentions cannot be localized. As a consequence, approaches to feature extraction with and without mention localization are considered.Application domain relevant themes identified by an automatic procedure are expressed by specific sentences whose words are hypothesized by an automatic speech recognition (asr) system. The asr system is error prone. The word error rates can be very high for many reasons. Among them it is worth mentioning unpredictable background noise, speaker accent, and various types of speech disfluencies.As the application task requires the composition of proportions of theme mentions, a sequential decision strategy is introduced in this paper for performing a survey of the large amount of conversations made available in a given time period. The strategy has to sample the conversations to form a survey containing enough data analyzed with high accuracy so that proportions can be estimated with sufficient accuracy.Due to the unpredictable type of theme mentions, it is appropriate to consider methods for theme hypothesization based on global as well as local feature extraction. Two systems based on each type of feature extraction will be considered by the strategy. One of the four methods is novel. It is based on a new definition of density of theme mentions and on the localization of high density zones whose boundaries do not need to be precisely detected.The sequential decision strategy starts by grouping theme hypotheses into sets of different expected accuracy and coverage levels. For those sets for which accuracy can be improved with a consequent increase of coverage a new system with new features is introduced. Its execution is triggered only when specific preconditions are met on the hypotheses generated by the basic four systems.Experimental results are provided on a corpus collected in the call center of the Paris transportation system known as ratp. The results show that surveys with high accuracy and coverage can be composed with the proposed strategy and systems. This makes it possible to apply a previously published proportion estimation approach that takes into account hypothesization errors.

배경소음상황에 따른 성인 말더듬화자의 발화 관련 변수 비교

This study was mainly aimed at investigating on the effects of background noises (i.e., white noise, multi-speaker conversational babble) on stuttering rate and other speech-related measures (i.e., articulation rate, speech effort). Nine Korean-speaking adults who stutter participated in the study. Each of the participants was asked to read a series of passages under each of four experimental conditions (i.e., typical solo reading (TR), choral reading (CR), reading under white noise presented (WR), reading with multi-speaker conversational babble presented (BR). Stuttering rate was computed based on a percentage of syllables stuttered (%SS) and articulation rate was also assessed as another speech-related measure under each of the experimental conditions. To examine the amount of physical effort needed to read, the speech effort was measured by using the 9-point Speech Effort Self Rating Scale originally employed by Ingham et al. (2006). Study results showed that there were no significant differences among each of the passage reading conditions in terms of stuttering rate, articulation rate, and speech effort. In conclusion, it can be argued that the two different types of background noises (i.e., white noise and multi-speaker conversational babble) are not different in the extent to which each of them enhances fluency of adults who stutter. Self ratings of speech effort may be also useful in measuring speech-related variables associated with vocal changes induced under each of the fluency enhancing conditions.

Interdependent processing and encoding of speech and concurrent background noise.

Speech processing can often take place in adverse listening conditions that involve the mixing of speech and background noise. In this study, we investigated processing dependencies between background noise and indexical speech features, using a speeded classification paradigm (Garner, 1974; Exp. 1), and whether background noise is encoded and represented in memory for spoken words in a continuous recognition memory paradigm (Exp. 2). Whether or not the noise spectrally overlapped with the speech signal was also manipulated. The results of Experiment 1 indicated that background noise and indexical features of speech (gender, talker identity) cannot be completely segregated during processing, even when the two auditory streams are spectrally nonoverlapping. Perceptual interference was asymmetric, whereby irrelevant indexical feature variation in the speech signal slowed noise classification to a greater extent than irrelevant noise variation slowed speech classification. This asymmetry may stem from the fact that speech features have greater functional relevance to listeners, and are thus more difficult to selectively ignore than background noise. Experiment 2 revealed that a recognition cost for words embedded in different types of background noise on the first and second occurrences only emerged when the noise and the speech signal were spectrally overlapping. Together, these data suggest integral processing of speech and background noise, modulated by the level of processing and the spectral separation of the speech and noise.

Estimation of Signal-to-Noise Ratios in Realistic Sound Scenarios.

Both in the design of hearing-device features, such as noise reduction, and in the evaluation measurements of such features, there is a need for more information about the sound scenarios hearing-device users encounter. The limitations of adaptive speech tests as outcome measures in the evaluation of hearing-device features have been discussed. The obvious alternative to adaptive speech testing is to test at a fixed signal-to-noise ratio (SNR). However, the question is which SNRs should be used if the test reflects real-life situations. The purpose of this study was to estimate SNRs in realistic sound environments encountered by hearing aid users. Research design was a descriptive study where recorded sound files were statistically analyzed. A total of 20 experienced, bilaterally fitted hearing aid users, all satisfied with their current hearing aids, made recordings for 3-4 days. The informants were instructed to record different situations in daily life and were told that all situations were of equal importance. The informants ranged in age from 18-81 yr old, and they had various occupations and varying hearing-loss configurations. The total duration of the recorded material for each informant was, on average, 84 min; the number of recordings was, on average, 17 per informant. The sound files were categorized based on the type of background noise and were analyzed with use of a manual noise estimation procedure. Segments of speech with noise present were cut out from the original recordings. Corresponding noise-only segments were also extracted. On the basis of power calculations for these two types of recorded segments, the SNR was estimated. Frequency-specific and overall SNRs, calculated both based on unweighted and A-weighted speech and noise levels, were derived. An estimation uncertainty measure was also developed. The range of SNRs found in the material was large. The most striking finding was that there were very few recorded situations where the SNR was negative or even close to 0 dB. For speech-in-babble noise, the average SNR was approximately 5 dB. The estimation uncertainty was generally low but became higher in highly fluctuating noises and at very low SNRs. The estimated SNRs were higher than previously reported. The results can be used in the design and evaluation of hearing-device features.

Memory performance on the Auditory Inference Span Test is independent of background noise type for young adults with normal hearing at high speech intelligibility.

Listening in noise is often perceived to be effortful. This is partly because cognitive resources are engaged in separating the target signal from background noise, leaving fewer resources for storage and processing of the content of the message in working memory. The Auditory Inference Span Test (AIST) is designed to assess listening effort by measuring the ability to maintain and process heard information. The aim of this study was to use AIST to investigate the effect of background noise types and signal-to-noise ratio (SNR) on listening effort, as a function of working memory capacity (WMC) and updating ability (UA). The AIST was administered in three types of background noise: steady-state speech-shaped noise, amplitude modulated speech-shaped noise, and unintelligible speech. Three SNRs targeting 90% speech intelligibility or better were used in each of the three noise types, giving nine different conditions. The reading span test assessed WMC, while UA was assessed with the letter memory test. Twenty young adults with normal hearing participated in the study. Results showed that AIST performance was not influenced by noise type at the same intelligibility level, but became worse with worse SNR when background noise was speech-like. Performance on AIST also decreased with increasing memory load level. Correlations between AIST performance and the cognitive measurements suggested that WMC is of more importance for listening when SNRs are worse, while UA is of more importance for listening in easier SNRs. The results indicated that in young adults with normal hearing, the effort involved in listening in noise at high intelligibility levels is independent of the noise type. However, when noise is speech-like and intelligibility decreases, listening effort increases, probably due to extra demands on cognitive resources added by the informational masking created by the speech fragments and vocal sounds in the background noise.

The impact of different background noises: effects on cognitive performance and perceived disturbance in employees with aided hearing impairment and normal hearing.

Health care professionals frequently meet employees with hearing impairment (HI) who experience difficulties at work. There are indications that the majority of these difficulties might be related to the presence of background noise. Moreover, research has also shown that high-level noise has a more detrimental effect on cognitive performance and self-rated disturbance in individuals with HI than low-level noise. The purpose of this study was to examine the impact of different types of background noise on cognitive performance and perceived disturbance (PD) in employees with aided HI and normal hearing. A mixed factorial design was conducted to examine the effect of noise in four experimental conditions. A total of 40 participants (21 men and 19 women) were recruited to take part in the study. The study sample consisted of employees with HI (n = 20) and normal hearing (n = 20). The group with HI had a mild-moderate sensorineural HI, and they were all frequent hearing-aid users. The current study was conducted by using four general work-related tasks (mental arithmetic, orthographic decoding, phonological decoding, and serial recall) in four different background conditions: (1) quiet, (2) office noise at 56 dBA, (3) daycare noise at 73.5 dBA, and (4) traffic noise at 72.5 dBA. Reaction time and the proportion of correct answers in the working tasks were used as outcome measures of cognitive performance. The Borg CR-10 scale was used to assess PD. Data collection occurred on two separate sessions, completed within 4 wk of each other. All tasks and experimental conditions were used in a counterbalanced order. Two-way analysis of variance with repeated measures was performed to analyze the results. To examine interaction effects, pairwise t-tests were used. Pearson correlation coefficients between reaction time and proportion of correct answers, and cognitive performance and PD were also calculated to examine the possible correlation between the different variables. No significant between-group or within-group differences in cognitive performance were observed across the four background conditions. Ratings of PD showed that both groups rated PD according to noise level, where higher noise level generated a higher PD. The present findings also demonstrated that the group with HI was more disturbed by higher than lower levels of noise (i.e., traffic and daycare setting compared with office setting). This pattern was observed consistently throughout four working tasks where the group with HI reported a significantly greater PD in the daycare and traffic settings compared with office noise. The present results demonstrate that background noise does not impair cognitive performance in nonauditory tasks in employees with HI and normal hearing, but that PD is affected to a greater extent in employees with HI during higher levels of background noise exposure. In addition, this study also supports previous studies regarding the detrimental effects that high-level noise has on employees with HI. Therefore, we emphasize the need of both self-rated and cognitive measurements in hearing care and occupational health services for both employees with normal hearing and HI.

Development of speech perception under adverse listening conditions

Speech communication success is dependent on interactions among the talker, listener, and listening environment. One such important interaction is between the listener’s age and the noise and reverberation in the environment. Previous work has demonstrated that children have greater difficulty than adults in noisy and highly reverberant environments, such as those frequently found in classrooms. I will review research that considers how a talker’s production patterns also contribute to speech comprehension, focusing on nonnative talkers. Studies from my lab have demonstrated that children have more difficulty than adults perceiving speech that deviates from native language norms, even in quiet listening conditions in which adults are highly accurate. When a nonnative talker’s voice was combined with noise, children’s word recognition was particularly poor. Therefore, similar to the developmental trajectory for speech perception in noise or reverberation, the ability to accurately perceive speech produced by nonnative talkers continues to develop well into childhood. Metrics to quantify speech intelligibility in specific rooms must consider both listener characteristics, talker characteristics, and their interaction. Future research should investigate how children’s speech comprehension is influenced by the interaction between specific types of background noise and reverberation and talker production characteristics. [Work supported by NIH-R21DC010027.]

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.

Effect of Several Parameters on Pitch Perception of Unresolved Harmonics

This paper studied the effect of several parameters on pitch perception of unresolved harmonics by measuring fundamental frequency difference limens. The parameters included roving fundamental frequency or not, type of background noise, and cutoff frequency of lowpass pink noise. Results found that performance was significantly worse when the fundamental frequency was roved than the constant case, lowpass pink noise had similar ability to mask combination tones as threshold equalizing noise, and that combination tones could be masked if the cutoff frequency of the lowpass pink noise was not lower than the frequency of the tenth harmonic. This paper proposed a set of appropriate parameters for experiments of pitch perception, which suggested that roving fundamental frequency, using threshold equalizing noise with the level of about 8-13 dB below the level per component of the stimuli, or using lowpass pink noise with the cutoff frequency not lower than the frequency of the tenth harmonic, can achieve effective performance for pitch perception.

MFCC AND CMN BASED SPEAKER RECOGNITION IN NOISY ENVIRONMENT

The performance of automatic speaker recognition (ASR) system degrades drastically in the presence of noise and other distortions, especially when there is a noise level mismatch between the training and testing environments. This paper explores the problem of speaker recognition in noisy conditions, assuming that speech signals are corrupted by noise. A major problem of most speaker recognition systems is their unsatisfactory performance in noisy environments. In this experimental research, we have studied a combination of Mel Frequency Cepstral Coefficients (MFCC) for feature extraction and Cepstral Mean Normalization (CMN) techniques for speech enhancement. Our system uses a Gaussian Mixture Models (GMM) classifier and is implemented under MATLAB®7 programming environment. The process involves the use of speaker data for both training and testing. The data used for testing is matched up against a speaker model, which is trained with the training data using GMM modeling. Finally, experiments are carried out to test the new model for ASR given limited training data and with differing levels and types of realistic background noise. The results have demonstrated the robustness of the new system.

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.