In this paper we propose an extension of the very low bit-rate speech coding technique, exploiting predictability of the temporal evolution of spectral envelopes, for wide-band audio coding applications. Temporal enve...
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ISBN:
(纸本)9781424407286
In this paper we propose an extension of the very low bit-rate speech coding technique, exploiting predictability of the temporal evolution of spectral envelopes, for wide-band audio coding applications. Temporal envelopes in critically band-sized sub-bands are estimated using frequency domain linear prediction applied on relatively long time segments. The sub-band residual signals, which play an important role in acquiring high quality reconstruction, are processed using a heterodyning-based signal analysis technique. For reconstruction, their optimal parameters are estimated using a closed-loop analysis-by-synthesis technique driven by a perceptual model emulating simultaneous masking properties of the human auditory system. We discuss the advantages of the approach and show some properties on challenging audio recordings. The proposed technique is capable of encoding high quality, variable rate audio signals on bit-rates below 1bit/sample.
In this paper we extend a lossy compression technique for surface EMG signals, which is based on the Algebraic Code Excited linear Prediction (ACELP) paradigm, to compress multi-channel surface EMG recordings by explo...
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ISBN:
(纸本)1424407281
In this paper we extend a lossy compression technique for surface EMG signals, which is based on the Algebraic Code Excited linear Prediction (ACELP) paradigm, to compress multi-channel surface EMG recordings by exploiting the correlation between the Line Spectral Frequencies (LSF). Experimental results show that the LSFs of the inner signals in a multi-channel recording can be efficiently represented with 13 bit/frame, versus the 38 bit/frame needed by independent ACELP coding of each signal, thus saving 66% of the bandwidth needed to transmit these coefficients while maintaining comparable performance in terms of the SNR, Average Rectified Value and Root Mean Square of the waveform, and mean and median frequencies of the power spectrum.
Obstructive sleep apnea (OSA) is a sleep disordered breathing that affects about 4% of adult men and 2% of adult women in the world. It is caused by a collapse of the upper airway during sleep, which could lead to man...
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ISBN:
(纸本)9783540368397
Obstructive sleep apnea (OSA) is a sleep disordered breathing that affects about 4% of adult men and 2% of adult women in the world. It is caused by a collapse of the upper airway during sleep, which could lead to many serious consequences, such as high risk of cardiovascular morbidity and even mortality. The gold standard for OSA diagnosis is the Polysomnography (PSG), which requires patients to stay overnight in a sleep laboratory, and attached to numerous physiologic sensors. Therefore, a full PSG diagnosis can be time consuming, inconvenient, and costly. Snoring is generated by the vibrating soft tissue in the upper airway, and it is the earliest symptom of OSA. Thus, snore signals may provide an excellent framework for non-invasive diagnosis of OSA. In this paper, the use of formant features of snore signals for OSA diagnosis is presented. The raw snore signals were preprocessed using a modified Normalized Least-Mean-Square (NLMS) adaptive filter for noise cancellation, and subsequently modeled using linear predictive coding (LPC) for spectra analysis. Acoustical changes due to the collapsing upper airways can be reflected on the formant features in the frequency spectrum, which were extracted to discriminate between benign and apneic snores. Results show that the formant features of snore signals carry useful information for OSA diagnosis, and demonstrate that the use of snore signals can be a convenient, inexpensive, and reliable diagnostic approach for mass screening of OSA.
We explore the performance of two dimensional (2-D) prediction based LSF quantization method for both wide-band and telephone-band (narrow-band) speech. The 2-D prediction based method exploits both the inter-frame an...
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We explore the performance of two dimensional (2-D) prediction based LSF quantization method for both wide-band and telephone-band (narrow-band) speech. The 2-D prediction based method exploits both the inter-frame and intra-frame correlations of LSF parameters. We show that a 4th order 2-D predictor provides optimum prediction gain as well as improved quantization performance at various choices of frame shift for both wide-band and telephone-band speech. Existing one dimensional (1-D) predictive method, exploiting only inter-frame correlation, results in poor performance at larger frame shifts; whereas proposed 2-D predictor provides lower spectral distortion as well as lower number of outliers compared to existing memory-based and memory-less methods.
linear Prediction with Low-frequency Emphasis (LPLE), an all-pole modeling technique which emphasizes the lower frequency range of the input signal, was described by Alku and Backstrom [1]. The method is based on firs...
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ISBN:
(纸本)9781424410286
linear Prediction with Low-frequency Emphasis (LPLE), an all-pole modeling technique which emphasizes the lower frequency range of the input signal, was described by Alku and Backstrom [1]. The method is based on first interpreting conventional linearpredictive (LP) analyses of successive prediction orders with parallel structures using the concept of symmetric linear prediction. The experiments presented in this work are aimed to show that the LPLE method is well-suited for those speech processing and enhancement applications, where low-order all-pole models with improved modeling of the lowest formants are needed. This is done by replacing the LP block with a LPLE block. Distortion measures using Itakura distance and noise cancellation using a Wiener "type" filter were explored, utilizing the LPLE coefficients instead of LP coefficients.
This paper presents a text independent speaker identification system using multi-band features with artificial neural network. linearpredictive cepstrum coefficients (LPCCs) computed from sub-band signals with higher...
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ISBN:
(纸本)9781424415502
This paper presents a text independent speaker identification system using multi-band features with artificial neural network. linearpredictive cepstrum coefficients (LPCCs) computed from sub-band signals with higher order statistics (HOS) are employed as the main features to represent the speaker characteristics. The multi-band representation of the speech signal is implemented by empirical mode decomposition (EMD). Dominant feature vectors are derived by applying principal component analysis (PCA) on LPCC space computed from the speech signal. The experimental results show that the proposed system improves the speaker identification performance. The efficiency is also compared for different features with noisy speech signals.
We propose an extension of ADPCM that includes adaptive pre- and post-filtering to achieve spectral shaping of the coding noise. The advantage of this coding scheme is that it allows a realization without algorithmic ...
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ISBN:
(纸本)9781424407286
We propose an extension of ADPCM that includes adaptive pre- and post-filtering to achieve spectral shaping of the coding noise. The advantage of this coding scheme is that it allows a realization without algorithmic delay by making the filters backwards-adaptive. The measurements we present indicate that the addition of adaptive pre- and post-filtering to ADPCM results in a significant improvement in perceived audio quality. We therefore believe that the proposed system is a viable way to near-transparent lossy audio coding without algorithmic delay.
Data compression techniques have extensive applications in power-con strained digital communication systems, such as in the rapidly-developing domain of wireless sensor network applications. This paper explores energy...
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ISBN:
(纸本)1424407281
Data compression techniques have extensive applications in power-con strained digital communication systems, such as in the rapidly-developing domain of wireless sensor network applications. This paper explores energy consumption tradeoffs associated with data compression, particularly in the context of lossless compression for acoustic signals. Such signal processing is relevant in a variety of sensor network applications, including surveillance and monitoring. Applying data compression in a sensor node generally reduces the energy consumption of the transceiver at the expense of additional energy expended in the embedded processor due to the computational cost of compression. This paper introduces a methodology for comparing data compression algorithms in sensor networks based on the figure of merit DIE, where D is the amount of data (before compression) that can be transmitted under a given energy budget E for computation and communication. We develop experiments to evaluate, using this figure of merit, different variants of linear predictive coding. We also demonstrate how different models of computation applied to the embedded software design lead to different degrees of processing efficiency, and thereby have significant effect on the targeted figure of merit.
This study combined behavioral performance, eventrelated potential (ERP) and explored the differences between positive expression and negative expression in face domain. A "study-test" pattern was adopted. A...
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