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While typical hybrid neural network architectures for automatic speech recognition (ASR) use a context window of frame-based features, this may not be the best approach to capture the wider temporal context, which contains phonetic and linguistic information that is equally important. In this paper, we introduce a system that integrates both the spectral and geometrical shape information from the...
In this paper, we propose a feature-based approach to address the challenging task of recognising overlapping sound events from single channel audio. Our approach is based on our previous work on Local Spectrogram Features (LSFs), where we combined a local spectral representation of the spectrogram with the Generalised Hough Transform (GHT) voting system for recognition. Here we propose to take the...
This paper presents a novel method for audio event classification in overlapping conditions. The method is based on Jump Function Kolmogorov (JFK), a stochastic representation, which is (a) additive, thus the sum of signal and noise yields the sum of their JFKs; (b) sparse, therefore audio events are separable in this domain. The proposed method is an extension of our previous works for classification...
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