Speech analysis/synthesis and modification using an analysis-by-synthesis/overlap-add sinusoidal model

George, E.B.; Smith, M.J.T.

doi:10.1109/89.622558

Cited by 167 publications

(61 citation statements)

References 26 publications

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“…On a historical note, the estimation procedure of [5], [11] first introduced in [48] is similar to that of matching pursuit for complex sinusoids later introduced in [43]. The RELAX algorithm [42] is an iterative sinusoidal frequency estimation algorithm, where the efficient solution to the one-sinusoid estimation problem is exploited in a recursive manner.…”

Section: Relaxation Of the Nls Estimatormentioning

confidence: 99%

“…In digital processing of speech, the sinusoidal estimation problem arises in such applications as speech modeling and coding [1]- [5] and speech enhancement [6] and more recently, renewed interest in sinusoidal coding of speech has been spurred by the increasing interest in voice over packet-based networks [7]- [10]. Also in the field of audio processing, the sinusoidal signal model has been of interest for music analysis and synthesis [11]- [13], and parametric coding of audio [14]- [20].…”

Section: Introductionmentioning

confidence: 99%

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On perceptual distortion minimization and nonlinear least-squares frequency estimation

Christensen

Jensen

2006

IEEE Trans. Audio Speech Lang. Process.

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Section: Relaxation Of the Nls Estimatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On perceptual distortion minimization and nonlinear least-squares frequency estimation

Christensen

Jensen

2006

IEEE Trans. Audio Speech Lang. Process.

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“…30) consists of a preanalysis block for window selection and envelope extraction, a sinusoidal analysisby-synthesis parameter estimation block, a perceptual model, and a quantization and coding block. Although it bears similarities to sinusoidal speech coding [167][184] [185] and music synthesis [186] algorithms that have been available for some time, the ASAC coder also incorporates some new techniques. In particular, whereas previous sinusoidal coders emphasized waveform matching by minimizing reconstruction error norms such as the mean square error, ASAC disregards classical error minimization criteria and instead selects sinusoids in decreasing order of perceptual importance by means of an iterative analysis-by-synthesis loop.…”

Section: A Analysis/synthesis Audio Codec (Asac)mentioning

confidence: 99%

Perceptual coding of digital audio

2000

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I. INTRODUCTIONAudio coding or audio compression algorithms are used to obtain compact digital representations of high-fidelity (wideband) audio signals for the purpose of efficient transmission or storage. The central objective in audio coding is to represent the signal with a minimum number of bits while achieving transparent signal reproduction, i.e., generating output audio that cannot be distinguished from the original input, even by a sensitive listener ("golden ears"). This paper gives a review of algorithms for transparent coding of high-fidelity audio.The introduction of the compact disk (CD) in the early eighties [1] brought to the fore all of the advantages of digital audio representation, including unprecedented high-fidelity, dynamic range, and robustness. These advantages, however, came at the expense of high data rates. Conventional CD and digital audio tape (DAT) systems are typically sampled at either 44.1 or 48 kilohertz (kHz) using pulse code modulation (PCM) with a sixteen bit sample resolution. This results in uncompressed data rates of 705.6/768 kilobits per second (kbps) for a monaural channel, or 1.41/1.54 megabits per second (Mbps) for a stereo pair at 44.1/48 kHz, respectively. Although high, these data rates were accommodated successfully in first generation digital audio applications such as CD and DAT. Unfortunately, second generation multimedia applications and wireless systems in particular are often subject to bandwidth and cost constraints that are incompatible with high data rates. Because of the success enjoyed by the first generation, however, end users have come to expect "CD-quality" audio reproduction from any digital system. Therefore, new network and wireless multimedia digital audio systems must reduce data rates without compromising reproduction quality. These and other considerations have motivated considerable research during the last decade towards formulation of compression schemes that can satisfy simultaneously the conflicting demands of high compression ratios and transparent reproduction quality for high-fidelity audio signals

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“…In recent years sinusoidal models for time domain speech signals have gained considerable interest in various applications including low bit-rate speech coding [6] and timescale/pitch modifications of speech [7] [3].…”

Section: Introductionmentioning

confidence: 99%

Exponential sinusoidal modeling of transitional speech segments

Jensen

Hansen

1999

1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258)

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A generalized sinusoidal model for speech signal processing is studied. The main feature of the model is that the amplitude of each sinusoidal component is allowed to vary exponentially with time. We propose to use the model in transitional speech segments such as speech onsets and voiced/unvoiced transitions. Computer simulations with natural speech signals indicate substantial better modeling performance in both transitional and voiced regions compared with the traditional constant-amplitude sinusoidal model.

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Speech analysis/synthesis and modification using an analysis-by-synthesis/overlap-add sinusoidal model

Cited by 167 publications

References 26 publications

On perceptual distortion minimization and nonlinear least-squares frequency estimation

On perceptual distortion minimization and nonlinear least-squares frequency estimation

Perceptual coding of digital audio

Exponential sinusoidal modeling of transitional speech segments

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