CN108536871B - Music main melody extraction method and device based on particle filtering and limited dynamic programming search range - Google Patents
Music main melody extraction method and device based on particle filtering and limited dynamic programming search range Download PDFInfo
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Abstract
A method for extracting the main melody of music by particle filter and limiting the search range of dynamic programming includes such steps as randomly throwing particles under the frame of particle filter, modeling the likelihood function of particle filter as the product of the square sum of amplitude of each subharmonic and the smoothness factor of harmonic, fitting the transition probability function by logistic distribution, recursively estimating the pitch sequence of main melody frame by frame according to the prediction and update equations of particle filter, smoothing, taking the upper and lower octave range of each frame pitch as the search range of main melody pitch, and executing dynamic programming algorithm in limited range to obtain the final pitch sequence estimation.
Description
Technical Field
The invention belongs to the field of music information retrieval, and relates to a method and a device for extracting a music main melody by particle filtering and limiting a dynamic programming search range.
Background
The extraction of main melody in the field of music information retrieval is to estimate the pitch sequence of main melody from music signal, mainly based on the significance and time sequence continuity of main melody components in music. The traditional main melody extraction method mostly decomposes the main melody extraction problem into two sub-problems of polyphonic height estimation and melody contour construction, so that two characteristics cannot be considered simultaneously, and the high correlation of continuous frame pitches is not considered in the polyphonic height estimation stage. Jo et al propose modeling the theme extraction problem using a Bayesian filtering framework and estimating parameters by means of a particle filtering method, but this method still requires multi-pitch estimation before particle filtering and has more parameters to estimate (S.Jo, C.D.Yoo, and A.Doucet, Melody tracking based on sequential Bayesian model, IEEE Journal of Selected Topics in Signal Processing,2011,5(6): 1216-.
In addition, the music signal is very complex and has the essential characteristic of typical non-stationarity, that is, the power of some short-time strong accompaniment sounds may exceed the main melody components, and the pitch frequency distribution of the main melody is wide and includes several octaves, so that the existing scheme has wide error of wrong pitch and octaves from the accompaniment sounds in the main melody extraction.
Disclosure of Invention
In order to solve the problems of taking significance and smoothness into account and reducing the influence of short-time strong accompaniment in the music main melody extraction method, the invention provides the following scheme:
a method for extracting music main melody includes such steps as randomly throwing particles in a particle filter frame, modeling the likelihood function of particle filter as the product of the square sum of amplitude of each subharmonic and harmonic smoothness factor, fitting the transition probability function with logistic distribution, recursively estimating the pitch sequence of main melody frame by frame according to prediction and update equations of particle filter, smoothing, taking the upper and lower octave ranges of each frame pitch after smoothing as the search range of main melody pitch, and executing dynamic programming algorithm in limited range to obtain the final pitch sequence estimation of main melody.
A device for extracting a musical key melody with particle filtering and defining a dynamically planned search range, storing a plurality of instructions adapted to be loaded and executed by a processor: randomly throwing particles in a particle filter frame, modeling a likelihood function of the particle filter as a product of the sum of squares of amplitude of each subharmonic and a harmonic smoothness factor, fitting a transition probability function by using logistic distribution, recursively finishing rough estimation of a main melody pitch sequence frame by using a prediction equation and an update equation of the particle filter, then smoothing the rough estimation, taking upper and lower octave ranges of each frame pitch after smoothing as a search range of the main melody pitch of the frame, and executing a dynamic programming algorithm in a limited range to obtain final estimation of the main melody pitch sequence.
The significance and the smoothness of the melody are fused into a particle filter framework, the significance is embodied by the sum of squares of the amplitude of each subharmonic of a likelihood function in the particle filter, the smoothness is realized by fitting the transition probability into a logistic distribution mode in the particle filter, and therefore the framework simultaneously completes multi-pitch estimation and melody contour construction, and the significance and the smoothness are both realized.
According to the method, rough estimation of the pitch sequence of the main melody is obtained through particle filtering, then smoothing is carried out on the rough estimation, the searching range of the dynamically planned pitch sequence of the main melody is limited, and final estimation of the pitch sequence of the main melody is obtained within the limited range through dynamic planning, so that strong accompaniment influence outside the searching range is reduced; according to the invention, a penalty factor is introduced in the dynamic planning stage, so that the influence of short-time strong accompaniment is further reduced.
In the likelihood function of the particle filter, the harmonic smoothness factor is introduced, so that the octave error is reduced; in the dynamic planning stage of the invention, the significance value of each frequency point is defined as the weighted sum of the amplitudes of the harmonic components in each order in the pitch searching range of the main melody, thereby further reducing the octave error.
Detailed Description
The utility model relates to a music melody extraction method, is used for solving the problem that traditional melody extraction method can't compromise significance and time sequence continuity, is used for reducing the wrong pitch and the octave error of the sound of accompanying that music signal non-stationarity leads to simultaneously.
The scheme is as follows: randomly throwing particles in a particle filter frame, modeling a likelihood function of the particle filter as a product of the sum of squares of amplitude of each subharmonic and a harmonic smoothness factor, fitting a transition probability function by using logistic distribution, recursively finishing rough estimation of a main melody pitch sequence frame by using a prediction equation and an update equation of the particle filter, then smoothing the rough estimation, taking upper and lower octave ranges of each frame pitch after smoothing as a search range of the main melody pitch of the frame, and executing a dynamic programming algorithm in a limited range to obtain final estimation of the main melody pitch sequence.
The method fuses significance and time sequence continuity into the same frame; a two-stage scheme combining particle filtering and dynamic programming is adopted to limit the possible range of the final main melody pitch sequence and reduce the short-time strong accompaniment influence in the main melody pitch estimation; and due to the adoption of harmonic smoothness factors in particle filtering and the significance function of weighted summation in dynamic programming, the octave error in the pitch estimation of the main melody is reduced.
The method specifically comprises the following steps:
s1, calculating the constant Q transformation of the music signal;
the constant Q transformation method for calculating the music signal comprises the following steps: non-stationary music signals are framed and then amplitude values for each frequency point distributed logarithmically are calculated using a constant Q transform, with 36 logarithmically distributed frequency points per octave range.
S2, obtaining a rough estimation of the main melody pitch sequence based on the particle filtering;
the method for obtaining the rough estimation of the main melody pitch sequence based on the particle filtering comprises the following steps:
initialization:
wherein
Represents the initial pitch of the ith particle
Obey [110Hz,1200Hz]Uniformly distributed in the range, NpThe number of particles is 300, which is the number of particles,
representing the ith particle weight.
And (3) iterative solution:
(1) predicting the pitch frequency of the melody of the t frame according to the transition probability:
according to the result of the statistical analysis of the pitch transition probability in the actual music, the pitch transition probability is modeled into a logistic distribution, and the probability density function is as follows:
wherein x ═f0,t/f0,t-1，f0,tThe pitch frequency of the melody in the tth frame is 1.00003 μ, and 0.0055045 s.
(2) Calculating particle weights
Wherein, P and S respectively represent the power and harmonic smoothness factors of the ith particle of the t frame, which are respectively defined as:
wherein A ism,tFor the ith particle pitch frequency f of the t frame0,tThe amplitude of the corresponding mth harmonic component, H, is the maximum harmonic order.
(3) Normalizing particle weights
(4) Calculating the effective particle number
If it is
Reinitializing particle pitch
Jumping to the step (1); otherwise, jumping to step (5), wherein fminAnd fmaxRespectively, a lower limit and an upper limit of the frequency of the pitch of the main melody.
(5) Calculating a current melody pitch estimate
Wherein
The average value of the sound frequency of each particle with the largest weight is obtained.
(6) Resampling the particles according to the normalized weight, and setting the weight of all the resampled particles to be 1/Np。
(7) Let t equal to t +1, if t is less than or equal to Nfrm(NfrmThe total frame number of the audio), jumping to the step (1); otherwise, the iterative process is ended.
After finishing the steps (1) to (7), the method comprises
Constituting a rough estimate of the pitch sequence of the main melody.
S3, obtaining a main melody pitch sequence smoothing estimation sequence;
the method for obtaining the main melody pitch sequence smooth estimation sequence comprises the following steps: and averaging the rough estimation results of the main melody pitch sequence within the time of 100 milliseconds before and after the rough estimation results of the main melody pitch sequence to be used as the smooth estimation values of the main melody pitch sequence of the current frame, wherein the smooth estimation values of the frames form the smooth estimation sequence of the main melody pitch sequence.
S4, obtaining the final pitch sequence estimation of the main melody;
the method for obtaining the final main melody pitch sequence estimation comprises the following steps:
(1) and taking the upper and lower octave range of the smooth estimation value of each frame of the main melody pitch sequence as the search range of the main melody pitch of the frame.
(2) Calculating the significance value S of each frequency point in the pitch search range of each frame of the main melodyt′(f)：
Wherein N ish10 is the maximum harmonic order, | Xt(hf) | is the h-th harmonic amplitude of frequency f.
(3) S 'for each frame't(f) Normalization is carried out to obtain St(f):
(4) And obtaining a final pitch sequence value of the main melody by using a dynamic programming algorithm, wherein the initial conditions of the dynamic programming are as follows:
D(1,ft,j)＝S1(fj) (11)
wherein D (t, f)t,j) Is the t-th frame to take ft,jAs a cumulative cost function of melody pitch, S1(fj) The value of the significance function of the 1 st frame at the jth frequency point is obtained from the formula (10). Then the recursive formula for solving the optimal solution of the main melody pitch sequence is:
D(t,ft,j)＝St(fj)+max{D(t-1,ft-1,k)-λd(ft-1,k,ft,j)},t＝1,...,Nfrm (12)
wherein f ist,jIs the possible value of the pitch of the main melody in the t-th frame, ft-1,kIs the pitch estimation value of the dominant melody in the t-1 th frame, d (f)t-1,k,ft,j) Is ft-1,kAnd ft,jλ is 0.05, which is a penalty factor. Wherein f ist,j、ft-1,kJ in (1),kDifferent notation is used because the t-1 frame is the main frame when iterating to the t frameMelody pitch estimation ft-1,kHas been obtained and is a definite value, and the possible value f of the pitch of the main melody in the t-th framet,jAre not yet identified and thus are distinguished.
And continuously iterating the formula (12) to obtain the final pitch sequence estimation of the main melody.
The significance and the smoothness of the melody are fused into a particle filter framework, the significance is embodied by the sum of squares of the amplitude of each subharmonic of a likelihood function in the particle filter, the smoothness is realized by fitting the transition probability into a logistic distribution mode in the particle filter, and therefore the framework simultaneously completes multi-pitch estimation and melody contour construction. In order to reduce the short-term strong accompaniment influence, the invention solves the problem by two ways: (1) the rough estimation of the main melody pitch sequence is obtained by adopting particle filtering, then the rough estimation is performed with smoothing treatment to limit the dynamically planned main melody pitch searching range, and the final estimation of the main melody pitch sequence is obtained in the limited range by the dynamic planning, so that the strong accompaniment influence outside the searching range is reduced; (2) and a penalty factor is introduced in the dynamic planning stage to further reduce the influence of short-time strong accompaniment. In order to reduce the octave error, the invention solves the problem by two ways: (1) introducing a harmonic smoothness factor in a likelihood function of particle filtering to reduce octave errors; (2) and in the dynamic programming stage, the significance value of each frequency point is defined as the weighted sum of the amplitudes of the subharmonic components in the pitch searching range of the main melody, so that the octave error is further reduced. Therefore, the method and the device have the advantages that the significance and the smoothness are simultaneously fused into the framework of particle filtering, and the influence of short-time strong accompaniment and the octave error are reduced.
That is, according to the above scheme, the scheme described in this embodiment has the following beneficial effects: the significance and the smoothness are fused into a particle filter frame, so that multi-pitch estimation and melody contour construction can be completed simultaneously; the final estimation of the main melody pitch sequence is finished through a scheme combining particle filtering and dynamic planning, the searching range of the main melody pitch is limited in the dynamic planning, and a penalty factor is introduced, so that the influence of short-time strong accompaniment is reduced; octave errors in the melody pitch estimation are reduced by introducing harmonic smoothness factors in the particle filtering and weighted summation significance functions in the dynamic programming.
Compared with the schemes disclosed by Jo and the like mentioned in the background of the disclosure, the music theme extraction method described in this embodiment has the following differences:
1. the likelihood function and the transition probability density of the method of the invention are different from those of the method of Jo and the like, and the method has different pitch significance description methods and transition probability density expressions, particularly probability density; the method also introduces a harmonic smoothness factor in the likelihood function to reduce the octave error;
the method of Jo, etc. first uses polyphonic height estimation to obtain the pitch candidates of the main melody for each frame, and then uses the particle filtering method to obtain the final pitch sequence of the main melody. In some situations where the power of the ending part of the main melody note may be less than that of some strong accompaniment, the method of Jo and the like still cannot eliminate these wrong pitches, and the accuracy is poor;
3. the two methods have different flows, the method of the invention is that particle filtering is firstly carried out and then dynamic planning is carried out, and the method of Jo and the like is that multi-tone high estimation is firstly carried out and then particle filtering is carried out;
4. the method has fewer parameters and easy parameter adjustment; the method of Jo, etc. has many parameters and is difficult to adjust.
In view of the above, music has strong non-stationary characteristics, and strong accompaniment sounds may exceed the main melody power for some short time, for example, at the end of some notes, the main melody power may be less than some accompaniment sounds; in addition, octave errors often exist, and during a certain note playing, the estimated pitch is switched between different octaves due to the change of the amplitude proportion of each harmonic. Therefore, if the approximate range of the main melody can be determined in advance, the accompaniment pitch and octave errors outside the range can be filtered out.
The method proposed by Jo et al firstly uses polyphonic pitch estimation to obtain melody pitch candidates of each frame, and then uses a particle filtering method to obtain a final main melody pitch sequence. This method still fails to solve the above-mentioned problems. The method provided by the invention firstly estimates the melody pitch by using particle filtering, and also has the problems, but most of the estimation results of the particle filtering method are accurate, so that the influence of error estimation is weakened by smoothing the estimation sequence, the approximate range of the melody pitch can be still determined, and the short-time strong accompaniment pitch and octave errors outside the range can be filtered by processing in a limited range by using dynamic programming.
The above description is only for the purpose of creating a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution and the inventive concept of the present invention within the technical scope of the present invention.
Claims (1)
1. A method for extracting music main melody through particle filtering and limiting a dynamic programming search range is characterized in that: randomly throwing particles in a particle filter frame, modeling a likelihood function of the particle filter as a product of the sum of squares of amplitude of each subharmonic and a harmonic smoothness factor, fitting a transition probability function by using logistic distribution, recursively finishing rough estimation of a main melody pitch sequence frame by using a prediction equation and an update equation of the particle filter, then smoothing the rough estimation, taking upper and lower octave ranges of each frame pitch after smoothing as a search range of the main melody pitch of the frame, and executing a dynamic programming algorithm in a limited range to obtain final estimation of the main melody pitch sequence; a plurality of instructions are stored, the instructions adapted to be loaded and executed by a processor to: randomly throwing particles in a particle filter frame, modeling a likelihood function of the particle filter as a product of the sum of squares of amplitude of each subharmonic and a harmonic smoothness factor, fitting a transition probability function by using logistic distribution, recursively finishing rough estimation of a main melody pitch sequence frame by using a prediction equation and an update equation of the particle filter, then smoothing the rough estimation, taking upper and lower octave ranges of each frame pitch after smoothing as a search range of the main melody pitch of the frame, and executing a dynamic programming algorithm in a limited range to obtain final estimation of the main melody pitch sequence;
the method for obtaining the rough estimation of the main melody pitch sequence based on the particle filtering comprises the following steps:
initialization:
wherein
Represents the initial pitch of the ith particle
Obey [110Hz,1200Hz]Uniformly distributed in the range, NpThe number of particles is 300, which is the number of particles,
the weight of the ith particle is represented,
and (3) iterative solution:
(1) predicting the pitch frequency of the melody of the t frame according to the transition probability:
according to the result of the statistical analysis of the pitch transition probability in the actual music, the pitch transition probability is modeled into a logistic distribution, and the probability density function is as follows:
wherein x ═ f0,t/f0,t-1，f0,tIs the pitch frequency of the tth frame melody, and μ is 1.00003, s is 0.0055045;
(2) calculating particle weights
Wherein, P and S respectively represent the power and harmonic smoothness factors of the ith particle of the t frame, which are respectively defined as:
wherein A ism,tFor the ith particle pitch frequency f of the t frame0,tThe amplitude of the corresponding mth harmonic component, H is the maximum harmonic frequency;
(3) normalizing particle weights
(4) Calculating the effective particle number
If it is
Reinitializing particle pitch
Jumping to the step (1); otherwise, jumping to step (5), wherein fminAnd fmaxThe frequency lower limit and the frequency upper limit of the main melody pitch are respectively;
(5) calculating a current melody pitch estimate
Wherein
The average value of the sound frequency of each particle with the largest weight value;
(6) resampling the particles according to the normalized weight, and setting the weight of all the resampled particles to be 1/Np；
(7) Let t equal to t +1, if t is less than or equal to Nfrm(NfrmThe total frame number of the audio), jumping to the step (1); otherwise, ending the iteration process;
after finishing the steps (1) to (7), the method comprises
Constituting a rough estimate of the pitch sequence of the main melody.
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| CN105957538B (en) * | 2016-05-09 | 2019-06-11 | 大连民族大学 | Polyphony Melody extraction method based on conspicuousness |
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