Voice state detection method suitable for echo cancellation system

Abstract

The invention relates to a voice state detection method suitable for an echo cancellation system. The voice state detection method relates to the field of voice interaction technologies based on an IP network. The voice state detection method comprises the steps of: constructing a support vector machine (SVM) classifier by utilizing noise training samples and voice training samples, wherein signals to be detected are far-end and near-end signals after blocking, carrying out VAD judgment on the block far-end signal by adopting the constructed SVM classifier based on a Gaussian mixture model, stopping updating and filtering of a filter and outputting a near-end voice signal directly if the judgment result is that no voice exists, and carrying out double-end conversation judgment when judging that voice exists at a far end; stopping updating coefficients of the filter when in double-end conversation, and filtering the near-end signal; otherwise, conducting coefficient updating and filtering of the filter according to the far-end signal. The voice state detection method improves accuracy of voice activity detection, prevents a double-end mute state from being misjudged to be a double-end conversation state, and prevents error updating and filtering of the filter without a reference signal.

Description

technical field [0001] The invention relates to the technical field of voice interaction based on an IP network, in particular to a voice state detection method suitable for an echo cancellation system. Background technique [0002] Echo cancellation technology is widely used in voice interactive systems based on IP networks such as teleconferencing systems, car bluetooth systems, IP phones, etc., to eliminate the sound played by the speaker after being picked up by the microphone after being propagated through various paths, and transmitted back to the remote end of the system Acoustic echoes formed. The core idea of ​​echo cancellation is to simulate the echo path through an adaptive filter and subtract the estimated echo signal from the signal picked up by the microphone. [0003] Voice state detection plays a vital role in echo cancellation. Before the sound signal enters the filter, it is necessary to judge the current voice state first, and determine the working stat...

AI Technical Summary

Problems solved by technology

[0008] 1. The Geigel algorithm assumes that the near-end voice is much larger than the far-end echo signal, which does not fully conform to the actual situation of echo cancellation, so it is not very accurate in some cases

[0009] 2. Directly performing DTD without performing remote VAD (Voice Activity Detection) may cause the double-end mute state to be misjudged as a double-end call state

[0010] 3. Stop filter coefficient update only in the double-talk state, continuous filtering and coefficient update in the state where the far-end voice does not exist may cause the filter to diverge and incorrectly subtract non-existent from the near-end signal far end voice

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