Experimental evaluation of encoding parameters of MPEG-4 ALS for high-resolution audio

Amada, Shota; Kamamoto, Yutaka; Harada, Noboru; Sugiura, Ryo; Moriya, Takehiro; Makino, Shoji

doi:10.1109/gcce.2017.8229373

Cited by 1 publication

(1 citation statement)

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“…WaveNet [14,15], a deep generative model based on autoregressive CNNs, achieves high speech quality but suffers from high computational complexity and long inference times, limiting its real-time applications. Wav-eRNN [16], combining RNNs with ResNet, maintains high-quality speech synthesis with lower complexity than WaveNet. LPCNet [17], a combination of linear predictive coding (LPC) and WaveRNN, excels at low bit-rate speech coding, making it suitable for limited bandwidth applications.…”

Section: Neural Network-based Speech Codingmentioning

confidence: 99%

Gated recurrent unit predictor model-based adaptive differential pulse code modulation speech decoder

Sheferaw,

Mwangi,

Kimwele

et al. 2024

J AUDIO SPEECH MUSIC PROC.

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Speech coding is a method to reduce the amount of data needs to represent speech signals by exploiting the statistical properties of the speech signal. Recently, in the speech coding process, a neural network prediction model has gained attention as the reconstruction process of a nonlinear and nonstationary speech signal. This study proposes a novel approach to improve speech coding performance by using a gated recurrent unit (GRU)-based adaptive differential pulse code modulation (ADPCM) system. This GRU predictor model is trained using a data set of speech samples from the DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus actual sample and the ADPCM fixed-predictor output speech sample. Our contribution lies in the development of an algorithm for training the GRU predictive model that can improve its performance in speech coding prediction and a new offline trained predictive model for speech decoder. The results indicate that the proposed system significantly improves the accuracy of speech prediction, demonstrating its potential for speech prediction applications. Overall, this work presents a unique application of the GRU predictive model with ADPCM decoding in speech signal compression, providing a promising approach for future research in this field.

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