Linear predictive coding
About: Linear predictive coding is a(n) research topic. Over the lifetime, 6565 publication(s) have been published within this topic receiving 142991 citation(s). The topic is also known as: Linear predictive coding, LPC.
Papers published on a yearly basis
TL;DR: An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data.
Abstract: An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data. The basic properties of the algorithm are discussed and demonstrated by examples. Quite general distortion measures and long blocklengths are allowed, as exemplified by the design of parameter vector quantizers of ten-dimensional vectors arising in Linear Predictive Coded (LPC) speech compression with a complicated distortion measure arising in LPC analysis that does not depend only on the error vector.
TL;DR: A stand-alone noise suppression algorithm that resynthesizes a speech waveform and can be used as a pre-processor to narrow-band voice communications systems, speech recognition systems, or speaker authentication systems.
Abstract: A stand-alone noise suppression algorithm is presented for reducing the spectral effects of acoustically added noise in speech. Effective performance of digital speech processors operating in practical environments may require suppression of noise from the digital wave-form. Spectral subtraction offers a computationally efficient, processor-independent approach to effective digital speech analysis. The method, requiring about the same computation as high-speed convolution, suppresses stationary noise from speech by subtracting the spectral noise bias calculated during nonspeech activity. Secondary procedures are then applied to attenuate the residual noise left after subtraction. Since the algorithm resynthesizes a speech waveform, it can be used as a pre-processor to narrow-band voice communications systems, speech recognition systems, or speaker authentication systems.
05 Sep 1978
TL;DR: This paper presents a meta-modelling framework for digital Speech Processing for Man-Machine Communication by Voice that automates the very labor-intensive and therefore time-heavy and expensive process of encoding and decoding speech.
Abstract: 1. Introduction. 2. Fundamentals of Digital Speech Processing. 3. Digital Models for the Speech Signal. 4. Time-Domain Models for Speech Processing. 5. Digital Representation of the Speech Waveform. 6. Short-Time Fourier Analysis. 7. Homomorphic Speech Processing. 8. Linear Predictive Coding of Speech. 9. Digital Speech Processing for Man-Machine Communication by Voice.
01 Mar 1993
TL;DR: The preface to the IEEE Edition explains the background to speech production, coding, and quality assessment and introduces the Hidden Markov Model, the Artificial Neural Network, and Speech Enhancement.
Abstract: Preface to the IEEE Edition. Preface. Acronyms and Abbreviations. SIGNAL PROCESSING BACKGROUND. Propaedeutic. SPEECH PRODUCTION AND MODELLING. Fundamentals of Speech Science. Modeling Speech Production. ANALYSIS TECHNIQUES. Short--Term Processing of Speech. Linear Prediction Analysis. Cepstral Analysis. CODING, ENHANCEMENT AND QUALITY ASSESSMENT. Speech Coding and Synthesis. Speech Enhancement. Speech Quality Assessment. RECOGNITION. The Speech Recognition Problem. Dynamic Time Warping. The Hidden Markov Model. Language Modeling. The Artificial Neural Network. Index.
TL;DR: An unbiased noise estimator is developed which derives the optimal smoothing parameter for recursive smoothing of the power spectral density of the noisy speech signal by minimizing a conditional mean square estimation error criterion in each time step.
Abstract: We describe a method to estimate the power spectral density of nonstationary noise when a noisy speech signal is given. The method can be combined with any speech enhancement algorithm which requires a noise power spectral density estimate. In contrast to other methods, our approach does not use a voice activity detector. Instead it tracks spectral minima in each frequency band without any distinction between speech activity and speech pause. By minimizing a conditional mean square estimation error criterion in each time step we derive the optimal smoothing parameter for recursive smoothing of the power spectral density of the noisy speech signal. Based on the optimally smoothed power spectral density estimate and the analysis of the statistics of spectral minima an unbiased noise estimator is developed. The estimator is well suited for real time implementations. Furthermore, to improve the performance in nonstationary noise we introduce a method to speed up the tracking of the spectral minima. Finally, we evaluate the proposed method in the context of speech enhancement and low bit rate speech coding with various noise types.
Related Topics (5)
73.4K papers, 983.5K citations
110.4K papers, 1.3M citations
111.8K papers, 2.1M citations
48.8K papers, 954.4K citations
Filter (signal processing)
81.4K papers, 1M citations