Musical pitch estimation and discrimination of musical instruments using comb filters for transcription

Miwa, Taeko; Tadokoro, Yoshiaki; Saito, Tsutomu

doi:10.1109/mwscas.1999.867220

Cited by 15 publications

(12 citation statements)

References 5 publications

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“…Many techniques have been tried in multi-pitch or mixed music analysis in the past, such as a comb filter [4], statistical information of chords and their progression [5,6], iterative estimation and separation [7], linear models for the overtone series [8], parameter estimation of superimposed spectrum models [9,10], acoustic object modeling using GMM and estimation with an EM algorithm [11][12][13]. Specmurt analysis [14][15][16][17][18] is another method of multi-pitch analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Specmurt analysis [14][15][16][17][18] is another method of multi-pitch analysis. The method defines the observed spectrum as a convolution of instrumental information and the fundamental frequency distribution, and it differs from the methods listed above in terms of the introduction of the specmurt domain while [4] is processed in the time domain and [5][6][7][8][9][10][11][12][13] are processed in the spectrum domain.…”

Section: Introductionmentioning

confidence: 99%

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Mixed Music Analysis with Extended Specmurt

Nishimura

Nakashika

Takiguchi

et al. 2013

JSEA

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This paper introduces a mixed music analysis method using extended specmurt analysis. Conventional specmurt can only analyze a multi-pitch music signal from a single instrument and cannot analyze a mixed music signal that has several different types of instruments being played at the same time. To analyze a mixed music signal, extended specmurt is proposed. We regard the observed spectrum extracted from the mixed music as the summation of the observed spectra corresponding to each instrument. The mixed music has as many unknown fundamental frequency distributions as the number of instruments since the observed spectrum of a single instrument can be expressed as a convolution of the common harmonic structure and the fundamental frequency distribution. The relation among the observed spectrum, the common harmonic structure and the fundamental frequency distribution is transformed into a matrix representation in order to obtain the unknown fundamental frequency distributions. The equation is called extended specmurt, and the matrix of unknown components can be obtained by using a pseudo inverse matrix. The experimental result shows the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mixed Music Analysis with Extended Specmurt

Nishimura

Nakashika

Takiguchi

et al. 2013

JSEA

View full text Add to dashboard Cite

show abstract

“…Many techniques have been tried in multi-pitch analysis in the past, such as a comb filter [5], statistical information of chords and their progression [6,7], iterative estimation and separation [8], linear models for the overtone series [9], parameter estimation of superimposed spectrum models [10,11], acoustic object modeling using GMM and estimation with an EM algorithm [12]- [14]. Specmurt analysis [15]- [21] is another method of multi-pitch analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Specmurt analysis [15]- [21] is another method of multi-pitch analysis. The method defines the observed spectrum as a convolution of the fundamental frequency distribution and instrumental information, and it differs from those listed above in terms of the introduction of the specmurt domain while [5] is processed in the time domain and [6]- [14] are processed in the spectrum domain.…”

Section: Introductionmentioning

confidence: 99%

Sparseness Criteria of F0-Frequencies Selection for Specmurt-Based Multi-Pitch Analysis without Modeling Harmonic Structure

Nishimura

Nakashika

Takiguchi

et al. 2013

Journal of Signal Processing

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This paper introduces a multi-pitch analysis method using specmurt analysis without modeling the common harmonic structure pattern. Specmurt analysis is based on the idea that the fundamental frequency distribution is expressed as a deconvolution of the observed spectrum by the common harmonic structure pattern. To analyze the fundamental frequency distribution, the common harmonic structure needs to be modeled accurately because it is often unknown while the observed spectrum is known. It is considered impossible, however, to obtain a highly accurate model of the structure since it can vary slightly depending on the pitch. Therefore we propose a method to analyze the fundamental frequency distribution without modeling the harmonic structure. We note that each peak of the observed spectrum indicates the fundamental frequency or the harmonic tone. Hence, the fundamental frequency distribution can be regarded as the set which only has the peaks corresponding to fundamental frequencies. To find the set, we prepare many sets of the peaks of the spectrum, and obtain a large number of common harmonic structures. We evaluate the sparseness of these structures using L1 or L2 norm, and then select the set that has derived the sparsest structure as a solution. The experimental result shows the effectiveness of the proposed method.

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“…They are very effective for a noiseless target speech. For real environments, robust techniques, less sensitive to background noise and reverberation, have been developed, like comb-filter approach [12], instantaneous amplitude (IA) and frequency (IF) approaches [13][14][15]. Another type of F 0 estimation approach is to use the recursive state estimation scheme such as Kalman filter and particle filter [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Estimation and tracking of fundamental, 2nd and 3d harmonic frequencies for spectrogram normalization in speech recognition

Fujimoto¹,

Hamada²,

Kasprzak³

2012

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract.A stable and accurate estimation of the fundamental frequency (pitch, F 0) is an important requirement in speech and music signal analysis, in tasks like automatic speech recognition and extraction of target signal in noisy environment. In this paper, we propose a pitch-related spectrogram normalization scheme to improve the speaker -independency of standard speech features. A very accurate estimation of the fundamental frequency is a must. Hence, we develop a non-parametric recursive estimation method of F 0 and its 2nd and 3d harmonic frequencies in noisy circumstances. The proposed method is different from typical Kalman and particle filter methods in the way that no particular sum of sinusoidal model is used. Also we tend to estimate F0 and its lower harmonics by using novel likelihood function. Through experiments under various noise levels, the proposed method is proved to be more accurate than other conventional methods. The spectrogram normalization scheme makes a mapping of real harmonic structure to a normalized structure. Results obtained for voiced phonemes show an increase in stability of the standard speech features -the average within-phoneme distance of the MFCC features for voiced phonemes can be decreased by several percent.

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Musical pitch estimation and discrimination of musical instruments using comb filters for transcription

Cited by 15 publications

References 5 publications

Mixed Music Analysis with Extended Specmurt

Mixed Music Analysis with Extended Specmurt

Sparseness Criteria of F0-Frequencies Selection for Specmurt-Based Multi-Pitch Analysis without Modeling Harmonic Structure

Estimation and tracking of fundamental, 2nd and 3d harmonic frequencies for spectrogram normalization in speech recognition

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