Scalable superwideband extension for wideband coding

Tammi, Mikko; Laaksonen, Laura; Rämö, Anssi; Toukomaa, Henri

doi:10.1109/icassp.2009.4959545

Cited by 10 publications

(11 citation statements)

References 9 publications

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“…Carrying out spectrum replication in the Modified Discrete Cosine Transform domain (MDCT) forms an efficient bandwidth extension algorithm as described in [18]. Using MDCT makes it relatively easy to detect similar shapes and patterns in the spectrum for high quality replication.…”

Section: Super Wideband and Fullbandmentioning

confidence: 99%

“…With suitable scaling applied for each selected subband, the high-frequency half can be reproduced from the lowfrequency half without the need to transmit the actual high-frequency half of the signal. In [18], the search for the best match is done in two steps to facilitate an optimal match: first in the linear domain to match the spectral amplitude peaks and then in the logarithmic domain to provide a perceptually better match with the finer details of the spectral shape. Highly periodic tonal signals that have clear energy peaks would need an unrealistically high number of subbands for accurate spectrum replication.…”

Section: Super Wideband and Fullbandmentioning

confidence: 99%

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Media coding for the next generation mobile system LTE

Järvinen

Bouazizi

Laaksonen

et al. 2010

Computer Communications

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Section: Super Wideband and Fullbandmentioning

confidence: 99%

Section: Super Wideband and Fullbandmentioning

confidence: 99%

Media coding for the next generation mobile system LTE

Järvinen

Bouazizi

Laaksonen

et al. 2010

Computer Communications

Self Cite

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“…However, for general signals, using the MDCT for signal analysis [5], [14], such as evaluating energy, delay, and correlations, is still not straightforward.…”

Section: Modified Discrete Cosine Transformmentioning

confidence: 99%

An overview of MDCT for Time Domain Aliasing Cancellation

Raj¹,

Dhas²,

Gnanadurai³

2014

2014 International Conference on Communication and Network Technologies

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Modified Discrete Cosine Transform (MDCT) is a upcoming special transform implemented in areas of audio signal processing and compression. As, the name implies it is the modified form of DCT (Discrete Cosine Transform) that allows overlapping of segments (say 50%) and thereby it helps in avoiding artifacts. MDCT in general is a lapped transform that rectifies the problem of TDAC (Time Domain Aliasing Cancellation) and hence widely used in audio codec (.mp3, .wav) applications. The scope of this paper is to use MDCT in a system that overcomes the problem of TDAC and implementation of different windows those are capable with this transform. Finally, we calculate the MSE (Mean Squared Error) for the system with different inputs stating that perfect audio reconstruction is possible.

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“…SWB references were produced with the G.729.1 Annex E at 36 kbit/s. The core layer of G.729.1 Annex E with the bit rate of 32 kbit/s is fully identical to ITU-T G.729.1 to reproduce the WB audio signals, and the SWB extension layer with the additional bit rate of 4 kbit/s adopts a two-mode BWE method [34] in the modified discrete cosine transform (MDCT) domain to extend the bandwidth of the reproduced audio to 14 kHz. The mode selection is done by estimating the tonality of the input audio signals.…”

Section: Test Data and Reference Methodsmentioning

confidence: 99%

Audio bandwidth extension based on temporal smoothing cepstral coefficients

Liu

Bao

2014

J AUDIO SPEECH MUSIC PROC.

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In this paper, we propose a wideband (WB) to super-wideband audio bandwidth extension (BWE) method based on temporal smoothing cepstral coefficients (TSCC). A temporal relationship of audio signals is included into feature extraction in the bandwidth extension frontend to make the temporal evolution of the extended spectra smoother. In the bandwidth extension scheme, a Gammatone auditory filter bank is used to decompose the audio signal, and the energy of each frequency band is long-term smoothed using minima controlled recursive averaging (MCRA) in order to suppress transient components. The resulting 'steady-state' spectrum is processed by frequency weighting, and the temporal smoothing cepstral coefficients are obtained by means of the power-law loudness function and cepstral normalization. The extracted temporal smoothing cepstral coefficients are fed into a Gaussian mixture model (GMM)-based Bayesian estimator to estimate the high-frequency (HF) spectral envelope, while the fine structure is restored by spectral translation. Evaluation results show that the temporal smoothing cepstral coefficients exploit the temporal relationship of audio signals and provide higher mutual information between the low-and high-frequency parameters, without increasing the dimension of input vectors in the frontend of bandwidth extension systems. In addition, the proposed bandwidth extension method is applied into the G.729.1 wideband codec and outperforms the Mel frequency cepstral coefficient (MFCC)-based method in terms of log spectral distortion (LSD), cosh measure, and differential log spectral distortion. Further, the proposed method improves the smoothness of the reconstructed spectrum over time and also gains a good performance in the subjective listening tests.

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Scalable superwideband extension for wideband coding

Cited by 10 publications

References 9 publications

Media coding for the next generation mobile system LTE

Media coding for the next generation mobile system LTE

An overview of MDCT for Time Domain Aliasing Cancellation

Audio bandwidth extension based on temporal smoothing cepstral coefficients

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