Stacked causal convolutional autoencoder based speech compression method
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
32nd IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2024 -- Tarsus Univ Campus, Mersin, TURKEY
Anahtar Kelimeler
Speech compression, residual vector quantization, convolutional autoencoder, deep learning
Kaynak
32Nd Ieee Signal Processing and Communications Applications Conference, Siu 2024
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
