A new algorithm for translating psycho-acoustic information to the wavelet domain

Adaptive Variable Degree- Zero-Trees for Re-Encoding of Perceptually Quantized Wavelet Packet Transformed Audio and High-Quality Speech

“…Figure 2 shows how the signal bandwidth is decomposed to 19 relatively critical subbands in the region of 0-7 kHz. The difference between our method and other similar ones is the combination of WP outputs in different levels of wavelet expansion tree wherever a perfect match with the critical bands is not possible (sub-bands [14][15][16][17][18][19]. These 19 critical sub-bands are summarized in Table 1 used the new wavelet kernel exploited in [6].…”

“…To avoid this, we act as follows. We calculate the average energy of the coefficients of the narrow-band region (sub-bands [3][4][5][6][7][8][9][10][11][12][13][14][15][16]. If this is smaller than a given value e 1 , we add one bit to the number of the allocated bits of the narrow sub-bands whose average energies are smaller than a defined value e 2 .…”

An enhanced psychoacoustic model based on the discrete wavelet packet transform

“…The computation of the psychoacoustic model has a general structure similar to several related techniques [14][15][16][17], and it is illustrated in Fig. 1.…”

“…In [16], Zurera et al presented a new algorithm to effectively translate psychoacoustic model information into the wavelet domain, even when low-selectivity filters were used to implement the wavelet transform or wavelet packet decomposition. They first calculated the masking and auditory thresholds in the frequency domain by using the Fourier transform.…”

“…The above psychoacoustic modeling methods are either computationally expensive [13,14,16,18] by relying on the Fourier transforms for the computation of the psychoacoustic model, or approximate the critical bands sub-optimally [15,17], which may result in objectionable audible distortion in the reconstructed signal. In contrast, it has been shown [19] that the discrete wavelet transform (DWT) requires a significantly lower computational load when compared both to the discrete cosine transform (DCT) or the discrete Fourier transform (DFT) while supporting psychoacoustically based models, and therefore it presents an attractive alternative.…”