Abstract
In this paper a new audio coding scheme combining the Hilbert transform and the Empirical Mode Decomposition (EMD) is introduced. Based on the EMD, the coding is fully data-driven approach. Audio signal is first decomposed adaptively, by EMD, into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs). The key idea of this work is to code both instantaneous amplitude (IA) and instantaneous frequency (IF), of the extracted IMFs, calculated using Hilbert transform. Since IA (resp. IF) is strongly correlated, it is encoded via a linear prediction technique. The decoder recovers the original signal by superposition of the demodulated IMFs. The proposed approach is applied to audio signals, and the results are compared to those obtained by AAC (Advanced Audio Coding) and MP3 codecs, and wavelets based compression. Coding performances are evaluated using the bit rate, Objective Difference Grade (ODG) and Noise to Mask Ratio (NMR) measures. Based on the analyzed audio signals, overall, our coding scheme performs better than wavelet compression, AAC and MP3 codecs. Results also show that this new scheme has good coding performances without significant perceptual distortion, resulting in an ODG in range [-1,0] and large negative NMR values.