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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Okajima, H. Honda, M. Yoshino, R. Matsunaga, N. |
| Copyright Year | 2014 |
| Description | Author affiliation: Grad. Sch. of Sci. & Technol., Kumamoto Univ., Kurokami, Japan (Okajima, H.; Honda, M.; Yoshino, R.; Matsunaga, N.) |
| Abstract | AD/DA conversion has become a core technology in digital signal processing. Signal compression is one of the important matters for AD/DA conversion system. Delta-sigma modulator (DSM) is well known as a effective method for encoding analog signals into digital signals. The data conversion systems are composed of the post-filter and the DSM. It is required to satisfy the low quantization noise and the low distortion characteristics by appropriate design of the filter and DSM. In our studies, we proposed a new design method of data conversion system that includes pre-filter, in addition to the traditional data conversion system. To design easier, an evaluation framework of the data conversion system is proposed. Then, a design algorithm based on the evaluation framework is proposed by using particle swarm optimization algorithm. Post and pre-filters are designed towards the original signal, so as to minimize the effect of noise and distortion due to quantization. In this paper, we verify the AD/DA systems with post and pre-filters by using the voice signal compression system. We evaluate the effectiveness by the simulation. |
| Starting Page | 1388 |
| Ending Page | 1394 |
| File Size | 708743 |
| Page Count | 7 |
| File Format | |
| e-ISBN | 9784907764463 |
| DOI | 10.1109/SICE.2014.6935272 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-09-09 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | SICE |
| Subject Keyword | Noise Quantization (signal) Algorithm design and analysis Data conversion Distortion Cutoff frequency Passband Noise reduction AD/DA converter Delta-Sigma modulator Data compression |
| Content Type | Text |
| Resource Type | Article |
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