An audio recognition method and system based on empirical mode decomposition

Abstract

A method and a system for audio recognition based on empirical mode decomposition. The method comprises the following steps: A. loading an original audio signal, sampling the original audio signal, then implementing a preliminary denoising treatment, applying a Hamming window, and performing a Fourier transform in that order to obtain spectral density data, then connecting the spectral density data of every frame to obtain a spectrogram (S101); B. obtaining, from the spectrogram, a point with the highest energy intensity at every frequency band, and then connecting the points with the highest energy intensity at each of the frequency bands to generate a time-frequency curve (S102); C. implementing empirical mode decomposition of the time-frequency curve generated, and obtaining a plurality of intrinsic mode functions (S103); and D. generating, by means of the plurality of intrinsic mode functions combined with a corresponding frequency band and time frame, a plurality of eigenvalues representing the original audio signal, then exporting the eigenvalues (S104). The invention fully integrates change and trend data of an audio feature to generate an eigenvalue, allowing the generated eigenvalue to provide a more comprehensive representation of an audio signal.

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-Filter Bank), Discrete cosine transform DCT (calculation of cepstral parameters), logarithmic energy, difference cepstral parameters (vector form, inverse Fourier transfor...

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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