BLIND BANDWIDTH EXTENSION BASED ON CONVOLUTIONAL AND RECURRENT DEEP NEURAL NETWORKS

被引：0

作者：

Schmidt, Konstantin ^{[1
]}

Edler, Bernd ^{[1
]}

机构：

[1] Int Audio Labs Erlangen, Am Wolfsmantel 33, D-91058 Erlangen, Germany

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2018年

关键词：

Blind Bandwith Extension; Artificial Bandwidth Extension; Speech Coding; Audio Coding; DNN; regressive DNN; LSTM; CNN;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

A blind bandwidth extension (BBWE) expands the bandwidth of telephone speech which often is limited to 0.2 to 3.4 kHz. The advantage is an increased perceived quality as well as an increased intelligibility. This work presents a BBWE similar to state-of-the-art bandwidth extensions like Intelligent Gap Filling with the difference that all processing is done in the decoder without the need of transmitting extra bits. Parameters like spectral envelope are estimated by a regressive Convolutional Deep Neuronal Network (CNN) with long short-term memory (LSTM). The system operates on frames of 20 ms without additional algorithmic delay and can be applied in state-of-the-art speech and audio codecs.

引用

页码：5444 / 5448

页数：5

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