Summary statistics in auditory perception

McDermott, Josh H.; Schemitsch, Michael; Simoncelli, Eero P.

doi:10.1038/nn.3347

Cited by 220 publications

(322 citation statements)

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“…For audition, perceptual learning has indeed been observed through distributed changes in ''tonotopic'' frequency maps, using pure tones [12,13]. As noise has a flat spectrum on average, a distributed code could not rely on tonotopy, but it could possibly recruit more-complex timbre maps [14,15]. In contrast, a local code posits dramatic changes at the single-neuron or small-network level, with small neural populations expressing the full benefit of learning [7,16].…”

Section: Resultsmentioning

confidence: 99%

Perceptual Learning of Acoustic Noise Generates Memory-Evoked Potentials

et al. 2015

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Experience continuously imprints on the brain at all stages of life. The traces it leaves behind can produce perceptual learning [1], which drives adaptive behavior to previously encountered stimuli. Recently, it has been shown that even random noise, a type of sound devoid of acoustic structure, can trigger fast and robust perceptual learning after repeated exposure [2]. Here, by combining psychophysics, electroencephalography (EEG), and modeling, we show that the perceptual learning of noise is associated with evoked potentials, without any salient physical discontinuity or obvious acoustic landmark in the sound. Rather, the potentials appeared whenever a memory trace was observed behaviorally. Such memory-evoked potentials were characterized by early latencies and auditory topographies, consistent with a sensory origin. Furthermore, they were generated even on conditions of diverted attention. The EEG waveforms could be modeled as standard evoked responses to auditory events (N1-P2) [3], triggered by idiosyncratic perceptual features acquired through learning. Thus, we argue that the learning of noise is accompanied by the rapid formation of sharp neural selectivity to arbitrary and complex acoustic patterns, within sensory regions. Such a mechanism bridges the gap between the short-term and longer-term plasticity observed in the learning of noise [2, 4-6]. It could also be key to the processing of natural sounds within auditory cortices [7], suggesting that the neural code for sound source identification will be shaped by experience as well as by acoustics.

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Section: Resultsmentioning

confidence: 99%

Perceptual Learning of Acoustic Noise Generates Memory-Evoked Potentials

et al. 2015

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“…Families of such synthetic natural-like sounds can then be used to isolate the specific natural feature that is particularly important for understanding behavioral or neural responses. This approach has been used, for example, to elucidate the natural sound features critical for phonotaxis in crickets [11], sound texture perception in humans [31,76] and selectivity for conspecific songs in songbirds [9,15].…”

Section: Methods For Estimating Strfs Using Natural Soundsmentioning

confidence: 99%

“…Sound mixtures also appear to be processed separately from isolated sound signals: whereas the short time detail of isolated sound signals is perceived with high accuracy (allowing for example rather extreme rates of phoneme perception), sound mixtures are perceived and categorized in terms of their long-term statistical properties yielding percepts of sound "texture", defined as the collective result of many similar acoustic events (e.g. rainstorms, insect swarms) [31].…”

Section: Statistics Of Sound Mixturesmentioning

confidence: 99%

Neural processing of natural sounds

2014

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Natural sounds have unique statistical structure that makes them perceptually salient. The auditory system is finely tuned to this natural structure. Selective neurons found at the higher levels of the auditory system respond selectively to vocalizations used in communication. On-line Summary1. Natural sounds include animal vocalizations, environmental sounds such as wind, water and fire noises and non-vocal sounds made by animals and humans for communication. These natural sounds have characteristic statistical properties that make them perceptually salient and that drive auditory neurons in optimal regimes for information transmission. 2.Recent advances in statistics and computer sciences have allowed neuro-physiologists to extract the stimulus-response function of complex auditory neurons from responses to natural sounds. These studies have shown a hierarchical processing that leads to the neural detection of progressively more complex natural sound features and have demonstrated the importance of the acoustical and behavioral contexts for the neural responses.3. High-level auditory neurons have shown to be exquisitely selective for conspecific calls.This fine selectivity could play an important role for species recognition, for vocal learning in songbirds and, in the case of the bats, for the processing of the sounds used in echolocation. Research that investigates how communication sounds are categorized into behaviorally meaningful groups (e.g. call types in animals, words in human speech) remains in its infancy. Animals and humans also excel at separating communication sounds from each otherand from background noise. Neurons that detect communication calls in noise have been found but the neural computations involved in sound source separation and natural auditory scene analysis remain overall poorly understood. Thus, future auditory research will have to focus not only on how natural sounds are processed by the auditory system but also on the computations that allow for this processing to occur in natural listening situations. 5.The complexity of the computations needed in the natural hearing task might require a high-dimensional representation provided by ensemble of neurons and the use of natural sounds might be the best solution for understanding the ensemble neural code.The final publication is available at Nature via http://www.nature.com/nrn/journal/v15/n6/full/nrn3731.html PrefaceWe might be forced to listen to a high frequency tone at our audiologist's office or we might enjoy falling asleep with a white-noise machine but the sounds that really matter to us are the voices of our companions or music from our favorite radio station; the auditory system has evolved to process behaviorally relevant natural sounds. Research has shown not only that our brain is optimized for natural hearing tasks but also that using natural sounds to probe the auditory system is the best way to understand the neural computations that allow us to comprehend speech or appreciate music.

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“…Recent psychological studies reveal that "Humans use summary statistics to perceive auditory sequences" [9], [10]. Although aforementioned descriptive statistics can be employed as off-the-shelf tools to "summarize" acoustic scenes, human auditory system is functioning differently.…”

Section: Introductionmentioning

confidence: 99%

Discriminant kernel learning for acoustic scene classification with multiple observations

Kobayashi

Tsuda

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, we propose a novel kernel learning scheme for acoustic scene classification using multiple short-term observations. The method takes inspiration from the recent result of psychological research -"Humans use summary statistics to perceive auditory sequences"; we endeavor to devise computational framework imitating such important auditory mechanism for acoustic scene parsing. Conventional schemes usually encode spectro-temporal patterns with a compact feature vector by timeaveraging, e.g. in Gaussian Mixture models (GMM). However, such integration may not be the ideal, since the arithmetic mean is vulnerable to extreme outliers which can be generated by sounds irrelevant to scene category. In this work, an effective scheme has been developed to exploit rich discriminant information from multiple short-term observations of an acoustic scene. Concretely, we first segment audio recording into short slices, e.g. 2 seconds; one vector can be extracted from each slice consisting of descriptive features. Then, we employ the resultant feature matrix to represent an acoustic scene. Since discriminant information of an acoustic scene can be characterized by either global structure or local patterns, we perform heterogeneous kernel analysis in hybrid feature spaces. Moreover, we conditionally fuse the twoway discriminant information to achieve better classification. The proposed method is validated using DCASE2016 challenge dataset. Experimental results demonstrated the effectiveness of our approach.

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Summary statistics in auditory perception

Cited by 220 publications

References 50 publications

Perceptual Learning of Acoustic Noise Generates Memory-Evoked Potentials

Perceptual Learning of Acoustic Noise Generates Memory-Evoked Potentials

Neural processing of natural sounds

Discriminant kernel learning for acoustic scene classification with multiple observations

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