A Parametric Method for Pitch Estimation of Piano Tones

Emiya, Valentin; David, Bertrand; Badeau, Roland

doi:10.1109/icassp.2007.366663

Cited by 24 publications

(18 citation statements)

References 14 publications

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“…The values of the parameters of the MPE algorithm are given in Table I. The order Q p of the spectral envelope filter A p (z) of a note p is set to Q p = H p /2 in order to be large enough to fit the data and small enough to obtain a smooth spectral envelope 7 . Parameters k σ 2…”

Section: A Implementation and Tuningmentioning

confidence: 99%

“…The proposed MPE algorithm has been tested on a database called MAPS 8 and composed of around 10000 piano sounds 7 Note that the number of degrees of freedom for the AR models is Hp/4 = Qp since half the poles are affected to the positive frequencies, the remaining poles being their complex conjugates. 8 MAPS stands for MIDI Aligned Piano Sounds and is available on request.…”

Section: B Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Multipitch Estimation of Piano Sounds Using a New Probabilistic Spectral Smoothness Principle

Emiya

Badeau

David

2010

IEEE Trans. Audio Speech Lang. Process.

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208

170

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Abstract-A new method for the estimation of multiple concurrent pitches in piano recordings is presented. It addresses the issue of overlapping overtones by modeling the spectral envelope of the overtones of each note with a smooth autoregressive model. For the background noise, a moving-average model is used and the combination of both tends to eliminate harmonic and sub-harmonic erroneous pitch estimations. This leads to a complete generative spectral model for simultaneous piano notes, which also explicitly includes the typical deviation from exact harmonicity in a piano overtone series. The pitch set which maximizes an approximate likelihood is selected from among a restricted number of possible pitch combinations as the one. Tests have been conducted on a large homemade database called MAPS, composed of piano recordings from a real upright piano and from high-quality samples.

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Section: A Implementation and Tuningmentioning

confidence: 99%

Section: B Evaluationmentioning

confidence: 99%

Multipitch Estimation of Piano Sounds Using a New Probabilistic Spectral Smoothness Principle

Emiya

Badeau

David

2010

IEEE Trans. Audio Speech Lang. Process.

Self Cite

208

170

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show abstract

“…Many real-life signals such as audio and voiced speech are quasi-periodic which makes the harmonic decomposition approach useful in practice. By using the harmonic model, we can write the signal vector as (24) where (25) In this approach, there is no interference since the harmonic model enables us to use all information embedded in in the estimation of . Moreover, the desired signal is the th entry of the vector , i.e., we can write it as (26) where is the th row of .…”

Section: B Harmonic Decompositionmentioning

confidence: 99%

Non-Causal Time-Domain Filters for Single-Channel Noise Reduction

Jensen

Benesty

Christensen

et al. 2012

IEEE Trans. Audio Speech Lang. Process.

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Abstract-In many existing time-domain filtering methods for noise reduction in, e.g., speech processing, the filters are causal. Such causal filters can be implemented directly in practice. However, it is possible to improve the performance of such noise reduction filtering methods in terms of both noise suppression and signal distortion by allowing the filters to be non-causal. Non-causal timedomain filters require knowledge of the future, and are therefore not directly implementable. If the observed signal is processed in blocks, however, the non-causal filters are implementable. In this paper, we propose such non-causal time-domain filters for noise reduction in speech applications. We also propose some performance measures that enable us to evaluate the performance of non-causal filters. Moreover, it is shown how some of the filters can be updated recursively. Using the recursive expressions, it is also shown that the output SNRs of the filters always increase as we increase the length of the filter when the desired signal is stationary. From both the theoretical and practical evaluations of the filters, it is clearly shown that the performance of time-domain filtering methods for noise reduction can be improved by introducing non-causality.Index Terms-Linearly constrained minimum variance (LCMV) filter, maximum signal-to-noise ratio (SNR) filter, minimum variance distortionless response (MVDR) filter, noise reduction, non-causal filters, performance measures, time-domain filtering, Wiener filter.

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“…Numerous algorithms have been proposed see, e.g. [1][2][3][4][5][6][7]. In real recorded signals, however, multi-pitch scenarios occur more frequently than single-pitch scenarios.…”

Section: Introductionmentioning

confidence: 99%

An iterative subspace-based multi-pitch estimation algorithm

et al. 2011

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a b s t r a c tIn this paper, we present an iterative method for estimation of pitches from signals containing multiple sources using subspace techniques. The resulting estimator is termed Iterative Harmonic MUltiple SIgnal Classification (I-HMUSIC). Different modifications of I-HMUSIC are proposed that improve upon the classical MUSIC algorithm, including a computationally efficient method for noise subspace updating I-HMUSIC and its modifications are evaluated and compared with both the Cramé r-Rao lower bound (CRLB) and non-iterative HMUSIC; good statistical performances have been obtained.

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A Parametric Method for Pitch Estimation of Piano Tones

Cited by 24 publications

References 14 publications

Multipitch Estimation of Piano Sounds Using a New Probabilistic Spectral Smoothness Principle

Multipitch Estimation of Piano Sounds Using a New Probabilistic Spectral Smoothness Principle

Non-Causal Time-Domain Filters for Single-Channel Noise Reduction

An iterative subspace-based multi-pitch estimation algorithm

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