Work

Stacked causal convolutional autoencoder based speech compression method

Year: 2024

Type: conference-paper

Abstract: 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.

Source: Signal Processing and Communications Applications Conference (SIU)

DOI: 10.1109/siu61531.2024.10600779

Authors: Tahir Bekіryazici, Gürkan Aydemіr, Hakan Gürkan

Institution: Bursa Technical University

Language: English

Topic: Speech and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4400908489

FWCI:

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