Audio detecting and classifying method with customization function

Abstract

The invention provides an audio detecting and classifying method with the customization function. According to the audio detecting and classifying method, audio activated detection is conducted on audio data; firstly, a part of original training sets are classified into a plurality of types of training sets according to the types, feature extraction is conducted on each type of training sets, and a Gaussian hybrid model corresponding to each type of training sets and parameters of the Gaussian hybrid model are trained, so that an overall Gaussian hybrid model is obtained; secondly, the other training sets are used as new training samples, and parameter updating is conducted on the overall Gaussian hybrid model so that a local model can be obtained; finally, features of a test set are extracted, a local model classifier is input, and a result is smoothed and output. According to the audio detecting and classifying method with the customization function, through training of the overall Gaussian hybrid model and the training of the local Gaussian hybrid model, the types and the parameters of the Gaussian hybrid models can be updated along with the increase of the number of the samples; through the combination of the audio detecting and classifying method and the classifier, the performance of a system is further improved, and audio detection and classification are achieved finally; the audio detecting and classifying method with the customization function can be widely applied to multiple machine learning fields, such as speaker recognition, voice recognition and human-computer interaction, relating to audio detection and classification.

Description

technical field [0001] The invention belongs to the technical field of audio processing, in particular to an audio detection and classification method with self-defining functions. Background technique [0002] In systems such as audio recognition and speaker recognition, audio activity detection (Voice activity detection, VAD) technology is widely used. It is mainly used to exclude silence and noise signals that are not related to speakers in continuous audio signals, determine the starting point of audio segments and End position to improve the performance of speech recognition and speaker recognition systems. Effective and accurate audio activation detection can improve system recognition performance by removing noise or silent segment signals, reducing system data processing and interference with subsequent audio analysis and processing. Research on audio activation detection algorithms has been carried out for many years. Traditional audio activation detection methods ...

AI Technical Summary

Problems solved by technology

[0011] In order to overcome the above-mentioned shortcoming of prior art, the object of the present invention is to provide a kind of audio frequency detection and classification method with self-defining function, at first part original training set is divided into several kinds of training sets according to type, and feature extraction is carried out for each type of training set , and train the corresponding Gaussian mixture model and its parameters to obtain a global Gaussian mixture model; further use other training sets as new training samples, update the parameters of the global Gaussian mixture model to obtain a local model; finally extract features from the test set , input the local model classifier, and smooth and output the result, its main advantage is to overcome the problem that the original audio activation detection cannot customize multiple categories and make judgments

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