By Raghunath S. Holambe
Advances in Non-Linear Modeling for Speech Processing contains complex issues in non-linear estimation and modeling innovations in addition to their purposes to speaker popularity.
Non-linear aeroacoustic modeling procedure is used to estimate the real fine-structure speech occasions, which aren't published by means of the fast time Fourier remodel (STFT). This aeroacostic modeling method presents the impetus for the excessive solution Teager strength operator (TEO). This operator is characterised by means of a time solution which can song swift sign power alterations inside a glottal cycle.
The cepstral beneficial properties like linear prediction cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are computed from the significance spectrum of the speech body and the section spectra is missed. to beat the matter of neglecting the part spectra, the speech construction method will be represented as an amplitude modulation-frequency modulation (AM-FM) version. To demodulate the speech sign, to estimation the amplitude envelope and instant frequency elements, the power separation set of rules (ESA) and the Hilbert rework demodulation (HTD) set of rules are mentioned.
Different positive aspects derived utilizing above non-linear modeling concepts are used to strengthen a speaker id method. eventually, it really is proven that, the fusion of speech creation and speech conception mechanisms may end up in a strong characteristic set.
Read or Download Advances in Non-Linear Modeling for Speech Processing PDF
Similar artificial intelligence books
Computing device studying tools extract price from great information units speedy and with modest assets.
They are verified instruments in a variety of commercial functions, together with se's, DNA sequencing, inventory marketplace research, and robotic locomotion, and their use is spreading speedily. those who comprehend the tools have their number of lucrative jobs. This hands-on textual content opens those possibilities to desktop technology scholars with modest mathematical backgrounds. it truly is designed for final-year undergraduates and master's scholars with constrained historical past in linear algebra and calculus.
Comprehensive and coherent, it develops every thing from uncomplicated reasoning to complicated strategies in the framework of graphical types. scholars study greater than a menu of ideas, they improve analytical and problem-solving abilities that equip them for the true international. quite a few examples and routines, either machine established and theoretical, are integrated in each bankruptcy.
Resources for college students and teachers, together with a MATLAB toolbox, can be found on-line.
The publication starts with a bankruptcy on conventional tools of supervised studying, masking recursive least squares studying, suggest sq. errors tools, and stochastic approximation. bankruptcy 2 covers unmarried agent reinforcement studying.
Topics contain studying worth capabilities, Markov video games, and TD studying with eligibility strains. bankruptcy three discusses participant video games together with participant matrix video games with either natural and combined recommendations. quite a few algorithms and examples are awarded. bankruptcy four covers studying in multi-player video games, stochastic video games, and Markov video games, targeting studying multi-player grid games—two participant grid video games, Q-learning, and Nash Q-learning. bankruptcy five discusses differential video games, together with multi participant differential video games, actor critique constitution, adaptive fuzzy regulate and fuzzy interference platforms, the evader pursuit online game, and the protecting a territory video games. bankruptcy 6 discusses new principles on studying inside robot swarms and the leading edge inspiration of the evolution of character traits.
• Framework for realizing numerous tools and ways in multi-agent desktop learning.
• Discusses tools of reinforcement studying corresponding to a few types of multi-agent Q-learning
• acceptable to investigate professors and graduate scholars learning electric and laptop engineering, laptop technological know-how, and mechanical and aerospace engineering
Genetic programming has emerged as an incredible computational technique for fixing advanced difficulties in a range of disciplines. with a purpose to foster collaborations and facilitate the trade of principles and data on the topic of the swiftly advancing box of Genetic Programming, the yearly Genetic Programming thought and perform Workshop was once geared up by means of the college of Michigan’s middle for the learn of advanced platforms to supply a discussion board for either those that improve computational idea and those who perform the artwork of computation.
"This publication is a entire number of wisdom on expanding the notions and versions in adaptive and loyal platforms, improving the notice of the position of adaptability and resilience in approach environments"--Provided by way of writer. summary: "This ebook is a complete choice of wisdom on expanding the notions and versions in adaptive and loyal platforms, bettering the attention of the position of adaptability and resilience in method environments"--Provided by means of writer
- Abstraction in Artificial Intelligence and Complex Systems
- Genetic Programming Theory and Practice XII (Genetic and Evolutionary Computation)
- Explanation-Based Neural Network Learning: A Lifelong Learning Approach
- Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation
- Predicting Structured Data (Neural Information Processing)
- Multi-Agent Machine Learning: A Reinforcement Approach
Extra info for Advances in Non-Linear Modeling for Speech Processing
Bishop C (1997) Neural networks for pattern recognition. 1 Introduction We begin this chapter by discussing signal energy in general. , the Teager energy operator (TEO) and how it can be obtained by considering a second order differential equation, which describes the motion of an object suspended by a spring. This operator is interesting because it has a small time window, making it ideal for local (time) analysis of signals. The analysis of AM–FM signals using the Teager Energy Operator is probably the field where most of the research regarding the operator has been done so far.
This model can also be defined in the canonical form as discussed below. , o[N − 1])T ≡ vector of N observation samples. 10) 30 3 Linear and Dynamic System Model Fig. 1 The source-filter model of voice production for voiced and unvoiced speech  where o is the observation or measurement vector, with the dimensionality of N × 1, θ is the parameter vector, with dimensions of n × 1, H is the observation matrix, with dimensions of N × n and v is the observation noise vector, which is of dimensions N × 1.
Wiley, New York 12. Makhoul J (1975) Linear prediction: a tutorial review. In. Proceedings of the IEEE, vol 64, pp 561–580 13. Atal BS (1974) Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification. J Acoust Soc Am 55:1304–1312 14. Teager HM (1980) Some observations on oral air flow during phonation. IEEE Trans Speech Audio Process 28(5):599–601 15. Campbell J (1997) Speaker recognition: a tutorial. Proc IEEE 511(9):1437–1462 16.
Advances in Non-Linear Modeling for Speech Processing by Raghunath S. Holambe