Kalle J. Palomäki

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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–frequency regions which constitute reliable evidence of the speech signal. This is achieved both by determining the spatial(More)
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)
A simulation of the acoustics of a simple rectangular prism room has been constructed using the MATLAB m-code programming language. The aim of this program (Roomsim) is to provide a signal generation tool for the speech and hearing research community, and an educational tool for illustrating the image method of simulating room acoustics and some acoustical(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)
In this study we describe an auditory processing front-end for missing data speech recognition, which is robust in the presence of reverberation. The model attempts to identify time-frequency regions that are not badly contaminated by reverberation and have strong speech energy. This is achieved by applying reverberation masking. Subsequently, reliable(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)
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)
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)
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 factorization. The signal model of the observation as a linear combination of sample spectrograms is augmented by a melspectral feature domain convolution to account for the effects of room(More)