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AssigneeMicrosoft Corporation
InventorsAlejandro Acero, Li Deng, James G. Droppo
Estimating the term…
A method and apparatus estimate additive noise in a noisy signal using an iterative technique within a recursive framework. In particular, the noisy signal is divided into frames and the noise in each frame is determined based on the noise in another frame and the noise determined in a previous iteration for the current frame. In one particular embodiment, the noise found in a previous iteration for a frame is used to define an expansion point for a Taylor series approximation that is used to estimate the noise in the current frame. In one embodiment, noise estimation employs a recursive-Expectation-Maximization framework with a maximum likelihood (ML) criteria. In a further embodiment, noise estimation employs a recursive-Expectation-Maximization framework based on a MAP (maximum a posterior) criteria.
A method for estimating noise in a noisy signal, the method comprising:
A computer-readable medium having computer-executable instructions for performing steps comprising:
A method of estimating noise in a current frame of a noisy signal, the method comprising:
Dependencies are inferred from the claim text.
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Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its…:…
Speech enhancement, e.g. noise reduction or echo cancellation: noise filtering
Noise filtering: characterised by the method used for estimating noise
Speech analysis techniques or speech synthesis
Term end not estimated
The present invention relates to noise estimation. In particular, the present invention relates to estimating noise in signals used in pattern recognition.
A pattern recognition system, such as a speech recognition system, takes an input signal and attempts to decode the signal to find a pattern represented by the signal. For example, in a speech recognition system, a speech signal (often referred to as a test signal) is received by the recognition system and is decoded to identify a string of words represented by the speech signal.
Input signals are typically corrupted by some form of noise. To improve the performance of the pattern recognition system, it is often desirable to estimate the noise in the noisy signal.
FIG. 1 is a block diagram of one computing environment in which the present invention may be practiced.
FIG. 2 is a block diagram of an alternative computing environment in which the present invention may be practiced.
FIG. 3 is a flow diagram of a method of estimating noise under one embodiment of the present invention.
FIG. 1 illustrates an example of a suitable computing system environment 100 on which the invention may be implemented. The computing system environment 100 is only one, example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the invention. Neither should the computing environment 100 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment 100.
The invention is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with the invention include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, telephony systems, distributed computing environments that include any of the above systems or devices, and the like.
The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Tasks performed by the programs and modules are described below and with the aid of figures. Those skilled in the art can implement the description and figures as computer-executable instructions, which can be embodied on any form of computer readable media discussed below.