Graceful Forgetting II. Data as a Process

Data are rapidly growing in size and importance for society, a trend motivated by their enabling power. The accumulation of new data, sustained by progress in technology, leads to a boundless expansion of stored data, in some cases with an exponential increase in the accrual rate itself. Massive data are hard to process, transmit, store, and exploit, and it is particularly hard to keep abreast of the data store as a whole. This paper distinguishes three phases in the life of data: acquisition, curation, and exploitation. Each involves a distinct process, that may be separated from the others in time, with a different set of priorities. The function of the second phase, curation, is to maximize the future value of the data given limited storage. I argue that this requires that (a) the data take the form of summary statistics and (b) these statistics follow an endless process of rescaling. The summary may be more compact than the original data, but its data structure is more complex and it requires an on-going computational process that is much more sophisticated than mere storage. Rescaling results in dimensionality reduction that may be beneficial for learning, but that must be carefully controlled to preserve relevance. Rescaling may be tuned based on feedback from usage, with the proviso that our memory of the past serves the future, the needs of which are not fully known.Comment: 30 pages, 17 figure

Scanning for oscillations

Objective. Oscillations are an important aspect of brain activity, but they often have a low signal- to-noise ratio (SNR) due to source-to-electrode mixing with competing brain activity and noise. Filtering can improve the SNR of narrowband signals, but it introduces ringing effects that may masquerade as genuine oscillations, leading to uncertainty as to the true oscillatory nature of the phenomena. Likewise, time–frequency analysis kernels have a temporal extent that blurs the time course of narrowband activity, introducing uncertainty as to timing and causal relations between events and/or frequency bands. Approach. Here, we propose a methodology that reveals narrowband activity within multichannel data such as electroencephalography, magnetoencephalography, electrocorticography or local field potential. The method exploits the between-channel correlation structure of the data to suppress competing sources by joint diagonalization of the covariance matrices of narrowband filtered and unfiltered data. Main results. Applied to synthetic and real data, the method effectively extracts narrowband components at unfavorable SNR. Significance. Oscillatory components of brain activity, including weak sources that are hard or impossible to observe using standard methods, can be detected and their time course plotted accurately. The method avoids the temporal artifacts of standard filtering and time–frequency analysis methods with which it remains complementary

A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding

The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on linear mappings between features of sound streams and EEG responses (forward model), or vice versa (backward model). It has been shown that when the envelope of attended speech and EEG responses are used to derive such mapping functions, the model estimates can be used to discriminate between attended and unattended talkers. However, the predictive/reconstructive performance of the models is dependent on how the model parameters are estimated. There exist a number of model estimation methods that have been published, along with a variety of datasets. It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different estimation methods to classify attended speakers from multi-channel EEG data. The performance of the model estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams. We find that when forward models predict the EEG from the attended audio, regularized models do not improve regression or classification accuracies. When backward models decode the attended speech from the EEG, regularization provides higher regression and classification accuracies

Enhancing the Intelligibility of Speech in Speech Noise

A brief introduction to a proposed project on integrating different source separation techniques to improve intelligibility resulting from speech separation from interfering speech

The Spectral Extent of Phasic Suppression of Loudness and Distortion-Product Otoacoustic Emissions by Infrasound and Low-Frequency Tones

We investigated the effect of a biasing tone close to 5, 15, or 30 Hz on the response to higher-frequency probe tones, behaviorally, and by measuring distortion-product otoacoustic emissions (DPOAEs). The amplitude of the biasing tone was adjusted for criterion suppression of cubic DPOAE elicited by probe tones presented between 0.7 and 8 kHz, or criterion loudness suppression of a train of tone-pip probes in the range 0.125–8 kHz. For DPOAEs, the biasing-tone level for criterion suppression increased with probe-tone frequency by 8–9 dB/octave, consistent with an apex-to-base gradient of biasing-tone-induced basilar membrane displacement, as we verified by computational simulation. In contrast, the biasing-tone level for criterion loudness suppression increased with probe frequency by only 1–3 dB/octave, reminiscent of previously published data on low-side suppression of auditory nerve responses to characteristic frequency tones. These slopes were independent of biasing-tone frequency, but the biasing-tone sensation level required for criterion suppression was ~ 10 dB lower for the two infrasound biasing tones than for the 30-Hz biasing tone. On average, biasing-tone sensation levels as low as 5 dB were sufficient to modulate the perception of higher frequency sounds. Our results are relevant for recent debates on perceptual effects of environmental noise with very low-frequency content and might offer insight into the mechanism underlying low-side suppression

Decoding the auditory brain with canonical component analysis

The relation between a stimulus and the evoked brain response can shed light on perceptual processes within the brain. Signals derived from this relation can also be harnessed to control external devices for Brain Computer Interface (BCI) applications. While the classic event-related potential (ERP) is appropriate for isolated stimuli, more sophisticated “decoding” strategies are needed to address continuous stimuli such as speech, music or environmental sounds. Here we describe an approach based on Canonical Correlation Analysis (CCA) that finds the optimal transform to apply to both the stimulus and the response to reveal correlations between the two. Compared to prior methods based on forward or backward models for stimulus-response mapping, CCA finds significantly higher correlation scores, thus providing increased sensitivity to relatively small effects, and supports classifier schemes that yield higher classification scores. CCA strips the brain response of variance unrelated to the stimulus, and the stimulus representation of variance that does not affect the response, and thus improves observations of the relation between stimulus and response

Strategies for voice separation based on harmonicity

cote interne IRCAM: DeCheveigne94c/National audienceStrategies for voice separation based on harmonicit

Pitch perception models

cote interne IRCAM: DeCheveigne03eNone / NoneNational audienceNon