Objective Measures of Listening Effort: Effects of Background Noise and Noise Reduction

Sarampalis, Anastasios; Kalluri, Sridhar; Edwards, Brent; Hafter, Ervin R.

doi:10.1044/1092-4388(2009/08-0111)

Cited by 384 publications

(518 citation statements)

References 28 publications

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“…Previous studies showed that NR algorithms based on spectral subtraction fail to improve speech intelligibility (e.g., Hu and Loizou 2007;Sarampalis et al 2009). However, several low-level (i.e., sensory) and high-level (e.g., linguistic) effects of NR may underlie the lack of speech perception benefits.…”

Section: Discussionmentioning

confidence: 99%

“…The effects of this algorithm on speech perception have been investigated in previous studies (e.g., Sarampalis et al 2009). The algorithm is described in detail by Fang and Nilsson (2004).…”

Section: Noise Reduction Algorithmmentioning

confidence: 99%

“…Their findings showed that in all but one of the 64 conditions (i.e., one algorithm with one type of noise at one SNR), NR algorithms did not improve sentence recognition. Furthermore, Sarampalis et al (2009) showed that NR algorithms based on spectral subtraction, representative of current technology Malah 1984, 1985), even slightly reduced word recognition with high-context sentences.…”

Section: Introductionmentioning

confidence: 99%

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Effects of Noise Reduction on AM and FM Perception

Ives

Calcus

Kalluri

et al. 2012

JARO

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The goal of noise reduction (NR) algorithms in digital hearing aid devices is to reduce background noise whilst preserving as much of the original signal as possible. These algorithms may increase the signal-tonoise ratio (SNR) in an ideal case, but they generally fail to improve speech intelligibility. However, due to the complex nature of speech, it is difficult to disentangle the numerous low-and high-level effects of NR that may underlie the lack of speech perception benefits. The goal of this study was to better understand why NR algorithms do not improve speech intelligibility by investigating the effects of NR on the ability to discriminate two basic acoustic features, namely amplitude modulation (AM) and frequency modulation (FM) cues, known to be crucial for speech identification in quiet and in noise. Here, discrimination of complex, non-linguistic AM and FM patterns was measured for normal hearing listeners using a same/different task. The stimuli were generated by modulating 1-kHz pure tones by either a two-component AM or FM modulator with patterns changed by manipulating component phases. Modulation rates were centered on 3 Hz. Discrimination of AM and FM patterns was measured in quiet and in the presence of a white noise that had been passed through a gammatone filter centered on 1 kHz. The noise was presented at SNRs ranging from −6 to +12 dB. Stimuli were left as such or processed via an NR algorithm based on the spectral subtraction method. NR was found to yield small but systematic improvements in discrimination for the AM conditions at favorable SNRs but had little effect, if any, on FM discrimination. A computational model of early auditory processing was developed to quantify the fidelity of AM and FM transmission. The model captured the improvement in discrimination performance for AM stimuli at high SNRs with NR. However, the model also predicted a relatively small detrimental effect of NR for FM stimuli in contrast with the average psychophysical data. Overall, these results suggest that the lack of benefits of NR on speech intelligibility is partly caused by the limited effect of NR on the transmission of narrowband speech modulation cues.

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Section: Discussionmentioning

confidence: 99%

Section: Noise Reduction Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effects of Noise Reduction on AM and FM Perception

Ives

Calcus

Kalluri

et al. 2012

JARO

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“…Although substantial effort is required to perceive speech in extremely noisy conditions, accurate perception in moderately noisy and reverberant environments is relatively effortless (1), presumably because of the presence of general filtering mechanisms in the auditory pathway (2). These mechanisms likely influence the representation and perception of both speech and other natural sounds with similarly rich spectrotemporal structure, such as species-specific vocalizations (3)(4)(5).…”

mentioning

confidence: 99%

Mechanisms of noise robust representation of speech in primary auditory cortex

Mesgarani

David

Fritz

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

126

155

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Humans and animals can reliably perceive behaviorally relevant sounds in noisy and reverberant environments, yet the neural mechanisms behind this phenomenon are largely unknown. To understand how neural circuits represent degraded auditory stimuli with additive and reverberant distortions, we compared single-neuron responses in ferret primary auditory cortex to speech and vocalizations in four conditions: clean, additive white and pink (1/f) noise, and reverberation. Despite substantial distortion, responses of neurons to the vocalization signal remained stable, maintaining the same statistical distribution in all conditions. Stimulus spectrograms reconstructed from population responses to the distorted stimuli resembled more the original clean than the distorted signals. To explore mechanisms contributing to this robustness, we simulated neural responses using several spectrotemporal receptive field models that incorporated either a static nonlinearity or subtractive synaptic depression and multiplicative gain normalization. The static model failed to suppress the distortions. A dynamic model incorporating feedforward synaptic depression could account for the reduction of additive noise, but only the combined model with feedback gain normalization was able to predict the effects across both additive and reverberant conditions. Thus, both mechanisms can contribute to the abilities of humans and animals to extract relevant sounds in diverse noisy environments.hearing | cortical | population code | phonemes V ocal communication in the real world often takes place in complex, noisy acoustic environments. Although substantial effort is required to perceive speech in extremely noisy conditions, accurate perception in moderately noisy and reverberant environments is relatively effortless (1), presumably because of the presence of general filtering mechanisms in the auditory pathway (2). These mechanisms likely influence the representation and perception of both speech and other natural sounds with similarly rich spectrotemporal structure, such as species-specific vocalizations (3-5). Despite the central role this robustness must play in animal and human hearing, little is known about the underlying neural mechanisms and whether the brain maintains invariant representations of these stimuli across variable soundscapes causing acoustic distortions of the original signals.Several theoretical and experimental studies have postulated that the distribution of linear spectrotemporal tuning of neurons found in the auditory pathway could support enhanced representation of temporal and spectral modulations matched to those prevalent in natural stimuli (6, 7). Others have attributed this effect to nonlinear response properties of neurons (8) and adaptation with various timescales (9). In this study, we tested the noise robustness of auditory cortical neurons by recording responses in ferret primary auditory cortex (A1) to natural vocalizations that were distorted by additive white and pink (1/f) noise or by convolutive reverber...

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“…The Sentence-final Word Identification and Recall (SWIRL) test was developed for this purpose [9], inspired by [11,13]. During the test, subjects are presented with sentences in background noise individualized so 95% of all sentences are fully understood.…”

Section: Hearing Aids and Cognitionmentioning

confidence: 99%

Cognitive hearing aids? Insights and possibilities

Petersen¹,

Lunner²

2015

AIP Conference Proceedings

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The working memory plays an important role in successfully overcoming adverse listening conditions andshould consequently be considered when designing and testing hearing aids. A number of studies have established therelationship between hearing in noise and working memory involvement, but with the Sentence-final Word Identificationand Recall (SWIRL) test, it is possible to show that working memory is also involved in listening under favorableconditions and that noise reduction has a positive influence in situation with very little noise. Although the capacity of theworking memory is a finite individual size, its involvement can differ with fatigue and other factors and individualizationof hearing aids should take this into account to obtain the best performance. A way of individually adapting hearing aidsis based on changes in the electrical activity of the brain (EEG). Here we present the possibilities that arise from usingEEG and show that ear-mounted electrodes is able to record useful EEG that can be explored for individualization ofhearing aids. Such an adaptation could be done based on changes in the electrical activity of the brain (EEG). Here wepresent the possibilities that arise from using EEG and show that ear-mounted electrodes is able to record useful EEG thatcan be explored for individualization of hearing aids

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Objective Measures of Listening Effort: Effects of Background Noise and Noise Reduction

Abstract: Results from both dual tasks support the hypothesis that NR reduces listening effort and frees up cognitive resources for other tasks. Future hearing aid research should incorporate objective measurements of cognitive benefits.

Cited by 384 publications

References 28 publications

Effects of Noise Reduction on AM and FM Perception

Effects of Noise Reduction on AM and FM Perception

Mechanisms of noise robust representation of speech in primary auditory cortex

Cognitive hearing aids? Insights and possibilities

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