The Effect of Memory Inclusion on Mutual Information Between Speech Frequency Bands

Nour-Eldin, Amr H.; Shabestary, Turaj Zakizadeh; Kabal, P.

doi:10.1109/icassp.2006.1660588

Cited by 12 publications

(17 citation statements)

References 10 publications

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“…[4,12,13], capture memory in the front-end instead, e.g., via delta features or static features from neighbouring frames. Following an investigation of front-end feature extraction for ABE [14], the work in [15][16][17] investigates the merit of memory inclusion through information theoretic analysis. This body of work demonstrates the benefit of memory inclusion through delta features under the constraint of fixed dimensionality.…”

Section: Introductionmentioning

confidence: 99%

Artificial Bandwidth Extension with Memory Inclusion Using Semi-supervised Stacked Auto-encoders

2018

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Artificial bandwidth extension (ABE) algorithms have been developed to improve quality when wideband devices receive speech signals from narrowband devices or infrastructure. The utilisation of contextual information in the form of dynamic features or explicit memory captured from neighbouring frames is common to ABE research, however the use of additional cues augments complexity and can introduce latency. Previous work shows that unsupervised, linear dimensionality reduction techniques help to reduce complexity. This paper reports a semisupervised, non-linear approach to dimensionality reduction using a stacked auto-encoder. In further contrast to previous work, it operates on raw spectra from which a low dimensional narrowband representation is learned in a data-driven manner. Three different objective speech quality measures show that the new features can be used with a standard regression model to improve ABE performance. Improvements in the mutual information between learned features and missing higher frequency components are also observed whereas improvements in speech quality are corroborated by informal listening tests.

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Section: Introductionmentioning

confidence: 99%

Artificial Bandwidth Extension with Memory Inclusion Using Semi-supervised Stacked Auto-encoders

2018

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“…In our recent work [3], we exploited the considerable temporal correlation properties of speech by including memory in Mel Frequency Cepstral Coefficient (MFCC) speech parametrization (through delta features). These features are obtained through linearly weighted differences between neighbouring conventional static feature vectors.…”

Section: Introductionmentioning

confidence: 99%

Objective analysis of the effect of memory inclusion on bandwidth extension of narrowband speech

Nour-Eldin

Kabal

2007

Interspeech 2007

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For the purpose of improving Bandwidth Extension (BWE) of narrowband speech, we continue our recent work on the positive effect of exploiting the temporal correlation of speech on the dependence between speech frequency bands. We have shown that such memory inclusion into MFCC speech parametrization translates into higher highband certainty. In the work presented herein, we employ VQ to estimate highband discrete entropies, thus refining our analysis of the effect of memory inclusion on increasing highband certainty. Moreover, we extend our previous analysis to LSF parameters. We further construct a BWE system that exploits our memory inclusion technique, thus translating highband certainty gains into practical BWE performance improvement as measured by the objective quality of reconstructed speech. Results show that memory inclusion decreases the log-Spectral Distortion of the extended highband speech by as much as 1 dB corresponding to more than 14% relative.

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“…As described in [1], we include memory directly in the representation of spectral envelopes by means of delta coefficients, which are appended to (or replace part of) the MFCC/LSF static features 4 . Delta coefficients are obtained from static vectors by a first-order regression (time-derivative) implemented by calculating linearly weighted differences between neighbouring static vectors per…”

Section: Delta Featuresmentioning

confidence: 99%

“…In our previous work; first introduced in [1] and later extended in [2], we made use of the concept of highband certainty (certainty about the high band given the narrow band); defined in [3] as the ratio of Mutual Information (MI) between the two bands to the discrete entropy of the high band, in order to quantify the correlation between speech frequency bands. Through highband certainty, we investigated the effect of including memory into the frontend on the resulting correlation (by using delta features in addition to the conventional static features which make no use of the considerable temporal correlation properties of speech), as well as the effect of the type of parametrization.…”

Section: Introductionmentioning

confidence: 99%

Combining frontend-based memory with MFCC features for Bandwidth Extension of narrowband speech

Nour-Eldin

Kabal

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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In this paper, we continue our previous work on improving Bandwidth Extension (BWE) of narrowband speech. We have shown that including memory into the parametrization frontend (through delta features) results in higher highband certainty irrespective of feature type, with MFCCs exhibiting higher correlation, in general, between both bands, reaching twice that using LSFs. By incorporating memory into the frontend of a conventional LP-based BWE system, we were able to translate the higher correlation due to memory into BWE performance improvement. Using high-resolution inverse DCT, we also achieved high quality speech reconstruction from MFCCs, thus enabling MFCC-based BWE with improved performance compared to conventional static LP-based BWE. We continue this work by incorporating the superior correlation properties of frontend memory into our MFCC-based BWE system. Log-Spectral Distortion as well as the more perceptually-correlated Itakura-based measures show that incorporating memory into our MFCC-based BWE system results in BWE performance superior to that of our dynamic LP-based BWE system. Index Terms-Bandwidth extension, memory inclusion, highresolution IDCT, highband certainty, mutual information BACKGROUNDIn traditional telephone networks, speech bandwidth is limited to the 0.3-3.4 kHz range. As a result, narrowband speech has sound quality inferior to its wideband counterpart and has reduced intelligibility especially for consonant sounds. Wideband speech reconstruction through Bandwidth Extension (BWE) attempts to regenerate the highband (3.4-7 kHz) signal lost during the filtering processes employed in traditional networks, thereby providing backward compatibility with existing networks. BWE is based on the assumption that narrowband speech correlates with the highband signal, and thus, given some a priori information about the nature of this correlation, the higher frequency speech content can be estimated given only the available narrow band. Most BWE schemes use either codebook mapping or statistical modelling to perform this estimation.Since BWE performance closely follows the correlation available between representations of the narrow and high frequency bands, the premise of our work has been to quantify this correlation for different speech representations in order to adopt those representations with the greatest potential for BWE performance improvement. In our previous work; first introduced in [1] and later extended in [2], we made use of the concept of highband certainty (certainty about the high band given the narrow band); defined in [3] as the ratio of Mutual Information (MI) between the two bands to the discrete entropy of the high band, in order to quantify the correlation between speech frequency bands. Through highband certainty, we investigated the effect of including memory into the frontend on the resulting correlation (by using delta features in addition to the conventional static features which make no use of the considerable temporal correlation properties of speech), as ...

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The Effect of Memory Inclusion on Mutual Information Between Speech Frequency Bands

Cited by 12 publications

References 10 publications

Artificial Bandwidth Extension with Memory Inclusion Using Semi-supervised Stacked Auto-encoders

Artificial Bandwidth Extension with Memory Inclusion Using Semi-supervised Stacked Auto-encoders

Objective analysis of the effect of memory inclusion on bandwidth extension of narrowband speech

Combining frontend-based memory with MFCC features for Bandwidth Extension of narrowband speech

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