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Classic approaches to multi-channel signal enhancement rely on model assumptions regarding speech source relative transfer functions and noise covariance matrix, or on estimates thereof obtained in, e.g., speech pauses. To alleviate these constraints, we here investigate an approach to adaptive estimation of the speech (target) source and noise related acoustic parameters based on localized speech...
In this paper we investigate methods to predict word error rates in automatic speech recognition in the presence of unknown noise types, which have not been seen during training. The performance measures operate on phoneme posteriorgrams that are obtained from neural nets. We compare average frame-wise entropy as a baseline measure to the mean temporal distance (M-Measure) and to the number of phonetic...
In many applications of machine listening it is useful to know how well an automatic speech recognition system will do before the actual recognition is performed. In this study we investigate different performance measures with the aim of predicting word error rates (WERs) in spatial acoustic scenes in which the type of noise, the signal-to-noise ratio, parameters for spatial filtering, and the amount...
Estimating non-linearities in phase differences between channel pairs of a multi-channel audio recording in a reverberant environment provides more precise spatial information that yields direct improvement in signal enhancement, as we show for the case of source separation. In this study, we propose an online method for estimating inter-channel phase differences (IPDs) that do not linearly depend...
Sound source localization algorithms commonly include assessment of inter-sensor (generalized) correlation functions to obtain direction-of-arrival estimates. Here, we present a classification-based method for source localization that uses discriminative support vector machine-learning of correlation patterns that are indicative of source presence or absence. Subsequent probabilistic modeling generates...
Estimation of non-linearities in phase differences between two or more channels of an audio recording leads to a more precise spatial information in audio signal enhancement applications. In this work, we propose the estimation of these non-linearities in multi-channel, multi-source audio mixtures in reverberant environments. For this task, we compute short term cross-correlation functions between...
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