Multiple fundamental frequency estimation using Gaussian smoothness

Pertusa, Antonio; Iñesta, José M.

doi:10.1109/icassp.2008.4517557

Cited by 36 publications

(31 citation statements)

References 6 publications

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“…with a −6 dB/octave slope for the harmonic spectra and a flat slope for the filter. Four reference mutiple pitch estimation algorithms were also evaluated: the correlogram-based algorithm in [6] implemented in the MIR Toolbox 1.2.1 [38], the spectral peak clustering algorithm in [7] implemented using the optimal parameter settings therein, the harmonic sum algorithm in [8] provided by its author, and the piano-specific AR model-based algorithm in [11], also provided by its author. The SONIC automatic piano music transcription algorithm [12] 2 was also considered.…”

Section: A Algorithms and Evaluation Metricsmentioning

confidence: 99%

“…Emmanuel Vincent and Nancy Bertin are with the METISS group, IRISA-INRIA, Campus de Beaulieu, 35042 Rennes Cedex, France (e-mail: emmanuel.vincent@irisa.fr; nancy.bertin@irisa.fr). Roland Badeau is with Institut Télécom, Télécom ParisTech, LTCI-CNRS, 37-39 rue Dareau, 75014 Paris, France (e-mail: roland.badeau@telecom-paristech.fr) relograms [6], spectral peak clustering [7] and harmonic sum [8] to probabilistic models [9], [10], [11], neural networks [12] and support vector machines [13]. One particular approach is to decompose the short-term magnitude or power spectrum of the signal into a sum of basis spectra representing individual pitches scaled by time-varying amplitudes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Harmonic Spectral Decomposition for Multiple Pitch Estimation

Vincent

Bertin

Badeau

2010

IEEE Trans. Audio Speech Lang. Process.

178

246

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Abstract-Multiple pitch estimation consists of estimating the fundamental frequencies and saliences of pitched sounds over short time frames of an audio signal. This task forms the basis of several applications in the particular context of musical audio. One approach is to decompose the short-term magnitude spectrum of the signal into a sum of basis spectra representing individual pitches scaled by time-varying amplitudes, using algorithms such as nonnegative matrix factorization (NMF). Prior training of the basis spectra is often infeasible due to the wide range of possible musical instruments. Appropriate spectra must then be adaptively estimated from the data, which may result in limited performance due to overfitting issues. In this article, we model each basis spectrum as a weighted sum of narrowband spectra representing a few adjacent harmonic partials, thus enforcing harmonicity and spectral smoothness while adapting the spectral envelope to each instrument. We derive a NMFlike algorithm to estimate the model parameters and evaluate it on a database of piano recordings, considering several choices for the narrowband spectra. The proposed algorithm performs similarly to supervised NMF using pre-trained piano spectra but improves pitch estimation performance by 6% to 10% compared to alternative unsupervised NMF algorithms.

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Section: A Algorithms and Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Harmonic Spectral Decomposition for Multiple Pitch Estimation

Vincent

Bertin

Badeau

2010

IEEE Trans. Audio Speech Lang. Process.

178

246

View full text Add to dashboard Cite

show abstract

“…Other notable feature-based AMT systems include the work by Pertusa and Iñesta [106], who proposed a computationally inexpensive method for multi-pitch detection which computes a pitch salience function and evaluates combinations of pitch candidates using a measure of distance between a harmonic partial sequence (HPS) and a smoothed HPS. Another approach for feature-based AMT was proposed in [113], which uses genetic algorithms for estimating a transcription by mutating the solution until it matches a similarity criterion between the original signal and the synthesized transcribed signal.…”

Section: Feature-based Multi-pitch Detectionmentioning

confidence: 99%

“…Typically, multiple-F0 estimation occurs using a pitch salience function (also called pitch strength function) or a pitch candidate set score function [74,106,127]. These feature-based techniques have produced the best results in the Music Information Retrieval Evaluation eXchange (MIREX) multi-F0 (frame-wise) and note tracking evaluations [7,91].…”

Section: Feature-based Multi-pitch Detectionmentioning

confidence: 99%

Automatic music transcription: challenges and future directions

et al. 2013

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. Abstract Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects. Permanent repository link

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“…The most straightforward solution is to analyze the time-frequency representation of audio and compute the fundamental frequencies [5]. Short-time Fourier transform (STFT) [6,7] and constant Q transform (CQT) [8] are two widely used time-frequency analysis methods. Zhou proposed resonator time-frequency image (RTFI), in which a first-order complex resonator filter bank is adopted to the analysis of music [9].…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Approach to Note-Level Transcription of a Specific Piano

2017

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This paper presents a two-stage transcription framework for a specific piano, which combines deep learning and spectrogram factorization techniques. In the first stage, two convolutional neural networks (CNNs) are adopted to recognize the notes of the piano preliminarily, and note verification for the specific individual is conducted in the second stage. The note recognition stage is independent of piano individual, in which one CNN is used to detect onsets and another is used to estimate the probabilities of pitches at each detected onset. Hence, candidate pitches at candidate onsets are obtained in the first stage. During the note verification, templates for the specific piano are generated to model the attack of note per pitch. Then, the spectrogram of the segment around candidate onset is factorized using attack templates of candidate pitches. In this way, not only the pitches are picked up by note activations, but the onsets are revised. Experiments show that CNN outperforms other types of neural networks in both onset detection and pitch estimation, and the combination of two CNNs yields better performance than a single CNN in note recognition. We also observe that note verification further improves the performance of transcription. In the transcription of a specific piano, the proposed system achieves 82% on note-wise F-measure, which outperforms the state-of-the-art.

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Multiple fundamental frequency estimation using Gaussian smoothness

Abstract: A multiple fundamental frequency estimator is presented in this work. At each time frame, a set of fundamental frequencies is found in a frame by frame analysis taking into account the spectral smoothness measure described in [1] and the information contained in adjacent frames.

Cited by 36 publications

References 6 publications

Adaptive Harmonic Spectral Decomposition for Multiple Pitch Estimation

Adaptive Harmonic Spectral Decomposition for Multiple Pitch Estimation

Automatic music transcription: challenges and future directions

A Two-Stage Approach to Note-Level Transcription of a Specific Piano

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