Using linear prediction to enhance the tracking of partials [musical audio processing]

Lagrange, Mathieu; Marchand, Sylvain; Rault, Jean-Bernard

doi:10.1109/icassp.2004.1326808

Cited by 13 publications

(10 citation statements)

References 4 publications

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“…Recent results towards improving partial tracking capability have been reported in [4]. In the latter, a stochastic linear prediction method based on the Burg algorithm has shown to be notably useful, specially in situations where crossing spectral lines appear.…”

Section: The Fundamental Partial Tracking Algorithmmentioning

confidence: 99%

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Recursive Least-Squares Estimation of the Evolution of Partials in Sinusoidal Analysis

Nunes

Merched

Biscainho

2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

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Classic methods for sinusoidal analysis rely on partial tracking, a technique where successive sets of spectral peaks of an audio signal must be properly associated in time. The resulting tracks describe, in terms of amplitude and frequency, the continuous evolution of the so-called partials which, combined, model the complex sounds emitted by a given instrument. A well-known challenge in this context is preserving amplitude and frequency coherence in the tracking mechanism, specially in cases where failure in peak detection may occur, or perhaps in the event of crossing partials. This paper presents a new decision-directed recursive least-squares (RLS) estimation method for frequency and amplitude tracking in sinusoidal analysis. Different performance measurements show that the proposed deterministic algorithm outperforms some procedures currently found in the literature.

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Section: The Fundamental Partial Tracking Algorithmmentioning

confidence: 99%

“…sequence of heuristics in order to tackle these issues [2], which can be further improved via standard linear prediction techniques [3,4]. Other alternatives include the use of HMM [5].…”

Section: Introductionmentioning

confidence: 99%

Recursive Least-Squares Estimation of the Evolution of Partials in Sinusoidal Analysis

Nunes

Merched

Biscainho

2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

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“…Each of the three methods was evaluated, but the Burg method was selected as it produced the most accurate and consistent results. Like the autocorrelation method, it has a minimum phase, and like the covariance method it estimates the coefficients on a finite support [21]. It can also be efficiently implemented in real time [20].…”

Section: Linear Predictionmentioning

confidence: 99%

“…There are a number of methods given in the literature, the most widespread among which are the autocorrelation method [20], covariance method [9] and the Burg method [21]. Each of the three methods was evaluated, but the Burg method was selected as it produced the most accurate and consistent results.…”

Section: Linear Predictionmentioning

confidence: 99%

Real-time detection of musical onsets with linear prediction and sinusoidal modeling

Glover

Lazzarini

Timoney

2011

EURASIP J. Adv. Signal Process.

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Real-time musical note onset detection plays a vital role in many audio analysis processes, such as score following, beat detection and various sound synthesis by analysis methods. This article provides a review of some of the most commonly used techniques for real-time onset detection. We suggest ways to improve these techniques by incorporating linear prediction as well as presenting a novel algorithm for real-time onset detection using sinusoidal modelling. We provide comprehensive results for both the detection accuracy and the computational performance of all of the described techniques, evaluated using Modal, our new open source library for musical onset detection, which comes with a free database of samples with hand-labelled note onsets.

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“…Some music analysis and synthesis methods have been proposed, for example, methods based on physical modeling [2], on the wave scattering methods for solving partial differential equations [3]. Methods based on sinusoid model of music signals include using LPC spectra or STFT followed by peak extraction and partial tracking [4,5]. Both LPC and STFT suffer from low frequency resolution for estimating sinusoids in the frequency-domain.…”

Section: Introductionmentioning

confidence: 99%

Music Signal Synthesis using Sinusoid Models and Sliding-Window Esprit

Gunnarsson

2006

2006 IEEE International Conference on Multimedia and Expo

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This paper proposes a music signal synthesis scheme that is based on sinusoid modeling and sliding-window ESPRIT. Despite widely used audio coding standards, effectively synthesizing music using sinusoid models, more suitable for harmonic rich music signals, remains an open issue. In the proposed scheme, music signals are modeled by a sum of damped sinusoids in noise. A sliding window ESPRIT algorithm is applied. A continuity constraint is then imposed for tracking the time trajectories of sinusoids in music and for removing spurious spectral peaks in order to adapt to the changing number of sinusoid contents in dynamic music. Simulations have been performed to several music signals with a range of complexities, including music recorded from banjo, flute and music with mixed instruments. The results from listening and spectrograms have strongly indicated that the proposed method is very robust for music synthesis with good quality.

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Using linear prediction to enhance the tracking of partials [musical audio processing]

Cited by 13 publications

References 4 publications

Recursive Least-Squares Estimation of the Evolution of Partials in Sinusoidal Analysis

Recursive Least-Squares Estimation of the Evolution of Partials in Sinusoidal Analysis

Real-time detection of musical onsets with linear prediction and sinusoidal modeling

Music Signal Synthesis using Sinusoid Models and Sliding-Window Esprit

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