Search results for "Speech processing"
showing 10 items of 210 documents
Automata and differentiable words
2011
We exhibit the construction of a deterministic automaton that, given k > 0, recognizes the (regular) language of k-differentiable words. Our approach follows a scheme of Crochemore et al. based on minimal forbidden words. We extend this construction to the case of C\infinity-words, i.e., words differentiable arbitrary many times. We thus obtain an infinite automaton for representing the set of C\infinity-words. We derive a classification of C\infinity-words induced by the structure of the automaton. Then, we introduce a new framework for dealing with \infinity-words, based on a three letter alphabet. This allows us to define a compacted version of the automaton, that we use to prove that ev…
Quantum Finite Automata and Logics
2006
The connection between measure once quantum finite automata (MO-QFA) and logic is studied in this paper. The language class recognized by MO-QFA is compared to languages described by the first order logics and modular logics. And the equivalence between languages accepted by MO-QFA and languages described by formulas using Lindstrom quantifier is shown.
The Phagocyte Lattice of Dyck Words
2006
We introduce a new lattice structure on Dyck words. We exhibit efficient algorithms to compute meets and joins of Dyck words.
On the Class of Languages Recognizable by 1-Way Quantum Finite Automata
2007
It is an open problem to characterize the class of languages recognized by quantum finite automata (QFA). We examine some necessary and some sufficient conditions for a (regular) language to be recognizable by a QFA. For a subclass of regular languages we get a condition which is necessary and sufficient. Also, we prove that the class of languages recognizable by a QFA is not closed under union or any other binary Boolean operation where both arguments are significant.
Probabilities to Accept Languages by Quantum Finite Automata
1999
We construct a hierarchy of regular languages such that the current language in the hierarchy can be accepted by 1-way quantum finite automata with a probability smaller than the corresponding probability for the preceding language in the hierarchy. These probabilities converge to 1/2.
Finite State Transducers with Intuition
2010
Finite automata that take advice have been studied from the point of view of what is the amount of advice needed to recognize nonregular languages. It turns out that there can be at least two different types of advice. In this paper we concentrate on cases when the given advice contains zero information about the input word and the language to be recognized. Nonetheless some nonregular languages can be recognized in this way. The help-word is merely a sufficiently long word with nearly maximum Kolmogorov complexity. Moreover, any sufficiently long word with nearly maximum Kolmogorov complexity can serve as a help-word. Finite automata with such help can recognize languages not recognizable …
Size of Quantum Finite State Transducers
2007
Sizes of quantum and deterministic finite state transducers are compared in the case when both quantum and deterministic finite state transducers exist. The difference in size may be exponential.
Measurement of ultra-low heating rates of a single antiproton in a cryogenic Penning trap
2019
Physical review letters 122(4), 043201 (2019). doi:10.1103/PhysRevLett.122.043201
Inducing the Lyndon Array
2019
In this paper we propose a variant of the induced suffix sorting algorithm by Nong (TOIS, 2013) that computes simultaneously the Lyndon array and the suffix array of a text in $O(n)$ time using $\sigma + O(1)$ words of working space, where $n$ is the length of the text and $\sigma$ is the alphabet size. Our result improves the previous best space requirement for linear time computation of the Lyndon array. In fact, all the known linear algorithms for Lyndon array computation use suffix sorting as a preprocessing step and use $O(n)$ words of working space in addition to the Lyndon array and suffix array. Experimental results with real and synthetic datasets show that our algorithm is not onl…
Environment Sound Classification using Multiple Feature Channels and Attention based Deep Convolutional Neural Network
2020
In this paper, we propose a model for the Environment Sound Classification Task (ESC) that consists of multiple feature channels given as input to a Deep Convolutional Neural Network (CNN) with Attention mechanism. The novelty of the paper lies in using multiple feature channels consisting of Mel-Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), the Constant Q-transform (CQT) and Chromagram. Such multiple features have never been used before for signal or audio processing. And, we employ a deeper CNN (DCNN) compared to previous models, consisting of spatially separable convolutions working on time and feature domain separately. Alongside, we use atten…