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Storage and transmission of spatial sound signals
Published in Bosun Xie, Spatial Sound, 2023
In the second type of transformation, the discrete time samples of the input signal are divided into block or frames with an appropriate length, and short-term discrete orthogonal transform is used to convert each block of time samples into spectral coefficients in the transform domain (such as in the frequency domain or more strictly in the time-frequency domain). Generally, various short-term discrete orthogonal transforms exhibit higher-frequency resolution and lower time resolution. A well-known short-term discrete orthogonal transform is the short-term Fourier transform (STFT) in Equation (8.3.15). However, modified discrete cosine transform (MDCT) is often used in spatial sound signal coding, such as Dolby Digital coding described in Section 13.6.1. The advantage of MDCT is that the power of a signal is dominated by the preceding spectral components, which are beneficial to signal compression. In addition to STFT and MDCT, other short-term discrete orthogonaltransforms, which yield the coefficients in the transform domain, are applicable to audio coding.
The Modified Discrete Cosine Transform
Published in Humberto Ochoa-Domínguez, K. R. Rao, Discrete Cosine Transform, 2019
Humberto Ochoa-Domínguez, K. R. Rao
The MDCT is a linear transformation that takes 2Ninputs and produces N outputs, which is designed to be applied to a sequence of 50%-overlapping blocks of a longer sequence (e.g., audio samples). Because this is non-square (fewer outputs than inputs), the IMDCT is not an “inverse” transformation in the usual sense; it only recovers the original data when IMDCTs of overlapping blocks are added (by “time-domain aliasing cancellation”).
Dolby AC-3 Audio Coding
Published in S. Merrill Weiss, Issues in Advanced Television Technology, 1996
It is also claimed for the MDCT transform that several memory and computationally efficient techniques are available for its implementation, making it more cost effective than other methods. Bits are allocated to each sub-band based upon a comparison with the log-spectral envelope computed for each audio block, calculated on a critical-band frequency scale.
Analysis of the application of college popular music education relying on the elite teaching optimization algorithm
Published in Applied Artificial Intelligence, 2023
MDCT is a fully reconstructed linear transformation, but it is not a fully orthogonal transformation. The input time domain signal first passes through a suitable window function and then undergoes MDCT transformation. There will be a 50% overlap between every two adjacent input signals, and they will be connected in sequence. Because the orthogonal change is generally performed in groups, and the coding of each group of coefficients is generally performed independently, the effects of quantization errors on successive groups are also different. In addition, due to the fixed discontinuity at the boundary of the quadrature, there may be a lot of noise at the boundary of these packets, but the human ear is particularly sensitive to this kind of noise. The expression of MDCT is as follows:
Detection of AAC compression using MDCT-based features and supervised learning
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2022
José Juan García-Hernández, Wilfrido Gómez-Flores
Lossy compression is typically achieved by quantising transform coefficients of the signals (Malvar, 1992). In this sense, the Modified Discrete Cosine Transform Coefficients (MDCT) are widely used in most current lossy audio compression schemes. The MDCT is a Fourier-related transform based on the type-IV discrete cosine transform (Luo et al., 2014).