Multiplex communications – Communication techniques for information carried in plural... – Combining or distributing information via code word channels
Reexamination Certificate
1999-12-03
2004-07-20
Kizou, Hassan (Department: 2662)
Multiplex communications
Communication techniques for information carried in plural...
Combining or distributing information via code word channels
C370S431000, C370S441000, C370S442000, C370S464000, C370S477000, C370S478000, C370S480000, C370S498000
Reexamination Certificate
active
06765930
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a decoding apparatus and method, and a providing medium. More particularly, the present invention relates to a decoding apparatus and method with which the circuit scale is reduced by performing a frequency-time transform after adding signal frequency components together, and a providing medium for providing a program to execute the decoding method in the decoding apparatus.
2. Description of the Related Art
As acoustic data coding systems, transform coding and subband coding, for example, are available. In the transform coding, a signal on the time base is blocked into frames in units of predetermined time, and the signal on the time base for each frame is transformed (spectrum-transformed) into another signal on the frequency base and divided into a plurality of frequency bands, followed by coding for each frequency band. In the subband coding, acoustic data on the time base is divided into a plurality of frequency bands without being divided into frames in units of predetermined time, and is then coded for each frequency band.
Also, a combined coding system of the transform coding and the sub-band coding is proposed. In such a combined coding system, after dividing acoustic data on the time base into a plurality of frequency bands by the subband coding, a signal for each band is spectrum-transformed into another signal on the frequency base, and coding is performed on each signal resulting from the spectrum transform.
A polyphase quadrature filter (PQF), for example, is known as a band dividing filter for use in the subband coding. The PQF has such a feature that it can divide a signal into a plurality of bands with an equal width at a time, and does not generate the so-called aliasing when the divided bands are combined together later.
Further, the above-mentioned spectrum transform for transforming a signal on the time base into another signal on the frequency base is performed, e.g., by dividing acoustic data into frames in units of predetermined time, and carrying out a discrete Fourier transform (DFT), discrete cosine transform (DCT), modified discrete cosine transform (MDCT) or the like for each frame.
Quantizing a signal thus divided with a filter or spectrum transform for each band makes it possible to control the band in which quantization noise occurs. In other words, coding can be made with higher efficiency on the auditory sense by utilizing masking effects, etc. By normalizing a signal component for each band based on a maximum value from among absolute values of signal components prior to the quantization, coding can be achieved with even higher efficiency.
When quantizing each of frequency components (hereinafter referred to as spectral components) divided into a plurality of frequency bands, a band width used for band division is set in consideration of, e.g., the human auditory characteristics. Specifically, acoustic data is generally divided into a plurality of frequency bands (e.g., 25 bands) whose width increases as the frequency increases up to a high frequency band called the critical band. Then, coding of data for each band is performed with bit allocation in predetermined number to each band or bit allocation in number adaptively changed for each band (adaptive bit allocation). In the case of coding, for example, coefficient data obtained by the MDCT processing with the adaptive bit allocation, the coding is performed with bits allocated in number adaptive to the coefficient data for each band obtained by the MDCT processing in units of frame.
The bit allocation is made, for example, based on the magnitude of a signal for each band. With this method, flat quantization noise spectra are obtained and the noise energy is minimized. However, since the masking effects are not utilized, an actual noise feeling is not always optimum on the auditory sense.
As another bit allocation method, there is known fixed bit allocation wherein auditory sense masking is utilized to obtain a required signal to noise ratio for each band. With this method, however, since the bit allocation is fixed even when a characteristic value is measured with a sine wave input, the characteristic value may not exhibit a very good value.
In order to solve those problems with the bit allocation, a high-efficiency coding system is proposed wherein all bits available for the bit allocation are divided into bits which are used for fixed bit allocation pattern determined in advance for each band or block that is obtained by further dividing each band, and bits which are used for bit allocation depending on the magnitude of a signal for each block. Further, the dividing ratio between the former and latter bits is determined based on properties of an input signal, for example, so that the number of bits allocated to the fixed bit allocation pattern is increased as the spectral distribution of the input signal becomes smoother.
With the above method, when energy is concentrated in a particular spectral component such as when a sine wave is inputted, a relatively large number of bits are allocated to the block which includes the spectral component. As a result, the overall signal to noise ratio characteristic can be improved. Generally, since the human auditory sense is very sensitive to a signal having a steep spectral distribution, an improvement of the signal to noise ratio by employment of the above method is effective in improving not only a numerical value as a result of the measurement, but also the sound quality perceived by the auditory sense.
Many other various methods than described above have also been proposed, and the model regarding the auditory sense has been developed in a finer manner.
In the case of employing the DFT or DCT as a method for spectrum-transforming a waveform signal made up of waveform elements (sample data), such as a digital audio signal in time domain, the signal is blocked for each of a number M of sample data, and the spectrum transform is performed for each block using the DFT or DCT. As a result of the spectrum transform for each block, a number M of real number data (coefficient data obtained by the DRT or MDCT processing) independent of one another are obtained. The number M of real number data thus obtained are quantized and then coded to provide coded data.
When decoding the coded data, obtained by the above-described coding process, to reproduce a waveform signal, the coded data is decoded and then dequantized to obtain real number data. The real number data is subjected to an inverse spectrum transform using, e.g., inverse DFT or DCT, for each block corresponding to the block in the coding process, thereby obtaining a waveform element signal. The blocks each represented by the waveform element signal are connected to each other to produce a waveform signal.
The produced waveform signal may be sometimes not satisfactory on the auditory sense because connection distortions occurs upon connection of the blocks and remain in the signal. To lessen the connection distortions between the blocks, the spectrum transform employing the DFT or DCT is usually performed for coding with a number M
1
of sample data shared by each of both adjacent blocks in overlapped fashion.
However, when the spectrum transform is performed with a number M
1
of sample data shared each of both adjacent blocks in overlapped fashion, a number M of real number data is obtained in average for a number (M-M
1
) of sample data. This means that the number of real number data obtained by the spectrum transform is larger than the number of sample data actually used in the spectrum transform. Such a fact that the number of real number data obtained by the spectrum transform is larger than the number of actual sample data is not satisfactory from the point of coding efficiency.
On the other hand, in the case of employing the MDCT as a method for spectrum-transforming a waveform signal made up of sample data, such as a digital audio signal, the spectrum transform is performed using a number 2M of sample da
Kizou Hassan
Logsdon Joe
Sonnenschein Nath & Rosenthal LLP
Sony Corporation
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