Cascaded Cross-Module Residual Learning towards Lightweight End-to-End Speech Coding

Jongmo Sung; Kai Zhen; Minje Kim; Mi Suk Lee; Seungkwon Beack

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Cascaded Cross-Module Residual Learning towards Lightweight End-to-End Speech Coding

Kai Zhen, Jongmo Sung, Mi Suk Lee, Seungkwon Beack, Minje Kim

Audio and Speech Processing · Submitted 18 Jun 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The proposed method shows better objective performance than AMR-WB and the state-of-the-art DNN-based speech codec with a similar network architecture.

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arXiv ID1906.07769 (version 4)

Pith ID4RTCQAWN, Pith's permanent record of this paper and its review

CitationJongmo Sung, Kai Zhen, Minje Kim, Mi Suk Lee, Seungkwon Beack. "Cascaded Cross-Module Residual Learning towards Lightweight End-to-End Speech Coding." arXiv:1906.07769 (2019). https://pith.science/paper/4RTCQAWN

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What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.14 · human participants 0.09 · controlled comparison 0.85 · randomized assignment 0.01 · states quantitative result 0.96 · compares with prior results 0.98 · mentions code or data availability 0.02.

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arXiv listing abstract v4. Results and the main claim are underlined; show every role.

Speech codecs learn compact representations of speech signals to facilitate data transmission. Many recent deep neural network (DNN) based end-to-end speech codecs achieve low bitrates and high perceptual quality at the cost of model complexity. We propose a cross-module residual learning (CMRL) pipeline as a module carrier with each module reconstructing the residual from its preceding modules. CMRL differs from other DNN-based speech codecs, in that rather than modeling speech compression problem in a single large neural network, it optimizes a series of less-complicated modules in a two-phase training scheme. The proposed method shows better objective performance than AMR-WB and the state-of-the-art DNN-based speech codec with a similar network architecture. As an end-to-end model, it takes raw PCM signals as an input, but is also compatible with linear predictive coding (LPC), showing better subjective quality at high bitrates than AMR-WB and OPUS. The gain is achieved by using only 0.9 million trainable parameters, a significantly less complex architecture than the other DNN-based codecs in the literature.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Speech codecs learn compact representations of speech signals to facilitate data transmission.	background 1.00	0.04	0.02	asserted 1.00
s1	Many recent deep neural network (DNN) based end-to-end speech codecs achieve low bitrates and high perceptual quality at the cost of model complexity.	background 0.89	0.06	0.04	asserted 1.00
s2	We propose a cross-module residual learning (CMRL) pipeline as a module carrier with each module reconstructing the residual from its preceding modules.	contribution 0.67	0.90	0.10	asserted 0.99
s3	CMRL differs from other DNN-based speech codecs, in that rather than modeling speech compression problem in a single large neural network, it optimizes a series of less-complicated modules in a two-phase training scheme.	method 0.85	0.78	0.08	asserted 1.00
s4	The proposed method shows better objective performance than AMR-WB and the state-of-the-art DNN-based speech codec with a similar network architecture.	result 1.00	0.94	0.34	asserted 1.00
s5	As an end-to-end model, it takes raw PCM signals as an input, but is also compatible with linear predictive coding (LPC), showing better subjective quality at high bitrates than AMR-WB and OPUS.	result 0.78	0.91	0.14	asserted 1.00
s6	The gain is achieved by using only 0.9 million trainable parameters, a significantly less complex architecture than the other DNN-based codecs in the literature.	result 0.86	0.92	0.28	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.80.

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arXiv listing abstract v4

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full text
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The paper

Original abstract

Speech codecs learn compact representations of speech signals to facilitate data transmission. Many recent deep neural network (DNN) based end-to-end speech codecs achieve low bitrates and high perceptual quality at the cost of model complexity. We propose a cross-module residual learning (CMRL) pipeline as a module carrier with each module reconstructing the residual from its preceding modules. CMRL differs from other DNN-based speech codecs, in that rather than modeling speech compression problem in a single large neural network, it optimizes a series of less-complicated modules in a two-phase training scheme. The proposed method shows better objective performance than AMR-WB and the state-of-the-art DNN-based speech codec with a similar network architecture. As an end-to-end model, it takes raw PCM signals as an input, but is also compatible with linear predictive coding (LPC), showing better subjective quality at high bitrates than AMR-WB and OPUS. The gain is achieved by using only 0.9 million trainable parameters, a significantly less complex architecture than the other DNN-based codecs in the literature.

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3 arXiv papers cite this in 5 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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Compares with it 1 paper

This is unlike [ref] and [ref] which enforce a specific bitrate during training and require training a different model for each target bitrate.

SoundStream: An End-to-End Neural Audio Codec · Related work · 0.98

Cites it as background 3 papers

The recent improvements of deep neural network (DNN) models and learning methods have open new horizons for highly structured sequential data-related tasks, including automatic speech recognition (ASR) ( [ref] ) as well as audio compression ( [ref] , [ref] , [ref] ).

Deep Neural Networks and End-to-End Learning for Audio Compression · Introduction · 0.96
An end-to-end audio codec targeting general audio at high bitrates (i.e., above $64$kbps) was proposed in [ref] .

SoundStream: An End-to-End Neural Audio Codec · Related work · 0.81
In the speech signal processing, analysis/synthesis methods have been developed based on simple autoencoder (AE) [ref] or cascading AE structures [ref] .

Conditional variational autoencoder to improve neural audio synthesis for polyphonic music sound · Introduction · 1.00

What it cites · 5 sentences without a confident label

Uses the method or tool of 1 paper

We use Adam optimizer [ref] with the batch size of 128 frames.

Adam: A Method for Stochastic Optimization · Experimental setup · 1.00

Compares with 1 paper

We first show that for the raw PCM input CMRL outperforms AMR-WB and Kankanahalli-Net in terms of objective metrics in the experimental setup proposed in [ref] , where the use of LPC was not tested.

End-to-End Optimized Speech Coding with Deep Neural Networks · Experiments · 0.94

Cites as background 2 papers

There have been various conventional speech coding methodologies, including linear predictive coding (LPC) [ref] , adaptive encoding [ref] , and perceptual weighting [ref] among other domain specific knowledge about the speech signals, that are used to construct classic codecs, such as AMR-WB [ref] and OPUS [ref] with high perceptual quality.

High-Quality, Low-Delay Music Coding in the Opus Codec · Introduction · 0.98
Many DNN methods [ref] [ref] take inputs in time-frequency (T-F) domain from short time Fourier transform (STFT) or modified discrete cosine transform (MDCT), etc.

Composition of Deep and Spiking Neural Networks for Very Low Bit Rate Speech Coding · Introduction · 0.81

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