Konuşma İşaretlerinin Derin Evrişimsel Oto Kodlayıcı ve Artık Vektör Nicemleme Tabanlı Sıkıştırılması

Yıl 2024, , 113 - 124, 30.04.2024

Tahir Bekiryazıcı , Gürkan Aydemir , Hakan Gürkan

https://doi.org/10.46387/bjesr.1452937

Öz

Bu çalışmada, konuşma işaretlerini sıkıştırmak için derin öğrenme tabanlı oto kodlayıcı ve artık vektör nicemlemesini temel alan sıkıştırma yöntemi önerilmiştir. Önerilen sıkıştırma yönteminde, öncelikle giriş konuşma işaretini daha düşük boyutlu bir uzaya atayan oto kodlayıcı kullanılmakta ve ardından oto kodlayıcı çıkışı, artık vektör nicemlemesi ile daha da sıkıştırılmaktadır. Sıkıştırma yöntemi, birbirine paralel çalışan iki farklı kod çözücü yapısı ve iki kod kitapçığı sayesinde farklı oranlarda sıkıştırma oranı sunmaktadır. Yöntemin başarımı konuşma kalitesini algısal değerlendirme metriği kullanılarak TIMIT veri kümesi ile test edilmiştir. Önerilen konuşma sıkıştırma yöntemi, 1.25 ve 2.5 kbps iletim hızları için sırasıyla 1.665 ve 1.985 konuşma kalitesini algısal değerlendirme skorları elde etmiştir.

Anahtar Kelimeler

Konuşma Sıkıştırma, Nedensel Evrişimsel Sinir Ağları, Artık Vektör Nicemlemesi, Derin Oto kodlayıcı

Destekleyen Kurum

Bursa Teknik Üniversitesi

Proje Numarası

230D005

Teşekkür

Bu çalışma Bursa Teknik Üniversitesi Bilimsel Araştırma Projeleri birimi tarafından desteklenmiştir. Proje no: 230D005

Deep Convolutional Autoencoder and Residual Vector Quantization-Based Compression of Speech Signals

Öz

This paper proposes a compression method based on deep convolutional autoencoder and residual vector quantization to compress speech signals. In the proposed method, the first encoder part of an autoencoder is utilized to map the input speech signal to a lower dimensional (code) space, and then the code is further compressed via residual vector quantization. The compression method offers different ratios due to two different decoder structures operating in parallel and the two codebooks. The method's performance is evaluated with the TIMIT dataset using the Perceptual Evaluation of Speech Quality metric. The proposed speech compression method achieved perceptual evaluation of speech quality scores of 1.665 and 1,985 for 1.25 and 2.5 kbps transmission rates, respectively.

Anahtar Kelimeler

Speech Compression, Causal Convolutional Neural Network, Residual Vector Quantization, Deep Autoencoder

Proje Numarası

230D005

Kaynakça

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