Pulse or digital communications – Pulse code modulation
Reexamination Certificate
1995-07-26
2003-11-11
Tran, Khai (Department: 2631)
Pulse or digital communications
Pulse code modulation
C375S354000
Reexamination Certificate
active
06647063
ABSTRACT:
BACKGROUND OF THE INVENTION
This invention relates to a method and apparatus for encoding input signals by high-efficiency encoding, a recording medium having the high efficiency encoded signals recorded thereon and a method and apparatus for decoding encoded signals transmitted over a transmission channel or reproduced from a recording medium to produce playback signals.
There exist a variety of high efficiency encoding techniques of encoding audio or speech signals. Examples of these techniques include transform coding in which a frame of digital signals representing the audio signal on the time axis is pre-set time units or frames and the frame-based time-axis audio signals are converted by an orthogonal transform into a block of spectral coefficients representing the audio signal on the frequency axis, and a sub-band coding in which the frequency band of the audio signal is divided by a filter bank into a plurality of sub-bands without forming the signal into frames along the time axis prior to coding. There is also known a combination of sub-band coding and transform coding, in which digital signals representing the audio signal are divided into a plurality of frequency ranges by sub-band coding, and transform coding is applied to each of the frequency ranges.
Among the filters for dividing a frequency spectrum into a plurality of equal-width frequency ranges include the quadrature mirror filter (QMF) as discussed in R. E. Crochiere, Digital Coding of Speech in Sub-bands, 55 Bell Syst. Tech J. No.8 (1976). With such QMF filter, the frequency spectrum of the signal is divided into two equal-width bands. With the QMF, aliasing is not produced when the frequency bands resulting from the division are subsequently combined together.
In “Polyphase Quadrature Filters—A New Subband Coding Technique”, Joseph H. Rothweiler ICASSP 83, Boston, there is shown a technique of dividing the frequency spectrum of the signal into equal-width frequency bands. With the present polyphase QMF, the frequency spectrum of the signals can be divided at a time into plural equal-width frequency bands.
There is also known a technique of orthogonal transform including dividing the digital input audio signal into frames of a predetermined time duration, and processing the resulting frames using a discrete Fourier transform (DFT), discrete cosine transform (DCT) and modified DCT (MDCT) for converting the signal from the time axis to the frequency axis. Discussions on MDCT may be found in J. P. Princen and A. B. Bradley, Subband Transform Coding Using Filter Bank Based on Time Domain Aliasing Cancellation”, ICASSP 1987.
By quantizing the signals divided on the band basis by the filter or orthogonal transform, it becomes possible to control the band subjected to quantization noise and psychoacoustically more efficient coding may be performed by utilizing the so-called masking effects. If the signal components are normalized from band to band with the maximum value of the absolute values of the signal components, it becomes possible to effect more efficient coding.
In a technique of quantizing the spectral coefficients resulting from an orthogonal transform, it is known to use sub bands that take advantage of the psychoacoustic characteristics of the human auditory system. That is, spectral coefficients representing an audio signal on the frequency axis may be divided into a plurality of critical frequency bands. The width of the critical bands increase with increasing frequency. Normally, about 25 critical bands are used to cover the audio frequency spectrum of 0 Hz to 20 kHz. In such a quantizing system, bits are adaptively allocated among the various critical bands. For example, when applying adaptive bit allocation to the spectral coefficient data resulting from MDCT, the spectral coefficient data generated by the MDCT within each of the critical bands is quantized using an adaptively allocated number of bits. There are presently known the following two bit allocation techniques.
For example, in IEEE Transactions of Acoustics, Speech and Signal Processing, vol. ASSP-25, No.4, August 1977, bit allocation is carried out on the basis of the amplitude of the signal in each critical band. This technique produces a flat quantization noise spectrum and minimizes the noise energy, but the noise level perceived by the listener is not optimum because the technique does not effectively exploit the psychoacoustic masking effect.
In the bit allocation technique described in M. A. Krassner, The Critical Band Encoder—Digital Encoding of the Perceptual Requirements of the Auditory System, ICASSP 1980, the psychoacoustic masking mechanism is used to determine a fixed bit allocation that produces the necessary signal-to-noise ratio for each critical band. However, if the signal-to-noise ratio of such a system is measured using a strongly tonal signal, for example, a 1 kHz sine wave, non-optimum results are obtained because of the fixed allocation of bits among the critical bands.
For overcoming these inconveniences, a high efficiency encoding apparatus has been proposed in which the total number of bits available for bit allocation is divided between a fixed bit allocation pattern pre-set for each small block and a block-based signal magnitude dependent bit allocation, and the division ratio is set in dependence upon a signal which is relevant to the input signal such that the smoother the signal spectrum, the higher becomes the division ratio for the fixed bit allocation pattern.
With this technique, if the energy is concentrated in a particular spectral component, as in the case of a sine wave input, a larger number of bits are allocated to the block containing the spectral component, for significantly improving the signal-to-noise characteristics in their entirety. Since the human auditory system is highly sensitive to a signal having acute spectral components, such technique may be employed for improving the signal-to-noise ratio for improving not only measured values but also the quality of the sound as perceived by the ear.
In addition to the above techniques, a variety of other techniques have been proposed, and the model simulating the human auditory system has been refined, such that, if the encoding device is improved in its ability, encoding may be made with higher efficiency in light of the human auditory system.
FIG. 1
shows a structural example of an encoding apparatus (encoder) for an acoustic waveform signal.
In this figure, a waveform signal I
101
, entering an input terminal
10
, is converted by a transform circuit
11
into a signal frequency component I
102
and subsequently normalized and quantized by a normalization/quantization circuit
13
, with the aid of the quantization step information I
103
as found by a quantization step decision circuit
12
.
The normalization/quantization circuit
13
outputs the normalization coefficient information I
104
and the encoded signal frequency component I
105
to a code string generating circuit
14
. The code string generating circuit
14
generates, from the quantization step information I
103
, normalization coefficient information I
104
and the encoded signal frequency I
105
, a code string I
106
, which is outputted at an output terminal
16
.
FIG. 2
shows an illustrative arrangement of the converting circuit
11
shown in FIG.
1
.
Referring to
FIG. 2
, an input waveform signal I
201
corresponding to the input waveform signal I
101
and supplied via a terminal
20
from the input terminal
10
, is split by a first-stage spectrum splitting filter
21
into two frequency band signals I
202
, I
203
. That is, the bandwidth of each of the two frequency band signals I
202
, I
203
is one-half of the bandwidth of the input waveform signal I
201
, that is, each frequency band signal I
202
, I
203
is sub-sampled by one-one-half the input waveform signal I
201
. The remaining signal I
203
, divided by the spectrum splitting filter
22
, is further split by the frequency splitting filter
22
into two band signals I
204
, I
205
. That is, the bandwidth of
Sonnenschein Nath & Rosenthal LLP
Sony Corporation
Tran Khai
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