Foreground Sounds Research Articles

Reduced neural responses to natural foreground versus background sounds in auditory cortex.

In everyday hearing, listeners face the challenge of understanding behaviorally relevant foreground stimuli (speech, vocalizations) in complex backgrounds (environmental, mechanical noise). Prior studies have shown that high-order areas of human auditory cortex (AC) pre-attentively form an enhanced representation of foreground stimuli in the presence of background noise. This enhancement requires identifying and grouping the features that comprise the background so they can be removed from the foreground representation. To study the cortical computations supporting this process, we recorded single unit activity in AC of male and female ferrets during the presentation of concurrent natural sounds from these two categories. In contrast to expectations from studies in high-order AC, single-unit responses to foreground sounds were strongly reduced relative to the paired background in primary and secondary AC. The degree of reduction could not be explained by a neuron's preference for the foreground or background stimulus in isolation but could be partially explained by spectro-temporal statistics that distinguish foreground and background categories. Responses to synthesized sounds with statistics either matched or randomized relative to natural sounds showed progressively decreased reduction of foreground responses as natural sound statistics were removed. These results challenge the expectation that cortical foreground representations emerge directly from a mixed representation in the auditory periphery. Instead, they suggest the early AC maintains a robust representation of background noise. Strong background representations may produce a distributed code, facilitating selection of foreground signals from a relatively small subpopulation of AC neurons at later processing stages.Significance statement Perception of important sounds in a world cluttered with competing background noise requires the ability to segregate relevant and irrelevant sound sources. Most prior work investigating neural mechanisms of this background/foreground contrast has supported the theory that auditory cortex activity is largely invariant to background noise, consistent with evidence from behavioral studies. However, it remains unclear what information about background noise is represented at the single-unit level. Here, contrary to prevailing theories, we show a relative dominance of single-unit responses to natural background noise over responses to natural foreground sounds in ferret auditory cortex. A robust representation of background noise in early stages of auditory cortex may be necessary for grouping features into perceptual objects and selecting information from foreground signals for preferential representation in downstream brain areas.

Noise schemas aid hearing in noise

Human hearing is robust to noise, but the basis of this robustness is poorly understood. Several lines of evidence are consistent with the idea that the auditory system adapts to sound components that are stable over time, potentially achieving noise robustness by suppressing noise-like signals. Yet background noise often provides behaviorally relevant information about the environment and thus seems unlikely to be completely discarded by the auditory system. Motivated by this observation, we explored whether noise robustness might instead be mediated by internal models of noise structure that could facilitate the separation of background noise from other sounds. We found that detection, recognition, and localization in real-world background noise were better for foreground sounds positioned later in a noise excerpt, with performance improving over the initial second of exposure to a noise. These results are consistent with both adaptation-based and model-based accounts (adaptation increases over time and online noise estimation should benefit from acquiring more samples). However, performance was also robust to interruptions in the background noise and was enhanced for intermittently recurring backgrounds, neither of which would be expected from known forms of adaptation. Additionally, the performance benefit observed for foreground sounds occurring later within a noise excerpt was reduced for recurring noises, suggesting that a noise representation is built up during exposure to a new background noise and then maintained in memory. These findings suggest that noise robustness is supported by internal models-"noise schemas"-that are rapidly estimated, stored over time, and used to estimate other concurrent sounds.

Research on blind source separation of operation sounds of metro power transformer through an Adaptive Threshold REPET algorithm

PurposeThe safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system. Through voiceprint technology, the sounds emitted by the transformer can be monitored in real-time, thereby achieving real-time monitoring of the transformer’s operational status. However, the environment surrounding power transformers is filled with various interfering sounds that intertwine with both the normal operational voiceprints and faulty voiceprints of the transformer, severely impacting the accuracy and reliability of voiceprint identification. Therefore, effective preprocessing steps are required to identify and separate the sound signals of transformer operation, which is a prerequisite for subsequent analysis.Design/methodology/approachThis paper proposes an Adaptive Threshold Repeating Pattern Extraction Technique (REPET) algorithm to separate and denoise the transformer operation sound signals. By analyzing the Short-Time Fourier Transform (STFT) amplitude spectrum, the algorithm identifies and utilizes the repeating periodic structures within the signal to automatically adjust the threshold, effectively distinguishing and extracting stable background signals from transient foreground events. The REPET algorithm first calculates the autocorrelation matrix of the signal to determine the repeating period, then constructs a repeating segment model. Through comparison with the amplitude spectrum of the original signal, repeating patterns are extracted and a soft time-frequency mask is generated.FindingsAfter adaptive thresholding processing, the target signal is separated. Experiments conducted on mixed sounds to separate background sounds from foreground sounds using this algorithm and comparing the results with those obtained using the FastICA algorithm demonstrate that the Adaptive Threshold REPET method achieves good separation effects.Originality/valueA REPET method with adaptive threshold is proposed, which adopts the dynamic threshold adjustment mechanism, adaptively calculates the threshold for blind source separation and improves the adaptability and robustness of the algorithm to the statistical characteristics of the signal. It also lays the foundation for transformer fault detection based on acoustic fingerprinting.

Listening loops and the adapting auditory brain.

Analysing complex auditory scenes depends in part on learning the long-term statistical structure of sounds comprising those scenes. One way in which the listening brain achieves this is by analysing the statistical structure of acoustic environments over multiple time courses and separating background from foreground sounds. A critical component of this statistical learning in the auditory brain is the interplay between feedforward and feedback pathways-"listening loops"-connecting the inner ear to higher cortical regions and back. These loops are likely important in setting and adjusting the different cadences over which learned listening occurs through adaptive processes that tailor neural responses to sound environments that unfold over seconds, days, development, and the life-course. Here, we posit that exploring listening loops at different scales of investigation-from in vivo recording to human assessment-their role in detecting different timescales of regularity, and the consequences this has for background detection, will reveal the fundamental processes that transform hearing into the essential task of listening.

Quantifying headphone listening experience in virtual sound environments using distraction

AbstractHeadphones are commonly used in various environments including at home, outside and on public transport. However, the perception and modelling of the interaction of headphone audio and noisy environments is relatively unresearched. This work investigates the headphone listening experience in noisy environments using the perceptual attributes of distraction and quality of listening experience. A virtual sound environment was created to simulate real-world headphone listening, with variations in foreground sounds, background contexts and busyness, headphone media content and simulated active noise control. Listening tests were performed, where 15 listeners rated both distraction and quality of listening experience across 144 stimuli using a multiple-stimulus presentation. Listener scores were analysed and compared to a computational model of listener distraction. The distraction model was found to be a good predictor of the perceptual distraction rating, with a correlation of 0.888 and an RMSE of 13.4%, despite being developed to predict distraction in the context of audio-on-audio interference in sound zones. In addition, perceived distraction and quality of listening experience had a strong negative correlation of − 0.953. Furthermore, the busyness and type of the environment, headphone media, loudness of the foreground sound and active noise control on/off were significant factors in determining the distraction and quality of listening experience scores.

Real-time rendering of decorative sound textures for soundscapes

Audio recordings contain rich information about sound sources and their properties such as the location, loudness, and frequency of events. One prevalent component in sound recordings is the sound texture, which contains a massive number of events. In such a texture, there can be some distinct and repeated sounds that we term as a foreground sound. Birds chirping in the wind is one such decorative sound texture with the chirping as a foreground sound and the wind as a background texture. To render these decorative sound textures in real-time and with high quality, we create two-layer Markov Models to enable smooth transitions from sound grain to sound grain and propose a hierarchical scheme to generate Head-Related Transfer Function filters for localization cues of sounds represented as area/volume sources. Moreover, during the synthesis stage, we provide control over the frequency and intensity of sounds for customization. Lastly, foreground sounds are often blended into background textures such as the sound of rain splats on car surfaces becoming submerged in the background rain. We develop an extraction component that outperforms existing learning-based methods to facilitate our synthesis with perceptible foreground sounds and well-defined textures.

Effects of Background Sounds on Annoyance Reaction to Foreground Sounds in Psychoacoustic Experiments in the Laboratory: Limits and Consequences

Effects of Background Sounds on Annoyance Reaction to Foreground Sounds in Psychoacoustic Experiments in the Laboratory: Limits and Consequences

Effects of Background Sounds on Annoyance Reaction to Foreground Sounds in Psychoacoustic Experiments in the Laboratory: Limits and Consequences

In a variety of applications, e.g., psychoacoustic experiments, virtual sound propagation demonstration, or synthesized noise production, noise samples are played back in laboratories. To simulate realistic scenes or to mask unwanted background sounds, it is sometimes preferable to add background ambient sounds to the noise. However, this can influence noise perception. It should be ensured that either background sounds do not affect, e.g., annoyance from foreground noise or that possible effects can be quantified. Two laboratory experiments are reported, in which effects of mixing background sounds to foreground helicopter samples were investigated. By means of partially balanced incomplete block designs, possible effects of three independent variables, i.e., helicopter’s sound exposure level, background type, and background sound pressure level were tested on the dependent variable annoyance, rated on the ICBEN 11-point numerical scale. The main predictor of annoyance was helicopter’s sound exposure level. Stimuli with eventful background sounds were found to be more annoying than those with less eventful background sounds. Furthermore, background type and level interacted significantly. For the major part of the background sound level range, increasing the background level was associated with increased or decreased annoyance for stimuli with eventful and less eventful background sounds, respectively.

Background noise exerts diverse effects on the cortical encoding of foreground sounds.

In natural listening conditions, many sounds must be detected and identified in the context of competing sound sources, which function as background noise. Traditionally, noise is thought to degrade the cortical representation of sounds by suppressing responses and increasing response variability. However, recent studies of neural network models and brain slices have shown that background synaptic noise can improve the detection of signals. Because acoustic noise affects the synaptic background activity of cortical networks, it may improve the cortical responses to signals. We used spike train decoding techniques to determine the functional effects of a continuous white noise background on the responses of clusters of neurons in auditory cortex to foreground signals, specifically frequency-modulated sweeps (FMs) of different velocities, directions, and amplitudes. Whereas the addition of noise progressively suppressed the FM responses of some cortical sites in the core fields with decreasing signal-to-noise ratios (SNRs), the stimulus representation remained robust or was even significantly enhanced at specific SNRs in many others. Even though the background noise level was typically not explicitly encoded in cortical responses, significant information about noise context could be decoded from cortical responses on the basis of how the neural representation of the foreground sweeps was affected. These findings demonstrate significant diversity in signal in noise processing even within the core auditory fields that could support noise-robust hearing across a wide range of listening conditions.NEW & NOTEWORTHY The ability to detect and discriminate sounds in background noise is critical for our ability to communicate. The neural basis of robust perceptual performance in noise is not well understood. We identified neuronal populations in core auditory cortex of squirrel monkeys that differ in how they process foreground signals in background noise and that may contribute to robust signal representation and discrimination in acoustic environments with prominent background noise.

Why can't I hear that “still small voice”: Did Neolithic noise growth result in human “alienation from nature”?

Did human consciousness change in the transition from the Paleolithic to the Neolithic era? Paleolithic humans lived with nature in small isolated groups of perhaps 30 to 150 people. For survival, Paleolithic hunter gatherers focused their auditory attention on recognizing emerging opportunities and threats. Consequently, Paleolithics were very attentive to weak background sounds. Sound levels often reached “din” levels but it was not noise. The soundscape was signal-rich, reflecting a healthy and resource-rich environment. Neurons of predator and prey evolved to facilitate recognition of the identity, condition, and intention of their natural sources. The Neolithic era that followed emphasized large scale agriculture and herding. Because of the need for organized labor Neolithic farmers were gradually distanced from nature and its sounds. Background sounds no longer signified major threats. With the emergence of cities, over time background sounds became noise. The Neolithic soundscape consisted largely of nearby human and agricultural sounds. So, Neolithic farmers gradually shifted their auditory attention to foreground sounds. Thus, Neolithics gradually lost aural contact with nature. Noise may be an important cause for “alienation from nature,” and for inattention to that “still small voice.”

Using binocular rivalry to tag foreground sounds: Towards an objective visual measure for auditory multistability

In binocular rivalry, paradigms have been proposed for unobtrusive moment-by-moment readout of observers' perceptual experience ("no-report paradigms"). Here, we take a first step to extend this concept to auditory multistability. Observers continuously reported which of two concurrent tone sequences they perceived in the foreground: high-pitch (1008 Hz) or low-pitch (400 Hz) tones. Interstimulus intervals were either fixed per sequence (Experiments 1 and 2) or random with tones alternating (Experiment 3). A horizontally drifting grating was presented to each eye; to induce binocular rivalry, gratings had distinct colors and motion directions. To associate each grating with one tone sequence, a pattern on the grating jumped vertically whenever the respective tone occurred. We found that the direction of the optokinetic nystagmus (OKN)-induced by the visually dominant grating-could be used to decode the tone (high/low) that was perceived in the foreground well above chance. This OKN-based readout improved after observers had gained experience with the auditory task (Experiments 1 and 2) and for simpler auditory tasks (Experiment 3). We found no evidence that the visual stimulus affected auditory multistability. Although decoding performance is still far from perfect, our paradigm may eventually provide a continuous estimate of the currently dominant percept in auditory multistability.

Sound im Hörfunk. Zur Praxis des Radio-Sounddesign

Radio analysis usually concentrates on radio content. Nevertheless, radio professionals invest a lot of effort in shaping the sound of radio programmes. In this article, I explore two dimensions of sound work that is done by radio practitioners: sound design and sound processing. The former is performed by sound designers as well as producers, technicians and presenters/DJs. They all deal with the time structure by shaping music, spoken word and ,jingles’ into an audio flow. In particular, I suggest a model of analysis that examines foreground sounds and background sounds separately to identify a perceivable density of radio programmes. The second dimension of sound work applies to the sound texture and aims at creating a distinguishable sound compared to other stations on the same market.

Perception and automatic detection of wind-induced microphone noise.

Wind can induce noise on microphones, causing problems for users of hearing aids and for those making recordings outdoors. Perceptual tests in the laboratory and via the Internet were carried out to understand what features of wind noise are important to the perceived audio quality of speech recordings. The average A-weighted sound pressure level of the wind noise was found to dominate the perceived degradation of quality, while gustiness was mostly unimportant. Large degradations in quality were observed when the signal to noise ratio was lower than about 15 dB. A model to allow an estimation of wind noise level was developed using an ensemble of decision trees. The model was designed to work with a single microphone in the presence of a variety of foreground sounds. The model outputted four classes of wind noise: none, low, medium, and high. Wind free examples were accurately identified in 79% of cases. For the three classes with noise present, on average 93% of samples were correctly assigned. A second ensemble of decision trees was used to estimate the signal to noise ratio and thereby infer the perceived degradation caused by wind noise.

Soundscape Reproduction and Synthesis

The aims of this work were to investigate (i) whether soundscape perceptual dimensions are correctly reproduced by ambisonic loudspeaker playback, (ii) whether soundscape dimensional analysis is robust to changes of location and from the field to laboratory playback, and (iii) whether a simple soundscape synthesis can be used to interactively design a soundscape. The first two aims were addressed by an experiment which attempted to repeat the dimensional analysis made by Kang (Kang, J. (2007), Urban Sound Environment. London: Taylor and Francis). Kang used semantic differential scales to conduct an in-situ survey of two urban soundscapes in Sheffield, UK. He used factor analysis to derive four perceptual dimensions from the responses. The present work repeated this approach, but the fifteen participants were judging ambisonic recordings of four soundscapes in Manchester, UK. The present work found very similar dimensions to Kang but with more variance explained: relaxation/calmness (41%), dynamics/vibrancy (10%), communication (7%) and spatiality (7%). The dimensions from the two studies load onto the semantic scales in a similar way. These results indicate that an ambisonic reproduction of a soundscape gives similar results to field experiments, though with more variance explained. They also show that dimensional analysis of soundscape response is robust enough to produce similar results for different locations in different cities. To investigate the third aim, the ambisonic reproduction was extended to a system which allowed independent interactive control of sixteen foreground sounds set in an ambisonic background soundscape. Eight participants were able to use this system to successfully design a soundscape that expressed their intentions. It was found that the designed soundscapes seemed to be based more on participant expectation of typical urban soundscapes than on their preference for individual sounds. These results suggest that a more sophisticated soundscape synthesiser might be suitable for real design problems.

Spatiotemporal analysis of an acoustic environment: interactions between landscape features and sounds

So far landscape analysis meant analysis of the spatial pattern of land cover or land use. However, biological organisms do not perceive the landscape only as land cover or land use, but they use all their senses, in order to become familiar with and react to their surroundings. We analyzed the acoustic environment as an additional layer of spatial information in landscape analysis, shortening the monopoly of visual patterns as landscape descriptors. We recorded sounds from a rural protected area into seven categories based on their origin, and examined their spatiotemporal variability and their correlation with landscape characteristics. The sounds were distinguished as Foreground or Background sounds. Foreground sounds correspond to sharp sounds originating near the observer and usually are understood as signals of urgent information, triggering reactions; while background sounds carry information over longer distances and may be used as landmarks to help individuals find their bearing even in the absence of visual signs. We found that the acoustic environment varies both temporally and spatially reflecting anthropogenic, geophysical and biological activities. The spatial pattern of the background sounds correlates, to an extent, with the visually perceived landscape features, but it does not correlate with the spatial pattern of the foreground sounds, which do not correlate strongly with the landscape pattern. This spatial pattern mismatch between acoustic environment and landscape, along with the highly dynamic nature of the acoustic environment compared to the relatively static nature of the land cover and land use spatial pattern highlight a limitation of the classical landscape analysis, and expands our understanding of the cognitive landscape.

Adaptive Characterization of Near and Far field Elements in the Soundscape

Characterization of a soundscape through objective parameters relies on our understanding of psychoacoustics and ability to model the complex signal processing of the mind. Certain physical parameters such as loudness and timbre are easily retrieved from data while other descriptive parameters are more difficult to measure objectively. Several signal processing algorithms are presented here in the context of describing a soundscape in terms of keynote sounds (background noise) and sound signals (foreground sounds). Simulations and stereo field data recorded in San Diego are analyzed. Adaptive matched field processing is used in conjunction with conventional spectral analysis for the detection and categorization of near field events. These sounds are then removed to provide a more accurate description of keynote sounds. Spatial distribution of the soundscape is measured using conventional beamforming algorithms.

The effects of background noise on the neural responses to natural sounds in cat primary auditory cortex

Animal vocalizations in natural settings are invariably accompanied by an acoustic background with a complex statistical structure. We have previously demonstrated that neuronal responses in primary auditory cortex of halothane-anesthetized cats depend strongly on the natural background. Here, we study in detail the neuronal responses to the background sounds and their relationships to the responses to the foreground sounds. Natural bird chirps as well as modifications of these chirps were used. The chirps were decomposed into three components: the clean chirps, their echoes, and the background noise. The last two were weaker than the clean chirp by 13 and 29 dB on average respectively. The test stimuli consisted of the full natural stimulus, the three basic components, and their three pairwise combinations. When the level of the background components (echoes and background noise) presented alone was sufficiently loud to evoke neuronal activity, these background components had an unexpectedly strong effect on the responses of the neurons to the main bird chirp. In particular, the responses to the original chirps were more similar on average to the responses evoked by the two background components than to the responses evoked by the clean chirp, both in terms of the evoked spike count and in terms of the temporal pattern of the responses. These results suggest that some of the neurons responded specifically to the acoustic background even when presented together with the substantially louder main chirp, and may imply that neurons in A1 already participate in auditory source segregation.

Artificial neural network models of sound signals in urban open spaces

Sound signals, known as foreground sounds, are important components of soundscape in urban open spaces. Previous studies in this area have shown that sound preferences are different according to social and demographic factors of individual users [W. Yang and J. Kang, J. Urban Des., 10, 69–88 (2005)]. This study develops artificial neural network (ANN) models of sound signals for architects at design stage, simulating subjective evaluation of sound signals. A database for ANN modeling has been established based on large-scale social surveys in European and Chinese cities. The ANN models have consequently been built, where individual’s social and demographic factors, activities, and acoustic features of the space and sounds are used as input variables while the sound preference is defined as the output. Through the process of training and testing the ANN models, considerable convergences have been achieved, which means that the models can be applied as practical tools for architects to design sound signals in urban open spaces, taking the characteristics of potential users into account. Currently ANN models combining foreground and background sounds are being developed.