Method for transforming input signal

A technology of input signals and variables, applied in the field of signal processing, can solve problems such as unrealistic independence assumptions

Inactive Publication Date: 2015-06-24
MITSUBISHI ELECTRIC CORP
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Problems solved by technology

However, the row-independence assumption of H is unrealistic, since the activation of a spectral mode at frame n is likely to correlate with the activation of other modes at the previous frame n-1

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  • Method for transforming input signal
  • Method for transforming input signal
  • Method for transforming input signal

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Embodiment Construction

[0063] introduction

[0064] Our embodiments provide models for transforming and processing dynamic (non-stationary) signals and data that have the advantages of HMM and NMF based models.

[0065] The model is characterized by a continuous non-negative state space. Gain adaptation is automatically handled in real-time during inference. The dynamics of the signal were modeled using a linear transfer matrix A. The model is a random variable ε with multiplicative non-negative innovation n non-negative linear dynamical system. The signal may be a non-stationary linear signal (such as an audio or speech signal) or a multidimensional signal. The signal may be represented as data in the digital domain. The innovation random variable is described in more detail below.

[0066] The embodiments also provide applications for using the models. In particular, the model can be used to process audio signals taken from several sources, e.g. the signal is a mixture of speech and noise (...

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Abstract

An input signal, in the form of a sequence of feature vectors, is transformed to an output signal by first storing parameters of a model of the input signal in a memory. Using the vectors and the parameters, a sequence of vectors of hidden variables is inferred. There is at least one vector hn of hidden variables hi,n for each feature vector xn, and each hidden variable is nonnegative. The output signal is generated using the feature vectors, the vectors of hidden variables, and the parameters. Each feature vector xn is dependent on at least one of the hidden variables hi,n for the same n. The hidden variables are related according to h i , n = ∑ j , l   c i , j , l  ɛ l , n  h j , n - 1 , where j and l are summation indices. The parameters include non-negative weights ci,j,l, and εl,n are independent non-negative random variables.

Description

technical field [0001] The present invention relates generally to signal processing, and more particularly to transforming an input signal into an output signal using a dynamics model, wherein the signal is an audio (speech) signal. Background technique [0002] A common framework for modeling dynamics in non-stationary signals is a hidden Markov model (HMM, hidden Markov model) with temporal dynamics. HMM is a de facto standard for speech recognition. Discrete-time HMMs are obtained by combining with unobserved random state variables {h n} for the sequence of N observed (acquired) random variables [0003] def def [0004] {x n}=x 1:N ={x 1 ,x 2 ,...,x N} (ie, signal samples) for modeling. Typically two constraints are defined on an HMM. [0005] First, the state variables have first-order Markov dynamics. This means that p(h n | h 1:n-1 )=p(h n | h n-1 ), where p(h n | h n-1 ) is called the transition probability. Transition probabilities are usually cons...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G10L21/0232G10L21/0216
CPCG10L21/0232G10L2021/02163
Inventor J·R·赫尔歇C·费沃特J·勒鲁克斯
Owner MITSUBISHI ELECTRIC CORP
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