Speech Enhancement \tMeasuring Speech Quality Subjective listening tests are preferred, but they are not always feasible and objective tests that attempt to predict how users will evaluate the speech quality is required. \tEcho Reduction in Coding Transform coding in audio and voice can introduce artifacts called pre/post echoes. \tThe Cause of Echoes in Coding Transform coding in audio and voice can introduce artifacts called pre/post echoes. \tBattlefield Voice Activated Transmission (VOX) Voice Activated Transmission is commonly used in radio communications in which push-to-talk is either inconvenient or not practical, thus hands-free communication is required. \tThe Applications of Voice Activity Detection (VAD) Voice Activity Detectors (VAD) play a major role in the telecommunications and speech processing applications. \tSpeech Enhancement and Speech Intelligibility Speech enhancement may improve the perceptual speech quality, it does not guarantee an improvement in speech intelligibility. \tEcho Cancellation Design Echo canceller design considerations and related articles. \tStandard Methods of Voice Activity Detection (VAD) Voice Activity Detectors (VAD) play a major role in the telecommunications and speech processing applications. \tDe-reverberation of Speech Signals De-reverberation attempts to model the impulse response of the acoustic environment and filter it from the received microphone signal. \tDual Channel Noise Estimation for Speech Enhancement This dual-channel noise estimation technique is an extension of the single-channel noise estimation that uses the phase difference information. \tWindowing in Vocoders to Remove Artifacts A discussion of the use of Windowing functions to smooth discontinuities and remove audio artifacts. \tYule-Walker Algorithm and Method Used in Voice Enhancement A discussion of the Yule-Walker algorithm and its use in voice enhancement applications. Speech Recognition \tAutomatic Speech Recognition and Speech Enhancement Discussion on the challenges of using ASR to convert speech to text. \tForensic Speaker Recognition Preprocessor Discussion on using speech preprocessing to improve speech recognition rates in ASR and other applications. \tSpeech Composition for Recognition How decomposing speech signals can help to determine the words being spoken. \tMachine Learning Algorithms Discussion on Machine Learning Algorithms used in advanced signal processing models. \tMeasuring Detector Performance in Voice Activity Detection Discussion of methods for measuring VAD performance in speech enhancement systems. \tRoom Quality Modeling Discussion on designing an acoustic space and the subjective phenomena that can be quantified for objective measurement. \tSpeech Composition for Recognition Discussion on time and frequency characteristics of speech that can be used to determine what words are being spoken. \tMicrophone Array Aided Distant Speech Recognition Discussion on using microphone arrays with DSR systems to improve speech recognition performance. Speech Coders \tComfort Noise Generation in Vocoders A description of Comfort Noise Generation (CNG) in Speech Coding. \tPacket Loss Concealment (PLC) in Vocoders A description of Packet Loss Concealment in Vocoders. \tArithmetic Coding in Vocoders A description of Arithmetic Coding as it applies to Voice Coding. \tAllpass Filters in Vocoders A discussion of the use of all-pass filters in Vocoders. \tStability of LPC Filters in Vocoders A description of algorithms used to ensure stability of Linear Predictive Coding (LPC) filters in Speech Coders. \tWideband Energy Detection in Scalable Speech Coding An algorithm is presented for determining if speech sampled at Wideband rates should be downsampled to narrowband rates. \tEfficient Implementation of LPC Analysis Filters An efficient algorithm for performing a LPC analysis filter is presented. \tG.729 and Its Application in the Cellular Market Advantages of G.729 codec for cellular applications are discussed. Beamforming \tAcoustic Beamformer Design A discussion of acoustic beamformer design considerations and applications. \tSound Propagation Models for Beamforming Beamforming can be applied to acoustic signal processing for speech enhancement and noise reduction. \tDifferential Microphone Arrays Microphone arrays can be used for localization of a desired speaker/signal, tracking of the signal in the environment, and, with advanced signal processing techniques, improving the overall sound quality of the system. \tGriffiths-Jim Beamformer for Noise Reduction A discussion of GJB using VAD for controlled adaption of phase alignment and noise reduction of desired speech signal. \tGeneration of Mixing Functions for BSS and Beamforming When developing acoustic noise reduction algorithms based on Beamforming and BSS, numerical simulations can be used to select the best microphone configuration for a given application. \tSpeech Separation with Microphone Array A discussion on using beamforming and blind signal separation microphone array techniques to separate speech. \tSpeaker Diarization Discussion on source diarization in audio video conferencing applications. \tSignal Restoration in Frequency Domain via Prediction A discussion on signal restoration or de-noising to reconstruct speech. \tAcoustic Two-Channel Crosstalk Canceller A discussion of using microphone arrays to extract signals from two acoustic sources. \tAcoustic Multiple-Channel Crosstalk Canceller A discussion of using microphone arrays to extract signals from multiple acoustic sources. Noise Reduction \tModel-Based Speech Enhancement Maintaining harmonic information will improve the overall speech quality when compared to standard noise reduction techniques. \tBandwidth Extension A discussion of algorithms and methods used to convert NB speech to WB. \tBlind Source Separation for Noise Reduction in Mobile Blind Source Separation or Independent Component Analysis is a multi-channel technique quite different from beamforming that can be used for noise reduction/cancellation in Mobile Telecommunications, such as cell phones and tablets \tPsychoacoustic Noise Suppression Psychoacoustic noise suppression takes into account the physiological and acoustic properties of the human hearing organ into the design of the algorithm for improving the perceptual quality of the communication. \tNoise Reduction in Sound Capture An acoustic echo canceller may need to address different acoustic and noise control environment issues. Constraints include the volume of the enclosure, the required bandwidth, and the tolerable delay. \tNoise Reduction of Non-stationary Noise Sources A discussion on Noise Reduction techniques in noisy environments with dynamic noise sources. \tSingle Channel Noise Detection Methods to detect wind noise captured by microphones when used outdoors. \tWind Noise in Mobile Telecommunication Discussion of algorithms to filter wind noise in hearing aids, cell phones and other mobile devices \tMonaural Speech Separation Speech segregation is the separation of a desired speech signal from a mix of environmental signals. \tThe Artifacts of Spectral Subtraction An example of the artifacts of the spectral subtraction is musical noise. Musical noise are little islands of spectrum power in a signal, that appear randomly in different frequency buckets from frame to frame. \tModel-Based Speech Enhancement Maintaining harmonic information will improve the overall speech quality when compared to standard noise reduction techniques. \tMaintaining the Harmonic Structures for Speech Enhancement Maintaining harmonic information will improve the overall speech quality when compared to standard noise reduction techniques. Particle Swarm Optimization \tParticle Swarm Optimization (PSO) in Speech Enhancement An application of swarm coding to two-channel noise reduction. \tParticle Swarm Optimization in Acoustic Echo Cancellation Particle Swarm Optimization is used to determine the optimal IIR filter for Acoustic Echo Cancellation. \tNoise Reduction using Singular Value Decomposition (SVD) and Particle Swarm Optimization (PSO) A method for reducing noise in speech signals based upon optimizing the effects of a Singular Value Decomposition.