Line Spectrum Pair Research Articles

Novel Leakage Detection and Localization Method Based on Line Spectrum Pair and Cubic Interpolation Search

Leakage detection in water distribution systems (WDS) is critical to ensuring the security of urban water supplies. Acoustic detection methods have been used for leakage detection in water utilities, but ambient noise in real cases interferes with their detection accuracy, and the localization process in meshed or looped pipe networks requires significant computational costs. To increase the effectiveness of acoustic detection methods in practical applications, the current work proposes a novel leakage detection and localization method. This method extracts line spectrum pairs (LSP) of leakage signals and uses a random forest (RF) model to detect leaks; then, a cubic interpolation search (CIS) algorithm is developed to locate leaks. The LSP-based leakage detection method shows a clear advantage over the detection methods based on linear prediction coefficients (LPC) and time or frequency domain features. The proposed leakage detection method achieves 99.45% accuracy. In the case of −5 dB, the detection accuracy reaches 93.89%. The CIS algorithm is found to be more stable and shows a faster convergence speed than a commonly used graph-based search algorithm. The localization error is low (i.e., 2.22 m to 9.99 m). The LSP-CIS combined algorithm provides a more effective solution for leakage detection and localization.

Multiple Vowels Repair Based on Pitch Extraction and Line Spectrum Pair Feature for Voice Disorder.

Individuals, such as voice-related professionals, elderly people and smokers, are increasingly suffering from voice disorder, which implies the importance of pathological voice repair. Previous work on pathological voice repair only concerned about sustained vowel /a/, but multiple vowels repair is still challenging due to the unstable extraction of pitch and the unsatisfactory reconstruction of formant. In this paper, a multiple vowels repair based on pitch extraction and Line Spectrum Pair feature for voice disorder is proposed, which broadened the research subjects of voice repair from only single vowel /a/ to multiple vowels /a/, /i/ and /u/ and achieved the repair of these vowels successfully. Considering deep neural network as a classifier, a voice recognition is performed to classify the normal and pathological voices. Wavelet Transform and Hilbert-Huang Transform are applied for pitch extraction. Based on Line Spectrum Pair (LSP) feature, the formant is reconstructed. The final repaired voice is obtained by synthesizing the pitch and the formant. The proposed method is validated on Saarbrücken Voice Database (SVD) database. The achieved improvements of three metrics, Segmental Signal-to-Noise Ratio, LSP distance measure and Mel cepstral distance measure, are respectively 45.87%, 50.37% and 15.56%. Besides, an intuitive analysis based on spectrogram has been done and a prominent repair effect has been achieved.

Fast Computation of LSP Frequencies Using the Bairstow Method

Linear prediction is the kernel technology in speech processing. It has been widely applied in speech recognition, synthesis, and coding, and can efficiently and correctly represent the speech frequency spectrum with only a few parameters. Line Spectrum Pairs (LSPs) frequencies, as an alternative representation of Linear Predictive Coding (LPC), have the advantages of good quantization accuracy and low spectral sensitivity. However, computing the LSPs frequencies takes a long time. To address this issue, a fast computation algorithm, based on the Bairstow method for computing LSPs frequencies from linear prediction coefficients, is proposed in this paper. The algorithm process first transforms the symmetric and antisymmetric polynomial to general polynomial, then extracts the polynomial roots. Associated with the short-term stationary property of speech signal, an adaptive initial method is applied to reduce the average iteration numbers by 26%, as compared to the statics in the initial method, with a Perceptual Evaluation of Speech Quality (PESQ) score reaching 3.46. Experimental results show that the proposed method can extract the polynomial roots efficiently and accurately with significantly reduced computation complexity. Compared to previous works, the proposed method is 17 times faster than Tschirnhus Transform, and has a 22% PESQ improvement on the Birge-Vieta method with an almost comparable computation time.

Fast Adaptive Reconstruction Algorithm for Line Spectrum Pair Parameters Based on Compressed Sensing

Line Spectrum Pair Parameters (LSF) are important parameters in low rate speech coding. Implementing LSF transparent quantization with as few bits as possible is a research hotspot in the field of low-rate speech coding. Based on the theory of compressed sensing, this paper proposes a new adaptive reconstruction algorithm. At the encoding end, the algorithm performs frame type discrimination on the LSF of single frame or superframe after voice activation detection and then applies the compressed sensing method to observe and quantize the LSF parameters of the single frame or superframe. At the decoding end, parameters are adaptively selected according to every single frame or superframe type, so that the number of bits required to reconstruct the LSF is reduced. The experimental results show that the proposed algorithm can reduce the number of bits required for LSF reconstruction and improve the reconstruction quality under the appropriate codebook storage and search complexity.

A Pathological Multi-Vowels Recognition Algorithm Based on LSP Feature

At present, pathological voice recognition is mainly based on the classification of pathological voice. However, almost all the researches are based on the single vowel \a samples, but few on multi-vowels. In addition, the current researches on multi-vowels recognition are mainly for normal voices, which are unsuitable for the speech recognition of normal and pathological multi-vowels simultaneously. This paper concentrates on developing an accurate and robust feature called enhanced-bark line spectrum pair (E-BLSP) to detect and classify normal and pathological multi-vowels. We explore the impact of E-BLSP feature on recognition performance and propose an effective method based on the combination of three features including E-BLSP for pathological and normal multi-vowels. In this paper, first LSP and difference of adjacent LSP (DAL) features of a vowel are extracted. Then LSP feature is warped at bark domain to get bark line spectrum pair (BLSP). In addition, then E-BLSP feature is calculated by adjusting BLSP using DAL feature. Finally, the adjusted E-BLSP feature and other two traditional features, including linear prediction cepstrum coefficient (LPCC) and mel-frequency cepstrum coefficients (MFCC) are applied to support vector machine (SVM) and deep neural network (DNN) classifiers to explore the classification performance of single feature and feature combinations for pathological and normal vowels /a/, /i/ and /u/. The results show that the highest achieved accuracies for DNN and SVM network are 98.6190% and 96.2693%, while the largest achieved area under curves (AUC) are 0.9925 and 0.9868, correspondingly with the combination of three features including LPCC, MFCC, and E-BLSP.

Method for the Maximization of the Likelihood Function of Speech Autoregressive Parameters Based on Line Spectrum Pairs

The paper considers the estimation of the parameters of auto­regressive model at additive white noise background. The principle of maximum likelihood is used for this purpose. The main goal is to find the maximum of likelihood function depending on parameters of autoregressive model. Representation of likelihood function through line spectrum pairs and other alternative parameters is presented. This provided possibility of likelihood function maximization by KNITRO algorithm. The presence of multiple local minima of the considered likelihood function is shown. Experimental results including the comparison with widely used expectation-maximization (EM) method are presented for the real speech signals.

An efficient perceptual hashing based on improved spectral entropy for speech authentication

Combined with the linear prediction-minimum mean squared error (LP-MMSE), an efficient perceptual hashing algorithm based on improved spectral entropy for speech authentication was proposed in this paper. The linear prediction analysis is conducted on speech signal after preprocessing, framing and adding windows, and obtained the minimum mean squared error coefficient matrix. And then, the spectral entropy parameter matrix of each frame is calculated by using improved spectral entropy method. And the final binary perceptual hashing sequence is generated based on the above two matrices, and the speech authentication is completed. Comparing the experimental results of combining the Teager energy operator (TEO) with the linear predictive coefficients (LPC), LP-MMSE and line spectrum pair (LSP) coefficient respectively, it can be seen that the proposed algorithm had a good compromise between robustness, discrimination and authentication efficiency, and the proposed algorithm can meet the requirement of real-time speech authentication in speech communication. Experimental results show that the proposed algorithm was better than other existing methods in compactness.

Low-Complexity Hardware Design for Fast Solving LSPs With Coordinated Polynomial Solution

This paper presents a low-complexity algorithm and the corresponding hardware based on the coordinated polynomial solutions for solving line spectrum pairs (LSPs). To improve the computation of LSPs, the enhanced Tschirnhaus transform (ETT) is proposed to accelerate the coordinated polynomial solution. The proposed ETT can replace fractional multiplication with addition and shift operations, so unnecessary operations are avoided. To further simplify the hardware of the ETT, three designs are presented: the preprocessing block (PPB), the iterative root-finding block (IRFB), and the closed-form solution block (CFSB). The PPB provides a design with less gate counts that can effectively transform LPCs into general-form polynomials. Such polynomials can be further decomposed into roots using the proposed IRFB based on the Birge-Vieta method. A pipeline-recursive framework is implemented in the IRFB to save calculations. To improve hardware utilization, this paper also analyzes the coefficients relationship of the ETT by introducing the data dependency graph to design the proposed functional blocks in CFSB. The experimental results show that the proposed hardware achieves a 40-fold improvement in throughput and reduces 1.16% of gate counts at the hardware synthesis level; the chip area is 1.29 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The precision analysis indicates the average log spectral distance is 0.310. Moreover, the ETT in the proposed hardware only requires 29.9% of multiplication compared with the original one. Such results reveal that the proposed work is superior to the baseline work, thereby demonstrating the effectiveness of the proposed design.

LSP (Line Spectrum Pair): Essential Technology for High-compression Speech Coding

LSP (Line Spectrum Pair): Essential Technology for High-compression Speech Coding

Frequency Warping for Speaker Adaptation in HMM-based Speech Synthesis

Speaker adaptation in speech synthesis transforms a source utterance to a target utterance that differs from the source in terms of voice characteristics. In this paper, we employ vocal tract length normalization, which is generally used in speech recognition to remove individual speaker characteristics, to speaker adaptation in speech synthesis. We propose a frequency warping approach based on a time-varying bilinear function to reduce the weighted spectral distance between the source speaker and the target speaker. The warped spectra of the source speaker are then converted to line spectrum pairs to train hidden Markov models (HMM). HMMs are further adapted by algorithms based on maximum likelihood linear regression with the target speaker's data. The experimental results show that our frequency warping approach can make the warped spectra of the source speaker closer to the target speaker, and the resultant adapted HMMs perform better than the HMMs trained by unwrapped spectra in terms of synthesized speech naturalness and speaker similarity.

Binary Search Vector Quantization

This paper proposes a fast search algorithm for vector quantization (VQ) based on a binary search space (BSS-VQ). The trade-off and learning aspects (TLA) were used to enhance the line spectrum pair (LSP) encoder of the G.729 standard. In the trade-off aspect, a slight loss occurred in the quantization quality; however, substantial computational savings were achieved. In the learning aspect, the binary search space was developed using he learning process, which uses full search VQ (FSVQ) as an inferred function. In the experiment, computational savings of 86.19% and a quantization accuracy of 98.15% were achieved, which confirmed the excellent performance of the BSS-VQ approach.

On the impact of excitation and spectral parameters for expressive statistical parametric speech synthesis

This paper presents a study on the importance of short-term speech parameterizations for expressive statistical parametric synthesis. Assuming a source-filter model of speech production, the analysis is conducted over spectral parameters, here defined as features which represent a minimum-phase synthesis filter, and some excitation parameters, which are features used to construct a signal that is fed to the minimum-phase synthesis filter to generate speech. In the first part, different spectral and excitation parameters that are applicable to statistical parametric synthesis are tested to determine which ones are the most emotion dependent. The analysis is performed through two methods proposed to measure the relative emotion dependency of each feature: one based on K-means clustering, and another based on Gaussian mixture modeling for emotion identification. Two commonly used forms of parameters for the short-term speech spectral envelope, the Mel cepstrum and the Mel line spectrum pairs are utilized. As excitation parameters, the anti-causal cepstrum, the time-smoothed group delay, and band-aperiodicity coefficients are considered. According to the analysis, the line spectral pairs are the most emotion dependent parameters. Among the excitation features, the band-aperiodicity coefficients present the highest correlation with the speaker's emotion. The most emotion dependent parameters according to this analysis were selected to train an expressive statistical parametric synthesizer using a speaker and language factorization framework. Subjective test results indicate that the considered spectral parameters have a bigger impact on the synthesized speech emotion when compared with the excitation ones.

METHOD AND APPARATUS FOR RECOVERING LINE SPECTRUM PAIR PARAMETER AND SPEECH DECODING APPARATUS USING SAME

A method and an apparatus for recovering a line spectrum pair (LSP) parameter of a spectrum region when frame loss occurs during speech decoding and a speech decoding apparatus adopting the same are provided. The method of recovering an LSP parameter in speech decoding includes: if it is determined that a received speech packet has an erased frame, converting an LSP parameter of a previous good frame (PGF) of the erased frame or LSP parameters of the PGF and a next good frame (NGF) of the erased frame into a spectrum region and obtaining a spectrum envelope of the PGF or spectrum envelopes of the PGF and NGF; recovering a spectrum envelope of the erased frame using the spectrum envelope of the PGF or the spectrum envelopes of the PGF and NGF; and converting the recovered spectrum envelope of the erased frame into an LSP parameter of the erased frame. The method and apparatus can improve the quality of a recovered speech signal, be applied to a variety of technologies, and provide a method of recovering an LSP parameter for development of an algorithm for speech decoding.

Intraframe quantization of speech line spectrum pairs for code-excited linear prediction based coders in packet networks

This paper proposes intraframe quantization schemes of line spectrum pair parameters to improve frame erasures for the ITU-T G.723.1 coder. The standard ITU-T G.723.1 coder uses an interframe quantization of the line spectrum pair parameters, which causes error propagation to the next frames. Simulation results show that our intraframe quantization schemes coding is much more robust to frame erasures than the embedded method in ITU-T G.723.1, and a typical improvement of 0.4 dB on average spectral distortion can be obtained with 40% packet loss. Enhanced modified bark spectral distortion tests, under various packet loss conditions, confirm that our proposed method is superior to the interframe algorithm embedded in the ITU-T G.723.1 coder of over 1.6. Copyright © 2012 John Wiley & Sons, Ltd.

Search-Free Codebook Mapping for Artificial Bandwidth Extension

A new codebook mapping algorithm for artificial bandwidth extension (ABE) is introduced in this paper. We design a wideband line spectrum pair (LSP) codebook which is coupled with the same index as the LSP codebook of a narrowband speech codec. The received narrowband LSP codebook indices are used to directly induce wideband LSP codewords. Thus, the proposed scheme eliminates codebook search processing to estimate the wideband spectrum envelope. We apply the proposed scheme to bandwidth extension in adaptive multi-rate (AMR) compressed domain. Its performance is assessed via the perceptual evaluation of speech quality (PESQ), informal listening tests, and weighted million operations per second (WMOPS) calculations.

Low Bit Rate Speech Coding Using Lattice Vector Quantization and Time-Scale Modification

This paper presents a low bit rate speech coder based on predictive lattice vector quantization (PLVQ) and time-scale modification (TSM). The coding model of proposed vocoder is built on the MELP, in which bit rate reduction is achieved by taking advantage of PLVQ and TSM techniques. PLVQ is used to encode the speech line spectrum pair (LSP) parameters, which has the advantage of lower implementation complexity than multi-stage vector quantization (MSVQ), moreover, it does not require memory for codebook storage. With our speech data base, PLVQ can save up to 4 bits/frame compared to unstructured codebook MSVQ. TSM can change the speed of speech signal with its perceptual characteristics remained. Through appending TSM as previous and post process, speech coding at bit rate about 1.1 kbps could be easily achieved without modifying the vocoder structure.

An Efficient Computation of LSP Frequencies Using Modified Complex-Free Ferrari Formula

In this paper, an efficient algorithm for computing line spectrum pair (LSP) frequencies from linear prediction coefficients is described. Based on the decimation-in-degree algorithm proposed by Wu and Chen, this paper modifies the Ferrari formula to reduce the computational complexity of finding the roots, i.e., LSPs, of the symmetric and anti-symmetric polynomials. By the use of this modified complex-free Ferrari formula, the proposed algorithm is highly accurate and can avoid the operation of complex number. The proposed algorithm provides three different guidelines for computing the 8-, 10-, and 12-order LSP frequencies. It is shown in various experimental results that the proposed algorithm has a considerable computation time improvement when compared with other conventional methods.

A Statistical Approach to Error Compensation in Spectral Quantization

In this paper, we propose a statistical approach to improve the performance of spectral quantization of speech coders. The proposed techniques compensate for the distortion in a decoded line spectrum pair (LSP) vector based on a statistical mapping function between a decoded LSP vector and its corresponding original LSP vector. We first develop two codebook-based probabilistic matching (CBPM) methods by investigating the distribution of LSP vectors. In addition, we propose an iterative procedure for the two CBPMs. Next, the proposed techniques are applied to the predictive vector quantizer (PVQ) used for the IS-641 speech coder. The experimental results show that the proposed techniques reduce average spectral distortion by around 0.064 dB and the percentage of outliers compared with the PVQ without any compensation, resulting in transparent quality of spectral quantization. Finally, the comparison of speech quality using the perceptual evaluation of speech quality (PESQ) measure is performed and it is shown that the IS-641 speech coder employing the proposed techniques has better decoded speech quality than the standard IS-641 speech coder.

Minimum Separation of Line Spectral Frequencies

We provide a theoretical lower limit on the distance of line spectral frequencies for both the line spectrum pair decomposition and the immittance spectrum pair decomposition. The result applies to line spectral frequencies computed from linear predictive polynomials with all roots within a zero-centered circle of radius r<1

Audio segmentation and classification using threshold values

A portion of an audio signal is separated into multiple frames from which one or more different features are extracted. These different features are used, in combination with a set of rules, to classify the portion of the audio signal into one of multiple different classifications (for example, speech, non-speech, music, environment sound, silence, etc.). In one embodiment, these different features include one or more of line spectrum pairs (LSPs), a noise frame ratio, periodicity of particular bands, spectrum flux features, and energy distribution in one or more of the bands. The line spectrum pairs are also optionally used to segment the audio signal, identifying audio classification changes as well as speaker changes when the audio signal is speech.