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Here, the perception of auditory spatial information as indexed by behavioral measures is linked to brain dynamics as reflected by the N1m response recorded with whole-head magnetoencephalography (MEG). Broadband noise stimuli with realistic spatial cues corresponding to eight direction angles in the horizontal plane were constructed via custom-made,(More)
In this study we describe a binaural auditory model for recognition of speech in the presence of spatially separated noise intrusions, under small-room reverberation conditions. The principle underlying the model is to identify time–fre-quency regions which constitute reliable evidence of the speech signal. This is achieved both by determining the spatial(More)
Sound location processing in the human auditory cortex was studied with magnetoencephalography (MEG) by producing spatial stimuli using a modern stimulus generation methodology utilizing head-related transfer functions (HRTFs). The stimulus set comprised wideband noise bursts filtered through HRTFs in order to produce natural spatial sounds. Neuromagnetic(More)
In an attempt to delineate the assumed 'what' and 'where' processing streams, we studied the processing of spatial sound in the human cortex by using magnetoencephalography in the passive and active recording conditions and two kinds of spatial stimuli: individually constructed, highly realistic spatial (3D) stimuli and stimuli containing interaural time(More)
In this study we describe two techniques for handling convolutional distortion with 'missing data' speech recognition using spectral features. The missing data approach to automatic speech recognition (ASR) is motivated by a model of human speech perception, and involves the modification of a hidden Markov model (HMM) classifier to deal with missing or(More)
Periodicity, which is caused by the vibration of the vocal folds, is an inherent feature of vowel sounds. Whether this periodic structure is reflected in cerebral processing of vowels was addressed via the use of non-invasive brain research methods combined with advanced stimulus production methodology. We removed the contribution of the source of the(More)
The quality and intelligibility of narrowband telephone speech can be enhanced by artifical bandwidth extension. This study combines Gaussian mixture model-based (GMM) mel spectrum extension with a filter bank implementation for generating the missing spectral content in the highband at 4–8 kHz. The narrowband mel spectrum is calculated from input(More)
This work presents an automatic speech recognition system which uses a missing data approach to compensate for environmental noise. The missing, noise-corrupted components are identified using binaural features or a support vector machine (SVM) classifier. To perform speech recognition using the partially observed data, the missing components are(More)
The problem of reverberation in speech recognition is addressed in this study by extending a noise-robust feature enhancement method based on non-negative matrix factor-ization. The signal model of the observation as a linear combination of sample spectrograms is augmented by a mel-spectral feature domain convolution to account for the effects of room(More)