Group delay functions and its applications in speech technology

Murthy, Hema A.; Yegnanarayana, B.

doi:10.1007/s12046-011-0045-1

Cited by 90 publications

(35 citation statements)

References 40 publications

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“…The work presented in [33] and [35] may explain in more detail the theory of how the zero-pole pairs affect the spectra. However, complementary additions are presented in this section.…”

Section: A Generalmentioning

confidence: 99%

“…These results are significant from many aspects, especially in the case of classifying and identifying loads. The results are known from other disciplines, such as speech recognition [33]. The theorems are presented next in a format suitable for electric machine identification.…”

Section: A Generalmentioning

confidence: 99%

“…In this paper, the theory is expanded by first using the Z-transform for system identification and also by using digital signal processing (DSP) tools for enhancing the applicability of the theory. The Z-transform has been widely used in other disciplines, e.g., in radar algorithms [31], [32] and speech recognition algorithms [33], but it has not been projected into electric power. Other works use the Z-transform in the context of electric machinery and electromagnetic phenomena identification [34], [35], control theory, and filter design [36].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Defining the Unique Signatures of Loads Using the Currents’ Physical Components Theory and $Z$-Transform

Calamero

Beck

Shmilovitz

2015

IEEE Trans. Ind. Inf.

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A method to extract unique features from measured waveforms of nonlinear loads is presented. These features can be used for the identification of loads connected to the grid. The method is based on the currents' physical components (CPC) electric power transport theory combined with the Z-transform (for implementation using digital signal processing (DSP). A set of admittance-based Z-transform functions that reflect the current physical components suggested by the CPC is constructed. The resulting transfer functions are shown to reflect the electric physical significance, and are used for electric load and machine identification through its electric characteristics. In this paper, the Z-transform analytic expressions are developed and extended along with their physical comprehension. Moreover, the strength of the theory over more traditional spectral analysis is explained. The method presented in this paper is demonstrated and analyzed using real-world measured waveforms (measured by power quality monitors). Index Terms-Load identification, power transfer theory, smart grid.1551-3203 worked with R&D for the Israel Air-Force (IAF), where he developed power processing topologies for avionic systems. During 1997-1999, he was a Post-Doctorate Fellow with New York Polytechnic University, Brooklyn, NY, USA. Since 2000, he has been with the Faculty of Engineering, Tel-Aviv University, where he heads the Power Electronics and Power Quality Research Group. He has authored and coauthored over 100 papers, of which over 40 are in refereed journals. His research interests include general circuit theory, switched-mode converters, power quality, special applications of power electronics (such as for alternative energy sources) powering of autonomous sensor networks, and implanted medical devices.

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“…The work presented in [33] and [35] may explain in more detail the theory of how the zero-pole pairs affect the spectra. However, complementary additions are presented in this section.…”

Section: A Generalmentioning

confidence: 99%

Section: A Generalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Defining the Unique Signatures of Loads Using the Currents’ Physical Components Theory and $Z$-Transform

Calamero

Beck

Shmilovitz

2015

IEEE Trans. Ind. Inf.

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“…It has been used in speaker identification (Hegde et al [84]), but also in speech analysis, speech segmentation, speech recognition and language identification frameworks (Murthy and Yegnanarayana [85]). …”

Section: Stft-based Frequency Featuresmentioning

confidence: 99%

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

2016

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Endowing machines with sensing capabilities similar to those of humans is a prevalent quest in engineering and computer science. In the pursuit of making computers sense their surroundings, a huge effort has been conducted to allow machines and computers to acquire, process, analyze and understand their environment in a human-like way. Focusing on the sense of hearing, the ability of computers to sense their acoustic environment as humans do goes by the name of machine hearing. To achieve this ambitious aim, the representation of the audio signal is of paramount importance. In this paper, we present an up-to-date review of the most relevant audio feature extraction techniques developed to analyze the most usual audio signals: speech, music and environmental sounds. Besides revisiting classic approaches for completeness, we include the latest advances in the field based on new domains of analysis together with novel bio-inspired proposals. These approaches are described following a taxonomy that organizes them according to their physical or perceptual basis, being subsequently divided depending on the domain of computation (time, frequency, wavelet, image-based, cepstral, or other domains). The description of the approaches is accompanied with recent examples of their application to machine hearing related problems.

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“…The properties of the group delay functions are reviewed [8]. The information in speech signals that is derived from short-time Fourier analysis is represented in terms of features in conventional methods.…”

Section: Literature Surveymentioning

confidence: 99%

AAYUDHA: A Tool for Automatic Segmentation and Labelling of Continuous Tamil Speech

Sree¹,

M²

2016

IJCA

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Speech! An effective way of communication between human is now becoming an alternative way to communicate between human and machine. This alternative way is now-a-days used in many real time systems for faster, easier and comfortable response and communication. Speech segmentation and labelling are the process that lay as a key to decide the accuracy of several speech related research. A tool "AAYUDHA" is proposed that enables automatic segmentation and labelling of continuous speech in Tamil. Two different segmentation algorithms, one based on Fast Fourier Transform (FFT) feature set and 2D filtering and other based on Discrete Wavelet Transform (DWT) feature set and its energy variation in different sub-bands are implemented. The segmentation accuracy of those algorithms is analyzed. Further the segmented speech is labelled using a baseline Hidden Markov Model (HMM) based acoustic model. A speech corpus named "KAZHANGIYAM" is created which includes the recorded Tamil speech of various speakers. The database also includes the information of manually segmented data of those speech data. This speech corpus is used to analyze the accuracy of the algorithms used in the proposed tool. This tool concentrates on the phonetic level segmentation of Tamil speech. The tool shows an acceptable segmentation and labelling accuracy.

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Group delay functions and its applications in speech technology

Cited by 90 publications

References 40 publications

Defining the Unique Signatures of Loads Using the Currents’ Physical Components Theory and $Z$-Transform

Defining the Unique Signatures of Loads Using the Currents’ Physical Components Theory and $Z$-Transform

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

AAYUDHA: A Tool for Automatic Segmentation and Labelling of Continuous Tamil Speech

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