Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Digital processing of speech signals

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

From the Publisher:
This is undoubtedly the most accessible book on digital signal processing (DSP) available to the beginner. Using intuitive explanations and well-chosen examples, this book gives you the tools to develop a fundamental understanding of DSP theory. The author covers the essential mathematics by explaining the meaning and significance of the key DSP equations. Comprehensive in scope, and gentle in approach, the book will help you achieve a thorough grasp of the basics and move gradually to more sophisticated DSP concepts and applications.

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Understanding digital signal processing

During the last decade, CD-quality digital audio has essentially replaced analog audio. Emerging digital audio applications for network, wireless, and multimedia computing systems face a series of constraints such as reduced channel bandwidth, limited storage capacity, and low cost. These new applications have created a demand for high-quality digital audio delivery at low bit rates. In response to this need, considerable research has been devoted to the development of algorithms for perceptually transparent coding of high-fidelity (CD-quality) digital audio. As a result, many algorithms have been proposed, and several have now become international and/or commercial product standards. This paper reviews algorithms for perceptually transparent coding of CD-quality digital audio, including both research and standardization activities. This paper is organized as follows. First, psychoacoustic principles are described, with the MPEG psychoacoustic signal analysis model 1 discussed in some detail. Next, filter bank design issues and algorithms are addressed, with a particular emphasis placed on the modified discrete cosine transform, a perfect reconstruction cosine-modulated filter bank that has become of central importance in perceptual audio coding. Then, we review methodologies that achieve perceptually transparent coding of FM- and CD-quality audio signals, including algorithms that manipulate transform components, subband signal decompositions, sinusoidal signal components, and linear prediction parameters, as well as hybrid algorithms that make use of more than one signal model. These discussions concentrate on architectures and applications of those techniques that utilize psychoacoustic models to exploit efficiently masking characteristics of the human receiver. Several algorithms that have become international and/or commercial standards receive in-depth treatment, including the ISO/IEC MPEG family (-1, -2, -4), the Lucent Technologies PAC/EPAC/MPAC, the Dolby AC-2/AC-3, and the Sony ATRAC/SDDS algorithms. Then, we describe subjective evaluation methodologies in some detail, including the ITU-R BS.1116 recommendation on subjective measurements of small impairments. This paper concludes with a discussion of future research directions.

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Perceptual coding of digital audio

Speech enhancement using iterative Wiener filtering has been shown to require interframe and intraframe constraints in all-pole parameter estimation We show that a clean speech VQ codebook is more effective in providing intraframe constraints and, hence, better convergence of the iterative filtering scheme Satisfactory speech enhancement results are obtained with a small codebook of 128, and the algorithm is effective for both white noise and pink noise up to 0 dB SNR

/pdf/codebook-constrained-wiener-filtering-for-speech-enhancement-18ikt40spx.pdf

Codebook constrained Wiener filtering for speech enhancement

A new method for fast and simple segmentation of peripheral blood smears using the Teager energy operator (TEO) is presented. The two-part algorithm efficiently segments leucocytes present in the smear into nucleus and cytoplasm. The local mean weighted high pass filtering property of the Teager energy operator is used to identify and distinguish the nucleus of white blood cells. Cytoplasm present in the white blood cells is segmented using selective mathematical morphology. Experimental results are shown for a blood smear containing multiple nuclei. It was observed that modified histogram developed using the TEO can efficiently segment even multiple nuclei. Experiments have been conducted to verify the accuracy of the proposed segmentation scheme for all the major types of leucocytes. Simulation results have shown that our algorithm segments nuclei effectively even at low percentage of impulse noise.

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Teager energy based blood cell segmentation

Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived from the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.

/pdf/compressive-sensing-for-sparsely-excited-speech-signals-349sw3alv1.pdf

Compressive sensing for sparsely excited speech signals

When an input data sequence has a large number of zeros and the number of output samples required to be computed is small, significant time saving can be achieved by a judicious combination of the pruning algorithms for decimation-in-time (DIT) and decimation-in-frequency (DIF). It is shown that the complementary structure of the DIT and the DIF formulations makes possible the application of the pruning algorithms simultaneously at the input, as well as at the output, for either of the formulations. For a given number of input and output points, a choice between the two formulations can be made based on the amount of time saved in each. Also, a simple assembly language modification is shown by which the bit reversal is made significantly faster.

FFT algorithm for both input and output pruning

Systems, devices, and methods for using Multi-Pattern Viterbi Algorithm for joint decoding of multiple patterns are disclosed. An exemplary method may receive a plurality of sets of time-sequential signal observations for each of a number K of signal repetitions. Further, each set of signal observations is associated with a respective dimension of a K-dimensional time grid having time-indexed points. Moreover, at each of a plurality of the time-indexed points, a state cost metric is calculated with a processor for each state in a set of states of a hidden Markov model (HMM). In addition, each state in the set of states and for a given time-indexed point, the state cost metric calculation provides a most- likely predecessor state and a corresponding most- likely predecessor time-indexed point. The exemplary method may also determine a sequence of states using the calculated state cost metrics and determine a corresponding cumulative probability measure for the HMM.

Thippur V. Sreenivas

Papers

Codebook constrained Wiener filtering for speech enhancement

Teager energy based blood cell segmentation

Compressive sensing for sparsely excited speech signals

FFT algorithm for both input and output pruning

System and method of using multi pattern viterbi algorithm for joint decoding of multiple patterns