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Unsupervised Noise Estimation and Speech Enhancement Method Based on Separable Deep Autocoding Technique

An automatic encoding and speech enhancement technology, applied in speech analysis, instruments, etc., can solve problems such as affecting effects and mismatching

Active Publication Date: 2018-11-13
PLA UNIV OF SCI & TECH
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Problems solved by technology

However, in the case of unknown noise, or the characteristics of the unknown noise are very different from the known noise, there will be a mismatch problem, which will affect its effect

Method used

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  • Unsupervised Noise Estimation and Speech Enhancement Method Based on Separable Deep Autocoding Technique
  • Unsupervised Noise Estimation and Speech Enhancement Method Based on Separable Deep Autocoding Technique
  • Unsupervised Noise Estimation and Speech Enhancement Method Based on Separable Deep Autocoding Technique

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Embodiment

[0090] The application principle of the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0091] combine figure 1 , the implementation process of the unsupervised noise estimation and speech enhancement method based on the separable deep automatic coding technology of the present invention is as follows.

[0092] S101, randomly select 500 sentences of speech from different genders and different speakers from the English classic database TIMIT, sample them at 8kHz, and use a frame shift of 64ms with a window length of 8ms as the parameter frame, and then do a fast Fu of 512 points Liye transform, after taking the modulus, extract their amplitude spectrum S;

[0093] S102, then implement non-negative matrix decomposition to S, and train a non-negative speech dictionary D capable of representing speech signals, wherein the size of the dictionary is the number of basis functions selected as 2000;

[0094] ...

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Abstract

The invention discloses an unsupervised noise estimation and speech enhancement method based on a separable deep automatic encoding technology. The method comprises a step of prior processing and a step of speech enhancement on unknown noise pollution. The method can be flexibly applied to various speech processing scenes. The method is not constrained by a language of a speech content, changes of a speaker, and a kind of noise. Compared with classical stationarity assumption-based spectral estimation algorithm SS (Spectrum Subtraction) and MMSE (Minimum Mean Square Error), the method does not rely on stationarity assumption and can accurately estimate spectrums for stationary or abrupt noise. Compared with algorithm based on a hidden Markov model and a linear prediction coefficient, the method does not need to specify the type of the processed non-stationary noise. Compared with a noise estimation method based on a low rank structure, noise in the method does not need to have a low-rank repeated structure.

Description

technical field [0001] The invention belongs to the technical field of speech signal processing, in particular to an unsupervised noise estimation and speech enhancement method based on separable deep automatic coding technology. Background technique [0002] Speech enhancement is of great significance both for improving the auditory effect of the speech signal and as a front-end processing to improve the performance of the speech recognizer. The core issue of speech enhancement is the separation of speech noise. The ideal speech enhancement technology needs to be able to achieve good results under the premise of unknown noise. For this reason, a key problem that needs to be solved in speech enhancement is the problem of noise estimation. In order to estimate the noise spectrum, some classical algorithms have been proposed, such as Spectrum Subtraction (SS), Minimum Mean Square Error (MMSE), etc., and have been widely used in voice communication. However, these methods are...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G10L21/0208G10L19/008
Inventor 孙蒙李轶南张雄伟王艺敏邹霞贾冲李莉
Owner PLA UNIV OF SCI & TECH
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