Fundamental frequency estimation of musical signals using a two-way mismatch procedure

Maher, Robert C.; Beauchamp, James W.

doi:10.1121/1.408685

Cited by 114 publications

(58 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For these systems, as well as fundamental frequency detection [13], audio coding, and music information retrieval (MIR), improving the estimation of sinusoidal parameters is very important.…”

Section: Introductionmentioning

confidence: 99%

Sinusoidal Parameter Estimation Using Quadratic Interpolation around Power-Scaled Magnitude Spectrum Peaks

Werner

Germain

2016

Applied Sciences

View full text Add to dashboard Cite

Abstract:The magnitude of the Discrete Fourier Transform (DFT) of a discrete-time signal has a limited frequency definition. Quadratic interpolation over the three DFT samples surrounding magnitude peaks improves the estimation of parameters (frequency and amplitude) of resolved sinusoids beyond that limit. Interpolating on a rescaled magnitude spectrum using a logarithmic scale has been shown to improve those estimates. In this article, we show how to heuristically tune a power scaling parameter to outperform linear and logarithmic scaling at an equivalent computational cost. Although this power scaling factor is computed heuristically rather than analytically, it is shown to depend in a structured way on window parameters. Invariance properties of this family of estimators are studied and the existence of a bias due to noise is shown. Comparing to two state-of-the-art estimators, we show that an optimized power scaling has a lower systematic bias and lower mean-squared-error in noisy conditions for ten out of twelve common windowing functions.

show abstract

Section: Introductionmentioning

confidence: 99%

Sinusoidal Parameter Estimation Using Quadratic Interpolation around Power-Scaled Magnitude Spectrum Peaks

Werner

Germain

2016

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In [6], the peak tracking algorithm is based on a hidden Markov models and thus has a high computation complexity. Meanwhile, others methods [7,8] are able to track spectral structures…”

Section: Time-frequency Tracking Of Peaks and Structuresmentioning

confidence: 99%

Monitoring Based on Time-Frequency Tracking of Estimated Harmonic Series and Modulation Sidebands

Gerber

Martin

Mailhes

2015

Applied Condition Monitoring

View full text Add to dashboard Cite

Abstract. A condition monitoring system is a key element in a predictive maintenance strategy allowing to reduce the operating costs of the monitored system. However, the system-driven generation of health indicators requires the knowledge of the system kinematics and the configuration of thresholds which may induce lots of false alarms. In this paper, we propose a generic and data-driven method to automatically generate system health indicators without any a priori knowledge on the monitored system or the acquired signals. The proposed method is based on the automatic detection of spectral content characterising every acquired signal. Within these successive spectral contents, peaks, harmonics series and modulation sidebands are then tracked over time and grouped in time trajectories which will be used to generate the system health indicators.

show abstract

“…These descriptions can be obtained using the audio features described in [19] and were discussed in previous works such as [14]. For example, the duration can be obtained using the Temporal-Effective-Duration feature, the description of the attack using the Log-Attack-Time (an efficient method to estimate it has been proposed in [19]), the pitch using numerous existing pitch estimation algorithms [32] [33] [34] [35], the spectral distribution using the perceptual features spectral centroid and spectral spread. …”

Section: E Remaining Descriptionmentioning

confidence: 99%

Sound Indexing Using Morphological Description

Peeters

Deruty

2010

IEEE Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

Abstract-Sound sample indexing usually deals with the recognition of the source/cause that has produced the sound. For abstract sounds, sound-effects, unnatural or synthetic sounds this cause is usually unknown or unrecognizable. An efficient description of these sounds has been proposed by Schaeffer under the name morphological description. Part of this description consists in describing a sound by identifying the temporal evolution of its acoustic properties to a set of profiles. In this work, we consider three morphological descriptions: dynamic profiles (ascending, descending, ascending/descending, stable, impulsive), melodic profiles (up, down, stable, up/down, down/up) and complex-iterative sound description (non-iterative, iterative, grain, repetition). We study the automatic indexing of a sound into these profiles. Because this automatic indexing is difficult using standard audio features, we propose new audio features to perform this task. The dynamic profiles are estimated by modeling the loudness over-time of a sound by a second-order B-spline model and derive features from this model. The melodic profiles are estimated by tracking over time the perceptual filter which has the maximum excitation. A function is derived from this track which is then modeled using a second-order B-spline model. The features are again derived from the B-spline model. The description of complex-iterative sounds is obtained by estimating the amount of repetition and the period of the repetition. These are obtained by computing an audio similarity function derived from an MFCC similarity matrix. The proposed audio features are then tested for automatic classification. We consider three classification tasks corresponding to the three profiles. In each case, the results are compared with the ones obtained using standard audio features.

show abstract

Fundamental frequency estimation of musical signals using a two-way mismatch procedure

Cited by 114 publications

References 16 publications

Sinusoidal Parameter Estimation Using Quadratic Interpolation around Power-Scaled Magnitude Spectrum Peaks

Sinusoidal Parameter Estimation Using Quadratic Interpolation around Power-Scaled Magnitude Spectrum Peaks

Monitoring Based on Time-Frequency Tracking of Estimated Harmonic Series and Modulation Sidebands

Sound Indexing Using Morphological Description

Contact Info

Product

Resources

About