Robust method of measurement of fundamental frequency by ACLOS: autocorrelation of log spectrum

Kunieda, Nobuyuki; Shimamura, Tetsuya; Suzuki, Jun

doi:10.1109/icassp.1996.540333

Cited by 15 publications

(7 citation statements)

References 4 publications

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“…Another category of algorithms measures periodicity in the frequency-domain, observing F0 from the intervals between the frequency partials of a sound. The spectrum autocorrelation method and its variants have been successfully used in several F0 estimators [37], [38]. An interesting difference between the time-domain and frequency-domain periodicity analysis methods is that the former methods are prone to errors in F0 halving and the latter to errors in F0 doubling.…”

Section: B Harmonicity Principlementioning

confidence: 99%

Multiple fundamental frequency estimation based on harmonicity and spectral smoothness

Klapuri

2003

IEEE Trans. Speech Audio Process.

245

140

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Section: B Harmonicity Principlementioning

confidence: 99%

Multiple fundamental frequency estimation based on harmonicity and spectral smoothness

Klapuri

2003

IEEE Trans. Speech Audio Process.

245

140

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“…Another class of FO estimators measure the periodicity of the Fourier spectrum of a sound [384], [380]. These methods are based on the observation that a harmonic sound has an approximately periodic magnitude spectrum, the period of which is the FO.…”

Section: Basic Principles Of Fo Estimationmentioning

confidence: 99%

Signal Processing Methods for Music Transcription

Klapuri¹,

Davy²

2006

252

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“…Time domain zero-crossings [19] and cepstrum measurements [20] are further methods proposed for monophonic fundamental frequencyestimation. Pitch estimation methods that work in frequencyd omain analyze spectral patterns to detect fundamental frequencies that best explain the harmonic structure [4,21].…”

Section: Introductionmentioning

confidence: 99%

Automatic Transcription of Recorded Music

Grosche

Schuller²,

Müller

et al. 2012

Acta Acustica united with Acustica

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The automatic transcription of music recordings with the objective to derive as core-liker epresentation from a givenaudio representation is afundamental and challenging task. In particular for polyphonic music recordings with overlapping sound sources, current transcription systems still have problems to accurately extract the parameters of individual notes specified by pitch, onset, and duration. In this article, we present amusic transcription system that is carefully designed to cope with various facets of music. One main idea of our approach is to consistently employam id-levelr epresentation that is based on am usically meaningful pitch scale. To achieve the necessary spectral and temporal resolution, we use amulti-resolution Fourier transform enhanced by an instantaneous frequencye stimation. Subsequently,h aving extracted pitch and note onset information from this representation, we employHidden Markov Models (HMM)for determining the note events in acontext-sensitive fashion. As another contribution, we evaluate our transcription system on an extensive dataset containing audio recordings of various genre. Here, opposed to manyp revious approaches, we do not only rely on synthetic audio material, bute valuate our system on real audio recordings using MIDI-audio synchronization techniques to automatically generate reference annotations. PACS no. 43.75.Xz, 43.75.zz ACTA ACUSTICA UNITED WITH ACUSTICA Grosche et al.:A utomatic transcription of music Vol. 98 (2012) ACTA ACUSTICA UNITED WITH ACUSTICA Vol. 98 (2012)

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Robust method of measurement of fundamental frequency by ACLOS: autocorrelation of log spectrum

Cited by 15 publications

References 4 publications

Multiple fundamental frequency estimation based on harmonicity and spectral smoothness

Multiple fundamental frequency estimation based on harmonicity and spectral smoothness

Signal Processing Methods for Music Transcription

Automatic Transcription of Recorded Music

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