Artificial Bandwidth Expansion Method to Improve Intelligibility and Quality of AMR-Coded Narrowband Speech

Laaksonen, Laura; Kontio, Jyrki; Alku, Paavo

doi:10.1109/icassp.2005.1415237

Cited by 10 publications

(10 citation statements)

References 2 publications

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“…The present authors have worked on ABE for several years and proposed algorithms that have been evaluated with subjective listening tests (e.g., Laaksonen et al, 2005;Kontio et al, 2007;Laaksonen et al, 2009;Pulakka et al, 2008;Pulakka and Alku, 2011) The results have been consistent and promising, indicating that ABE improves both the quality and intelligibility of narrowband speech. In addition, listening tests in three different languages (English, Mandarin Chinese, and Russian) did not show substantial differences in the ABE performance between the examined languages (Pulakka et al, 2008).…”

Section: Introductionmentioning

confidence: 51%

“…The second method, ABE2 (Pulakka and Alku, 2011), uses a neural network to estimate the mel spectrum of the highband. Pulakka and Alku (2011) reported subjective listening tests that indicated that ABE2 performs better than the earlier algorithm version of ABE1 described by Laaksonen et al (2005).…”

Section: Abe Algorithmsmentioning

confidence: 98%

“…The first method, ABE1, introduced by Laaksonen et al (2011), is based on spectral folding and weighting of the highband in the time domain. Several listening tests have been conducted during the last years to evaluate the performance of the method and its earlier versions (Laaksonen et al, 2005;Pulakka et al, 2008;Laaksonen et al, 2009Laaksonen et al, , 2011. The second method, ABE2 (Pulakka and Alku, 2011), uses a neural network to estimate the mel spectrum of the highband.…”

Section: Abe Algorithmsmentioning

confidence: 99%

“…Several different forms of subjective listening tests have therefore been utilized in evaluating the ABE methods. These include paired comparison tests (Park and Kim, 2000), absolute category rating tests with the mean opinion score (MOS) (Laaksonen et al, 2005;Gustafsson et al, 2006), and comparison category rating tests with the comparison mean opinion score (CMOS) (Iser and Schmidt, 2005;Pulakka et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Conversational quality evaluation of artificial bandwidth extension of telephone speech

Pulakka

Laaksonen

Yrttiaho

et al. 2012

The Journal of the Acoustical Society of America

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Artificial bandwidth extension methods have been developed to improve the quality and intelligibility of narrowband telephone speech and to reduce the difference with wideband speech. Such methods have commonly been evaluated with objective measures or subjective listening-only tests, but conversational evaluations have been rare. This article presents a conversational evaluation of two methods for the artificial bandwidth extension of telephone speech. Bandwidth-extended narrowband speech is compared with narrowband and wideband speech in a test setting including a simulated telephone connection, realistic conversation tasks, and various background noise conditions. The responses of the subjects indicate that speech processed with one of the methods is preferred to narrowband speech in noise, but wideband speech is superior to both narrowband and bandwidth-extended speech. Bandwidth extension was found to be beneficial for telephone conversation in noisy listening conditions.

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Section: Introductionmentioning

confidence: 51%

Section: Abe Algorithmsmentioning

confidence: 98%

Section: Abe Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Conversational quality evaluation of artificial bandwidth extension of telephone speech

Pulakka

Laaksonen

Yrttiaho

et al. 2012

The Journal of the Acoustical Society of America

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“…Relevant information about the spectral envelope of the extension band is extracted from narrow band speech. Other approaches forego the LPC analysis and copy the narrow band speech spectrum [15].…”

Section: Related Workmentioning

confidence: 99%

Statistical Inference of Missing Speech Data in the ICA Domain

Rosca

Gerkmann

Balcan

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings

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We address the problem of speech estimation as statistical estimation with "missing" data in the independent component analysis (ICA) domain. Missing components are substituted by values drawn from "similar" data in a multi-faceted ICA representation of the complete data. The paper presents the algorithm for the inference of missing data in the case of a fixed pattern of missing data. We apply our approach to the problem of bandwidth extension, or where speech is degraded by a fixed filtering process and show the capability of the algorithm to reconstruct fine missing details of the original data with little artifacts. The evaluation is done using objective distortion measures on speech samples from the NTT database.

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