Stacked causal convolutional autoencoder based speech compression method

被引：0

作者：

Bekiryazici, Tahir ^{[1
]}

Aydemir, Gurkan ^{[1
]}

Gurkan, Hakan ^{[1
]}

机构：

[1] Bursa Tekn Univ, Elekt Elekt Muhendisligi Bolumu, Bursa, Turkiye

来源：

32ND IEEE SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU 2024 | 2024年

关键词：

Speech compression; residual vector quantization; convolutional autoencoder; deep learning;

D O I：

10.1109/SIU61531.2024.10600779

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This study proposes a speech compression method based on one-dimensional convolutional autoencoder and residual vector quantization. The proposed method offers different compression ratios at low bit rates. Speech quality evaluation metric (PESQ) was used to test the performance of the proposed method. Experimental results show that the proposed method achieves a PESQ value of 1.903 for 2.5 kbps and 2.24 for 5 kbps.

引用

页数：4

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