Audio Analysis/synthesis System

A method and apparatus for the automatic analysis, synthesis and modification of audio signals, based on an overlap-add sinusoidal model, is disclosed. Automatic analysis of amplitude, frequency and phase parameters of the model is achieved using an analysis-by-synthesis procedure which incorporates successive approximation, yielding synthetic waveforms which are very good approximations to the original waveforms and are perceptually identical to the original sounds. A generalized overlap-add sinusoidal model is introduced which can modify audio signals without objectionable artifacts. In addition, a new approach to pitch-scale modification allows for the use of arbitrary spectral envelope estimates and addresses the problems of high-frequency loss and noise amplification encountered with prior art methods. The overlap-add synthesis method provides the ability to synthesize sounds with computational efficiency rivaling that of synthesis using the discrete short-time Fourier transform (DSTFT) while eliminating the modification artifacts associated with that method.Georgia Tech Research Corporatio

Toward an interpretive framework of two-dimensional speech-signal processing

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 177-179).Traditional representations of speech are derived from short-time segments of the signal and result in time-frequency distributions of energy such as the short-time Fourier transform and spectrogram. Speech-signal models of such representations have had utility in a variety of applications such as speech analysis, recognition, and synthesis. Nonetheless, they do not capture spectral, temporal, and joint spectrotemporal energy fluctuations (or "modulations") present in local time-frequency regions of the time-frequency distribution. Inspired by principles from image processing and evidence from auditory neurophysiological models, a variety of twodimensional (2-D) processing techniques have been explored in the literature as alternative representations of speech; however, speech-based models are lacking in this framework. This thesis develops speech-signal models for a particular 2-D processing approach in which 2-D Fourier transforms are computed on local time-frequency regions of the canonical narrowband or wideband spectrogram; we refer to the resulting transformed space as the Grating Compression Transform (GCT). We argue for a 2-D sinusoidal-series amplitude modulation model of speech content in the spectrogram domain that relates to speech production characteristics such as pitch/noise of the source, pitch dynamics, formant structure and dynamics, and offset/onset content. Narrowband- and wideband-based models are shown to exhibit important distinctions in interpretation and oftentimes "dual" behavior. In the transformed GCT space, the modeling results in a novel taxonomy of signal behavior based on the distribution of formant and onset/offset content in the transformed space via source characteristics. Our formulation provides a speech-specific interpretation of the concept of "modulation" in 2-D processing in contrast to existing approaches that have done so either phenomenologically through qualitative analyses and/or implicitly through data-driven machine learning approaches. One implication of the proposed taxonomy is its potential for interpreting transformations of other time-frequency distributions such as the auditory spectrogram which is generally viewed as being "narrowband"/"wideband" in its low/high-frequency regions. The proposed signal model is evaluated in several ways. First, we perform analysis of synthetic speech signals to characterize its properties and limitations. Next, we develop an algorithm for analysis/synthesis of spectrograms using the model and demonstrate its ability to accurately represent real speech content. As an example application, we further apply the models in cochannel speaker separation, exploiting the GCT's ability to distribute speaker-specific content and often recover overlapping information through demodulation and interpolation in the 2-D GCT space. Specifically, in multi-pitch estimation, we demonstrate the GCT's ability to accurately estimate separate and crossing pitch tracks under certain conditions. Finally, we demonstrate the model's ability to separate mixtures of speech signals using both prior and estimated pitch information. Generalization to other speech-signal processing applications is proposed.by Tianyu Tom Wang.Ph.D

Aspiration noise during phonation : synthesis, analysis, and pitch-scale modification

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 139-145).The current study investigates the synthesis and analysis of aspiration noise in synthesized and spoken vowels. Based on the linear source-filter model of speech production, we implement a vowel synthesizer in which the aspiration noise source is temporally modulated by the periodic source waveform. Modulations in the noise source waveform and their synchrony with the periodic source are shown to be salient for natural-sounding vowel synthesis. After developing the synthesis framework, we research past approaches to separate the two additive components of the model. A challenge for analysis based on this model is the accurate estimation of the aspiration noise component that contains energy across the frequency spectrum and temporal characteristics due to modulations in the noise source. Spectral harmonic/noise component analysis of spoken vowels shows evidence of noise modulations with peaks in the estimated noise source component synchronous with both the open phase of the periodic source and with time instants of glottal closure. Inspired by this observation of natural modulations in the aspiration noise source, we develop an alternate approach to the speech signal processing aim of accurate pitch-scale modification. The proposed strategy takes a dual processing approach, in which the periodic and noise components of the speech signal are separately analyzed, modified, and re-synthesized. The periodic component is modified using our implementation of time-domain pitch-synchronous overlap-add, and the noise component is handled by modifying characteristics of its source waveform.(cont.) Since we have modeled an inherent coupling between the original periodic and aspiration noise sources, the modification algorithm is designed to preserve the synchrony between temporal modulations of the two sources. The reconstructed modified signal is perceived to be natural-sounding and generally reduces artifacts that are typically heard in current modification techniques.by Daryush Mehta.S.M

Glottal-synchronous speech processing

Glottal-synchronous speech processing is a field of speech science where the pseudoperiodicity of voiced speech is exploited. Traditionally, speech processing involves segmenting and processing short speech frames of predefined length; this may fail to exploit the inherent periodic structure of voiced speech which glottal-synchronous speech frames have the potential to harness. Glottal-synchronous frames are often derived from the glottal closure instants (GCIs) and glottal opening instants (GOIs). The SIGMA algorithm was developed for the detection of GCIs and GOIs from the Electroglottograph signal with a measured accuracy of up to 99.59%. For GCI and GOI detection from speech signals, the YAGA algorithm provides a measured accuracy of up to 99.84%. Multichannel speech-based approaches are shown to be more robust to reverberation than single-channel algorithms. The GCIs are applied to real-world applications including speech dereverberation, where SNR is improved by up to 5 dB, and to prosodic manipulation where the importance of voicing detection in glottal-synchronous algorithms is demonstrated by subjective testing. The GCIs are further exploited in a new area of data-driven speech modelling, providing new insights into speech production and a set of tools to aid deployment into real-world applications. The technique is shown to be applicable in areas of speech coding, identification and artificial bandwidth extension of telephone speec

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Reconstruction of intelligible audio speech from visual speech information

The aim of the work conducted in this thesis is to reconstruct audio speech signals using information which can be extracted solely from a visual stream of a speaker's face, with application for surveillance scenarios and silent speech interfaces. Visual speech is limited to that which can be seen of the mouth, lips, teeth, and tongue, where the visual articulators convey considerably less information than in the audio domain, leading to the task being difficult. Accordingly, the emphasis is on the reconstruction of intelligible speech, with less regard given to quality. A speech production model is used to reconstruct audio speech, where methods are presented in this work for generating or estimating the necessary parameters for the model. Three approaches are explored for producing spectral-envelope estimates from visual features as this parameter provides the greatest contribution to speech intelligibility. The first approach uses regression to perform the visual-to-audio mapping, and then two further approaches are explored using vector quantisation techniques and classification models, with long-range temporal information incorporated at the feature and model-level. Excitation information, namely fundamental frequency and aperiodicity, is generated using artificial methods and joint-feature clustering approaches. Evaluations are first performed using mean squared error analyses and objective measures of speech intelligibility to refine the various system configurations, and then subjective listening tests are conducted to determine word-level accuracy, giving real intelligibility scores, of reconstructed speech. The best performing visual-to-audio domain mapping approach, using a clustering-and-classification framework with feature-level temporal encoding, is able to achieve audio-only intelligibility scores of 77 %, and audiovisual intelligibility scores of 84 %, on the GRID dataset. Furthermore, the methods are applied to a larger and more continuous dataset, with less favourable results, but with the belief that extensions to the work presented will yield a further increase in intelligibility

Scalable and perceptual audio compression

This thesis deals with scalable perceptual audio compression. Two scalable perceptual solutions as well as a scalable to lossless solution are proposed and investigated. One of the scalable perceptual solutions is built around sinusoidal modelling of the audio signal whilst the other is built on a transform coding paradigm. The scalable coders are shown to scale both in a waveform matching manner as well as a psychoacoustic manner. In order to measure the psychoacoustic scalability of the systems investigated in this thesis, the similarity between the original signal\u27s psychoacoustic parameters and that of the synthesized signal are compared. The psychoacoustic parameters used are loudness, sharpness, tonahty and roughness. This analysis technique is a novel method used in this thesis and it allows an insight into the perceptual distortion that has been introduced by any coder analyzed in this manner