We present an algorithm for tracking individual instruments in polyphonic music recordings. The algorithm takes as input the instrument identities of the recording and uses non-negative matrix factorisation to compute an instrument-independent pitch activation function. The Viterbi algorithm is applied to find the most likely path through a number of candidate instrument and pitch combinations ...

In this paper, we describe an application of the Forward-Backwards (F-B) algorithm for maximum likelihood training of hybrid HMM/Bayesian Network (BN) acoustic models. Previously, HMM/BN parameter estimation was based on a Viterbi training algorithm that requires two passes over the training data: one for BN learning and one for updating HMM transition probabilities. In this work, we first anal...

A new decoding algorithm for geomelrically uniform trellis codes exploiting the group structure of the codes is presented. The algorithm complexity does not depend on the number of states of the trellis describing the code. Performance of the algorithm are evaluated through simulation, and its complexity is compared to the Viterbi algorithm.

This paper addresses the problem of detecting keywords in unconstrained speech without explicit modeling of non-keyword segments. The proposed algorithms are based on recent developments in confidence measures using local posterior probabilities, and searches for the segment maximizing the average observation posterior along the most likely path in the hypothesized keyword model. We can also us...

A Markov model for language syntax and its use in a text recognition algorithm is proposed. Syntactic constraints are described by the transition probabilities between classes. The confusion between the feature string for a word and the syntactic classes is also described probabilistically. A modification of the Viterbi algorithm is also proposed that finds a fixed number of sequences of syntac...

While one-dimensional Hidden Markov Models have been very successfully applied to numerous problems, their extension to two dimensions has been shown to be exponentially complex, and this has very much restricted their usage for problems such as image analysis. In this paper we propose a novel algorithm which is able to approximate the search for the best state path (Viterbi decoding) in a 2D H...

This paper proposes a method to re-estimate output visual parameters for speech-to-lip movement synthesis using audio-visual Hidden Markov Models(HMMs) under the Expectation-Maximization(EM) algorithm. In the conventional methods for speech-to-lip movement synthesis, there is a synthesis method estimating a visual parameter sequence through the Viterbi alignment of an input acoustic speech sign...

The proliferation of biological sequence data has motivated the need for an extremely fast probabilistic sequence search. One method for performing this search involves evaluating the Viterbi probability of a hidden Markov model (HMM) of a desired sequence family for each sequence in a protein database. However, one of the difficulties with current implementations is the time required to search...