A Computationally Efficient Method for Polyphonic Pitch Estimation

Zhou, Ruohua; Reiss, Joshua D.; Mattavelli, Marco; Zoia, Giorgio

doi:10.1155/2009/729494

Cited by 10 publications

(21 citation statements)

References 15 publications

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“…The spectral smoothness principle has successfully been used in different ways in the literature [7,[26][27][28][29]. A novel smoothness measure based on the convolution of the hypothetical harmonic pattern with a Gaussian window is proposed.…”

Section: Methodsmentioning

confidence: 99%

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Efficient methods for joint estimation of multiple fundamental frequencies in music signals

Pertusa

Iñesta

2012

EURASIP J. Adv. Signal Process.

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This study presents efficient techniques for multiple fundamental frequency estimation in music signals. The proposed methodology can infer harmonic patterns from a mixture considering interactions with other sources and evaluate them in a joint estimation scheme. For this purpose, a set of fundamental frequency candidates are first selected at each frame, and several hypothetical combinations of them are generated. Combinations are independently evaluated, and the most likely is selected taking into account the intensity and spectral smoothness of its inferred patterns. The method is extended considering adjacent frames in order to smooth the detection in time, and a pitch tracking stage is finally performed to increase the temporal coherence. The proposed algorithms were evaluated in MIREX contests yielding state of the art results with a very low computational burden.

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Section: Methodsmentioning

confidence: 99%

“…Some techniques rely on the mid-level representation, trying to emphasize the underlying fundamental frequencies by applying signal processing transformations to the input signal [5][6][7]. Supervised [8,9] and unsupervised [10,11] learning techniques have also been investigated for this task.…”

Section: Introductionmentioning

confidence: 99%

Efficient methods for joint estimation of multiple fundamental frequencies in music signals

Pertusa

Iñesta

2012

EURASIP J. Adv. Signal Process.

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“…To this end, several different algorithms have been proposed: perceptually motivated models that attempt to model human audition (Klapuri, 2005); salience methods, which transform the audio signal to accentuate the underlying fundamental frequencies (Klapuri, 2006;Zhou et al, 2009); iterative estimation methods, which iteratively select a predominant fundamental from the frequency spectrum and then subtract an estimate of its harmonics from the residual spectrum until no fundamental frequency candidates remain (Klapuri, 2006); and joint estimation, which holistically selects fundamental frequency candidates that, together, best describe the observed frequency domain of the input audio signal (Yeh, Roebel & Rodet, 2010).…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, the guitar recordings in the test set of each fold are transcribed by a digital signal processing polyphonic transcription algorithm developed by Zhou et al (2009), which was evaluated in the 2008 MIREX and received an f -measure of 0.76 on a dataset of 30 synthesized and real piano recordings (Zhou & Reiss, 2008). The Zhou et al (2009) polyphonic transcription algorithm processes audio signals at a sampling rate of 44,100 Hz.…”

Section: Note Event Evaluationmentioning

confidence: 99%

Isolated guitar transcription using a deep belief network

Burlet

Hindle

2017

PeerJ Computer Science

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Music transcription involves the transformation of an audio recording to common music notation, colloquially referred to as sheet music. Manually transcribing audio recordings is a difficult and time-consuming process, even for experienced musicians. In response, several algorithms have been proposed to automatically analyze and transcribe the notes sounding in an audio recording; however, these algorithms are often general-purpose, attempting to process any number of instruments producing any number of notes sounding simultaneously. This paper presents a polyphonic transcription algorithm that is constrained to processing the audio output of a single instrument, specifically an acoustic guitar. The transcription system consists of a novel note pitch estimation algorithm that uses a deep belief network and multi-label learning techniques to generate multiple pitch estimates for each analysis frame of the input audio signal. Using a compiled dataset of synthesized guitar recordings for evaluation, the algorithm described in this work results in an 11% increase in the f-measure of note transcriptions relative to Zhou et al.'s (2009) transcription algorithm in the literature. This paper demonstrates the effectiveness of deep, multi-label learning for the task of polyphonic transcription.

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“…This field of research is known as Music Information Retrieval (MIR) and it has significantly gained in interest in recent years. In this domain, music onset detection forms the basis of many higher level processing tasks, including polyphonic transcription [2], beat tracking [3] and interactive musical accompaniment [4].…”

Section: Introductionmentioning

confidence: 99%

Onset Event Decoding Exploiting the Rhythmic Structure of Polyphonic Music

Degara

Davies

Peña

et al. 2011

IEEE J. Sel. Top. Signal Process.

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Abstract-In this paper we propose a rhythmically-informed method for onset detection in polyphonic music. Music is highly structured in terms of the temporal regularity underlying onset occurrences and this rhythmic structure can be used to locate sound events. Using a probabilistic formulation, the method integrates information extracted from the audio signal and rhythmic knowledge derived from tempo estimates in order to exploit the temporal expectations associated with rhythm and make musically meaningful event detections. To do so, the system explicitly models note events in terms of the elapsed time between consecutive events and decodes the most likely sequence of onsets that led to the observed audio signal. In this way, the proposed method is able to identify likely time instants for onsets and to successfully exploit the temporal regularity of music. The goal of this work is to define a general framework to be used in combination with any onset detection function and tempo estimator. The method is evaluated using a dataset of music that contains multiple instruments playing at the same time, including singing and different music genres. Results show that the use of rhythmic information improves the commonly used adaptive thresholding onset detection method which only considers local information. It is also shown that the proposed probabilistic framework successfully exploits rhythmic information using different detection functions and tempo estimation algorithms.

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A Computationally Efficient Method for Polyphonic Pitch Estimation

Cited by 10 publications

References 15 publications

Efficient methods for joint estimation of multiple fundamental frequencies in music signals

Efficient methods for joint estimation of multiple fundamental frequencies in music signals

Isolated guitar transcription using a deep belief network

Onset Event Decoding Exploiting the Rhythmic Structure of Polyphonic Music

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