Audio signal classification method and device

Abstract

An embodiment of the invention discloses an audio signal classification method and device, which are used for carry out classification on input audio signals. The method comprises the following steps: according to sound activity of a current audio frame, determining whether obtaining spectrum fluctuation of the current audio frame and storing the spectrum fluctuation to a spectrum fluctuation storage device, wherein the spectrum fluctuation represents energy fluctuation of frequency spectrum of the audio signals; according to the result of whether the audio frame is a percussion music or the activity of a history audio frame, updating the spectrum fluctuation stored in the spectrum fluctuation storage device; and according to statistics of a part of or all of effective data of the spectrum fluctuation stored in the spectrum fluctuation storage device, classifying the current audio frame into a speech frame or a music frame.

Description

technical field [0001] The invention relates to the technical field of digital signal processing, in particular to an audio signal classification method and device. Background technique [0002] In order to reduce the resources occupied during video signal storage or transmission, the audio signal is compressed at the sending end and then transmitted to the receiving end, and the receiving end restores the audio signal through decompression processing. [0003] In audio processing applications, audio signal classification is a widely used and important technique. For example, in audio codec applications, the more popular codec is a hybrid codec. This codec usually consists of a speech generation model-based coder (such as CELP) and a transform-based coder (such as an MDCT-based coder). At medium and low bit rates, the encoder based on the speech generation model can obtain better speech encoding quality, but the encoding quality for music is relatively poor, while the tran...

AI Technical Summary

Problems solved by technology

[0006] The disadvantage of this speech / music classifier is that on the one hand, the absolute recognition rate of music still needs to be improved; room for improvement

[0008] Although this type of classifier has a high theoretical basis, it usually has high computational or storage complexity, and the implementation cost is high.

Images