(NOTE: Each chapter begins with an introduction and concludes with a Summary, Exercises and Bibliography.) 1. Introduction. Discrete-Time Speech Signal Processing. The Speech Communication Pathway. Analysis/Synthesis Based on Speech Production and Perception. Applications. Outline of Book. 2. A Discrete-Time Signal Processing Framework. Discrete-Time Signals. Discrete-Time Systems. Discrete-Time Fourier Transform. Uncertainty Principle. z-Transform. LTI Systems in the Frequency Domain. Properties of LTI Systems. Time-Varying Systems. Discrete-Fourier Transform. Conversion of Continuous Signals and Systems to Discrete Time. 3. Production and Classification of Speech Sounds. Anatomy and Physiology of Speech Production. Spectrographic Analysis of Speech. Categorization of Speech Sounds. Prosody: The Melody of Speech. Speech Perception. 4. Acoustics of Speech Production. Physics of Sound. Uniform Tube Model. A Discrete-Time Model Based on Tube Concatenation. Vocal Fold/Vocal Tract Interaction. 5. Analysis and Synthesis of Pole-Zero Speech Models. Time-Dependent Processing. All-Pole Modeling of Deterministic Signals. Linear Prediction Analysis of Stochastic Speech Sounds. Criterion of "Goodness". Synthesis Based on All-Pole Modeling. Pole-Zero Estimation. Decomposition of the Glottal Flow Derivative. Appendix 5.A: Properties of Stochastic Processes. Random Processes. Ensemble Averages. Stationary Random Process. Time Averages. Power Density Spectrum. Appendix 5.B: Derivation of the Lattice Filter in Linear Prediction Analysis. 6. Homomorphic Signal Processing. Concept. Homomorphic Systems for Convolution. Complex Cepstrum of Speech-Like Sequences. Spectral Root Homomorphic Filtering. Short-Time Homomorphic Analysis of Periodic Sequences. Short-Time Speech Analysis. Analysis/Synthesis Structures. Contrasting Linear Prediction and Homomorphic Filtering. 7. Short-Time Fourier Transform Analysis and Synthesis. Short-Time Analysis. Short-Time Synthesis. Short-Time Fourier Transform Magnitude. Signal Estimation from the Modified STFT or STFTM. Time-Scale Modification and Enhancement of Speech. Appendix 7.A: FBS Method with Multiplicative Modification. 8. Filter-Bank Analysis/Synthesis. Revisiting the FBS Method. Phase Vocoder. Phase Coherence in the Phase Vocoder. Constant-Q Analysis/Synthesis. Auditory Modeling. 9. Sinusoidal Analysis/Synthesis. Sinusoidal Speech Model. Estimation of Sinewave Parameters. Synthesis. Source/Filter Phase Model. Additive Deterministic-Stochastic Model. Appendix 9.A: Derivation of the Sinewave Model. Appendix 9.B: Derivation of Optimal Cubic Phase Parameters. 10. Frequency-Domain Pitch Estimation. A Correlation-Based Pitch Estimator. Pitch Estimation Based on a "Comb Filter<170. Pitch Estimation Based on a Harmonic Sinewave Model. Glottal Pulse Onset Estimation. Multi-Band Pitch and Voicing Estimation. 11. Nonlinear Measurement and Modeling Techniques. The STFT and Wavelet Transform Revisited. Bilinear Time-Frequency Distributions. Aeroacoustic Flow in the Vocal Tract. Instantaneous Teager Energy Operator. 12. Speech Coding. Statistical Models of Speech. Scaler Quantization. Vector Quantization (VQ). Frequency-Domain Coding. Model-Based Coding. LPC Residual Coding. 13. Speech Enhancement. Introduction. Preliminaries. Wiener Filtering. Model-Based Processing. Enhancement Based on Auditory Masking. Appendix 13.A: Stochastic-Theoretic parameter Estimation. 14. Speaker Recognition. Introduction. Spectral Features for Speaker Recognition. Speaker Recognition Algorithms. Non-Spectral Features in Speaker Recognition. Signal Enhancement for the Mismatched Condition. Speaker Recognition from Coded Speech. Appendix 14.A: Expectation-Maximization (EM) Estimation. Glossary.Speech Signal Processing.Units.Databases.Index.About the Author.

Discrete-Time Speech Signal Processing: Principles and Practice

Methods and devices are provided for providing surgical access into a body cavity. In one embodiment, a surgical access device is provided that includes a housing coupled to a retractor. The housing can be have one or more movable sealing ports for receiving surgical instruments. Each movable sealing port can include one or more sealing elements therein for sealing the port and/or forming a seal around a surgical instrument disposed therethrough. Each movable sealing port can be rotatable relative to the housing and each sealing element can be rotatable relative to the housing along a predetermined orbital path.

Methods and devices for providing access into a body cavity

The auditory system encodes sound by decomposing the amplitude signal arriving at the ear into multiple frequency bands whose center frequencies and bandwidths are approximately exponential functions of the distance from the stapes. This organization is thought to result from the adaptation of cochlear mechanisms to the animal's auditory environment. Here we report that several basic auditory nerve fiber tuning properties can be accounted for by adapting a population of filter shapes to encode natural sounds efficiently. The form of the code depends on sound class, resembling a Fourier transformation when optimized for animal vocalizations and a wavelet transformation when optimized for non-biological environmental sounds. Only for the combined set does the optimal code follow scaling characteristics of physiological data. These results suggest that auditory nerve fibers encode a broad set of natural sounds in a manner consistent with information theoretic principles.

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Efficient coding of natural sounds.

It is widely accepted that thermostatically controlled loads (TCLs) can be used to provide regulation reserve to the grid. We first argue that the aggregate flexibility offered by a collection of TCLs can be succinctly modeled as a stochastic battery with dissipation. We next characterize the power limits and energy capacity of this battery model in terms of TCL parameters and random exogenous variables such as ambient temperature and user-specified set-points. We then describe a direct load control architecture for regulation service provision. Here, we use a priority-stack-based control framework to select which TCLs to control at any time. The control objective is for the aggregate power deviation from baseline to track an automatic generation control signal supplied by the system operator. Simulation studies suggest the practical promise of our methods.

Aggregate Flexibility of Thermostatically Controlled Loads

Recently, it has been proposed to estimate the noise power spectral density by means of minimum mean-square error (MMSE) optimal estimation. We show that the resulting estimator can be interpreted as a voice activity detector (VAD)-based noise power estimator, where the noise power is updated only when speech absence is signaled, compensated with a required bias compensation. We show that the bias compensation is unnecessary when we replace the VAD by a soft speech presence probability (SPP) with fixed priors. Choosing fixed priors also has the benefit of decoupling the noise power estimator from subsequent steps in a speech enhancement framework, such as the estimation of the speech power and the estimation of the clean speech. We show that the proposed speech presence probability (SPP) approach maintains the quick noise tracking performance of the bias compensated minimum mean-square error (MMSE)-based approach while exhibiting less overestimation of the spectral noise power and an even lower computational complexity.

Unbiased MMSE-Based Noise Power Estimation With Low Complexity and Low Tracking Delay

In this paper, the price elasticity of electrical demand is studied in a smart grid framework where electricity loads are scheduled by distributed Energy Management Controller (EMC) units. It is shown that different price responsive behaviors of electricity loads are results from interactions between their utilities to customers as a function of time and electricity prices. Based on this observation, a parametric utility model is introduced, from which the price elastic behaviors of aggregated loads are effectively modeled as a set of multi-dimensional demand-price functions. The developed demand elasticity model is further utilized in determining the optimal price signal for Real-Time Pricing (RTP) based Demand Response (DR) programs. Considering price elastic behaviors of customers, it is shown that the optimal real-time prices to induce the desired power consumption behaviors from customers for social welfare maximization can be effectively pre-calculated by utility company using the developed demand elasticity model without the need for excessive information exchange among end users and utility companies. Typical results of the proposed methods are further illustrated through numerical examples from a 6-bus test system.

A Statistical Demand-Price Model With Its Application in Optimal Real-Time Price

A dual microphone voice activity detector system is presented. A voice activity detector system estimates the signal level and noise level at each microphone. A level differential between the two microphones of nearby sounds such as the signal is greater than the level differential of more distant sounds such as the noise. Thus, the voice activity detector detects the presence of nearby sounds.

Multi-microphone voice activity detector

An electroacoustic channel soundfield is altered. An audio signal is applied by an electromechanical transducer to an acoustic space, causing air pressure changes therein. Another audio signal is obtained by a second electromechanical transducer, responsive to air pressure changes in the acoustic space. A transfer function estimate of the electroacoustic channel is established, responsive to the second audio signal and part of the first audio signal. The transfer function estimate is derived to be adaptive to temporal variations in the electroacoustic channel transfer function. Filters are obtained with transfer functions based on the transfer function estimate. Part of the first audio signal is filtered therewith.

Method for adaptive control and equalization of electroacoustic channels

As the latest extension of MPEG-4 Audio coding, MPEG-4 Lossless Audio Coding includes a scalable audio coding solution (SLS) that integrates the functionalities of lossless audio coding, perceptual audio coding, and fine granular scalable audio coding into a single coder framework while providing backward compatibility to MPEG Advanced Audio Coding (AAC) at the bit-stream level. Despite its abundant functionalities, SLS still achieves a compression performance that is comparable to state-of-the-art non-scalable lossless audio coding algorithms. As a result, SLS provides a universal digital audio format for a variety of application domains including professional audio, Internet music, consumer electronics, broadcasting and others. This paper presents the structure of SLS and its latest developments during the MPEG standardization process.

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MPEG-4 Scalable to Lossless Audio Coding

This paper presents Advanced Audio Zip (AAZ), a fine grained scalable to lossless (SLS) audio coder that has recently been adopted as the reference model for MPEG-4 audio SLS work. AAZ integrates the functionalities of high-compression perceptual audio coding, fine granular scalable audio coding, and lossless audio coding in a single framework, and simultaneously provides backward compatibility to MPEG-4 Advanced Audio Coding (AAC). AAZ provides the fine granular bit-rate scalability from lossy to lossless coding, and such a scalability is achieved in a perceptually meaningful way, i.e., better perceptual quality at higher bit-rates. Despite its abundant functionalities, AAZ only introduces negligible overhead in terms of lossless compression performance compared with a nonscalable, lossless only audio coder. As a result, AAZ provides a universal yet efficient solution for digital audio applications such as audio archiving, network audio streaming, portable audio playing, and music downloading which were previously catered for by several different audio coding technologies, and eliminates the need for any transcoding system to facilitate sharing of digital audio contents across these application domains

Rongshan Yu

Papers

A Statistical Demand-Price Model With Its Application in Optimal Real-Time Price

Multi-microphone voice activity detector

Method for adaptive control and equalization of electroacoustic channels

MPEG-4 Scalable to Lossless Audio Coding

A fine granular scalable to lossless audio coder