Audio identification method and system based on empirical mode decomposition

Abstract

The invention discloses an audio identification method and system based on empirical mode decomposition. The method comprises the following steps: A, inputting original audio signals, sampling the original audio signals, then obtaining frequency spectrum data by successively performing denoising preprocessing, Hamming windowing and Fourier transformation processing, and then obtaining a sound spectrogram by successive connection with each frame of the frequency spectrum data; B, obtaining an energy maximum point of each frequency range of the sound spectrogram, and generating a time-frequency curve through successive connection with the energy maximum point of each frequency range; C, obtaining multiple intrinsic mode functions by performing the empirical mode decomposition on the generated time-frequency curve; and D, through the obtained multiple intrinsic mode functions, through combination with the corresponding frequency ranges and time frames, generating and outputting multiple feature values used for representing the original audio signals. According to the invention, change trend information of audio features can be fully integrated with generation of the feature values, and the generated feature values can more completely represent the audio signals.

Description

technical field [0001] The invention relates to the field of audio recognition, in particular to an audio recognition method and system based on empirical mode decomposition. Background technique [0002] Audio recognition refers to the spectrum analysis of the audio signal to obtain the spectrum of the audio signal, extract the characteristic value of the audio signal, construct a model or constellation diagram, and perform target matching and identification. The main technologies include short-time Fourier transform, spectrogram feature extraction, feature template generation, etc. [0003] The specific processing of a piece of original audio or speech mostly goes through the following steps: pre-emphasis denoising, framing, windowing processing, fast Fourier transform (FFT), filter bank processing (Mel-FilterBank), discrete Cosine transform DCT (calculation of cepstrum parameters), logarithmic energy, difference cepstrum parameters (vector form, inverse Fourier transform...

AI Technical Summary

Problems solved by technology

[0007] In view of the deficiencies in the prior art above, the purpose of the present invention is to provide an audio recognition method and system based on empirical mode decomposition, aiming to solve the problem that the existing recognition methods cannot completely and fully characterize the audio signal

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