Patent Publication Number: US-7593847-B2

Title: Pitch detection method and apparatus

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
   This application claims the benefit of Korean Patent Application No. 2003-74923, filed on Oct. 25, 2003 in the Korean Intellectual Property Office, the disclosure of which is incorporated herein in its entirety by reference. 
   BACKGROUND OF THE INVENTION 
   1. Field of the Invention 
   The present invention relates to pitch detection, and more particularly, to a method and apparatus for detecting a pitch by decomposing voice data into even symmetrical components and then obtaining segment correlation values. 
   2. Description of the Related Art 
   In the voice signal processing field such as voice recognition, synthesis and analysis, it is important to accurately detect a fundamental frequency, that is, a pitch period. If the fundamental frequency of a voice signal can be accurately detected, effects caused by a speaker&#39;s voice in voice recognition can be reduced such that the accuracy of the recognition can be raised, and when the voice is synthesized, naturalness and individual characteristics can be easily modified or maintained. In addition, in voice analysis, if the voice is analyzed in synchronization with a pitch, accurate vocal tract parameters in which the effect of a glottis is removed can be obtained. 
   Thus, performing pitch detection in a voice signal is an important part and methods for pitch detection have been suggested in a variety of ways. These methods can be broken down into time domain detection, frequency domain detection, and time-frequency hybrid domain detection. 
   Time domain detection is a method emphasizing periodicity of waveforms and then detecting a pitch by a decision logic, and includes a parallel processing method, average magnitude difference function (hereinafter referred to as AMDF), and auto-correlation method (hereinafter referred to as ACM). These methods are usually performed in time domain such that transforming of the domain is not needed and only simple operations such as addition, subtraction, and comparison logics are needed. However, when a phoneme stretches over a transition interval, signal power levels in a frame change severely and the pitch period changes. Accordingly, detection of a pitch is difficult and influenced by a formant in that interval. In particular, when voice is mixed with noise, decision logic for pitch detection is complicated such that detection error increases. More specifically, in the ACM method, it is highly probable that pitch determination errors, including mistaking a first formant for a pitch, pitch doubling, and pitch halving, occur. 
   Frequency domain detection is a method detecting the fundamental frequency of voiced sound by measuring harmonic intervals of a voice spectrum, and a harmonic analysis method, Lifter method, and Comb-filtering method have been suggested as frequency domain detection. Since a spectrum is generally obtained within a frame with a duration of 20 to 40 ms, even if phoneme transition/change or background noise occurs within the frame, the influence is not great. However, the detection processing needs to transform to a frequency domain and therefore, the calculation is complicated. If the number of FFT pointers is increased in order to raise the accuracy of a fundamental frequency, the processing time increases proportionately and it is difficult to accurately detect the changed characteristic. 
   Time-frequency hybrid domain detection is based on the advantages of the two methods, calculation time reduction and pitch accuracy of the time domain detection and frequency domain detection&#39;s capability of accurately obtaining a pitch despite background noise or phoneme change. This includes the Cepstrum method, and the spectrum comparison method. However, in these methods, when time domain and frequency domain are alternately visited, errors increase and can affect pitch detection accuracy. In addition, since the time and frequency domains are applied at the same time, the calculation is complicated. 
   SUMMARY OF THE INVENTION 
   According to an aspect of the present invention there is provided a pitch detection method and apparatus by which voice data contained in a single frame is decomposed into even symmetrical components and a maximum segment correlation value between a reference point and each of local peaks is determined as a pitch period. 
   According to another aspect of the present invention, there is provided a pitch detection apparatus including: a data rearrangement unit which rearranges voice data based on a center peak of the voice data included in a single frame; a decomposition unit which decomposes the rearranged voice data into even symmetrical components based on the center peak; a pitch determination unit which obtains a segment correlation value between a reference point and at least one or more local peaks in relation to the even symmetrical components, and determines the location of a local peak corresponding to a maximum segment correlation value among the obtained segment correlation values, as a pitch period. 
   According to another aspect of the present invention, there is provided a pitch detection method including: decomposing voice data into even symmetrical components based on a center peak of the voice data included in a single frame; obtaining a segment correlation value between a reference point and at least one or more local peaks in relation to the even number symmetrical components; and determining the location of a local peak corresponding to a maximum segment correlation value among the obtained segment correlation values, as a pitch period. 
   According to another aspect of the present invention, the method can be implemented by a computer readable recording medium having embodied thereon a computer program for executing the method in a computer. 
   Additional aspects and/or advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     These and/or other aspects and advantages of the invention will become apparent and more readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which: 
       FIG. 1  is a block diagram of the structure of an embodiment of a pitch detection apparatus according to an aspect of the present invention; 
       FIGS. 2A through 2C  are waveforms of respective modules shown in  FIG. 1 ; and 
       FIG. 3  is a flowchart of operations performed by an embodiment of a pitch detection method according to an aspect of the present invention. 
   

   DETAILED DESCRIPTION OF THE EMBODIMENTS 
   Reference will now be made in detail to the embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. The embodiments are described below to explain the present invention by referring to the figures. 
     FIG. 1  is a block diagram of the structure of an embodiment of a pitch detection apparatus according to an aspect of the present invention. The pitch detection apparatus includes a data rearrangement unit  110 , a decomposition unit  120 , and a pitch determination unit  130 . The data rearrangement unit  110  includes a filter unit  111 , a frame forming unit  113 , a center peak detection unit  115 , and a data transition unit  117 . The pitch determination unit  130  includes a local peak detection unit  131 , a correlation value calculation unit  133 , and a pitch period determination unit  135 . Operation of the pitch detection apparatus shown in  FIG. 1  will now be explained in relation to the waveforms shown in  FIGS. 2A to 2C . 
   Referring to  FIG. 1 , in the data rearrangement unit  110 , the filter unit  111  is implemented by an infinite impulse response (IIR) or finite impulse response (FIR) digital filter, and is a low pass filter, for example, with a cutoff frequency having a frequency characteristic of 230 Hz. The filter unit  111  performs low pass filtering of voice data, which is analog-digital data, to remove high frequency components, and finally outputs voice data with a waveform as shown in  FIG. 2A . 
   The frame forming unit  113  divides voice data provided by the filter unit  111 , in predetermined time units, and forms frame units. For example, when analog-to-digital conversion is performed and the sampling rate is 20 kHz, if 40 msec is set as a predetermined time unit, a total of 800 samples form one frame. Since a pitch is usually between 50 Hz and 400 Hz, the number of samples required to detect a pitch, that is, a unit time, is set to twice 50. Hz, that is, 25 Hz or 40 msec. At this time, preferably, but not required, the interval between adjacent frames is 10 msec. In the above example, when the sampling rate is 20 kHz, the frame forming unit  113  forms a first frame with 800 samples of voice data, and skips over the first 200 samples in the first frame, and then forms a second frame with 800 samples by adding the next 600 samples in the first frame and the next 200 new samples. 
   The center peak determination unit  115  multiplies voice data as shown in  FIG. 2A , by a predetermined weight window function in time domain, and determines a location where the absolute value of the result of the multiplication is a maximum, as a center peak. Types of weight windows available to use include Triangular, Hanning, Hamming, Blackmann, Welch, and Blackmann-Harris windows. 
   The data transition unit  117  shifts the voice data shown in  FIG. 2A  on the basis of the center peak determined in the center peak determination unit  115  so that the center peak is placed at the center of the voice data, and outputs a signal with a waveform as shown in  FIG. 2B . 
   The decomposition unit  120  decomposes the voice data rearranged by the data transition unit  117 , into even symmetrical components on the basis of the center peak, and outputs a signal with a waveform as shown in  FIG. 2C . This will now be explained in more detail. 
   First, it is assumed that x(n) is voice data provided by the frame forming unit  113  and rearranged in the data transition unit  117 , and is a periodical signal having period N 0 . That is, for all integer k, x(n±kN 0 )=x(n). This periodical signal can be decomposed into even and odd symmetrical components, and assuming that s(n) is a symmetrical signal, the following equation 1 is valid:
 
 s ( n )= s ( N−n )=2 x   e ( n )  (1)
 
   Here, x e (n) denotes even symmetrical components, and can be expressed as the following equation 2. Here, N denotes the number of the entire samples of one frame. 
   
     
       
         
           
             
               
                 
                   
                     
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   Signal s(n) generated by equation 1 is symmetrical in relation to period N 0  as well as frame length N, and becomes a periodical signal with period N 0 . That is, like periodical signal x(n), s(n±kN 0 )=s(n). This can be proved by the following equation 3: 
   
     
       
         
           
             
               
                 
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   Meanwhile, in order to more easily explain the symmetry of s(n) in period N 0 , instead of s(n)=s(N 0 −n), s(N/2+n)=s(N/2+N 0 −n) will now be proved. That is, it will be proved that s(n) is a symmetrical and periodical signal with respect to the center part of one frame. When each of s(N/2+n) and s(N/2+N 0 −n) is explained by x(n), those can be expressed by the following equations 4 and 5: 
   
     
       
         
           
             
               
                 
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                 ( 
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   That is, it can be shown that the right-hand side of the equation 4 is the same as the right-hand side of the equation 5. Accordingly, it can be seen that the even symmetrical components of periodical signal x(n) become a symmetrical and periodical signal within one period. 
   Meanwhile, in order to prevent the possibility of pitch doubling in which the pitch period detected next is a multiple of a first detected pitch period, the decomposition unit  120  multiplies voice data rearranged in the data transition unit  117  by a predetermined weight window function, and then can decompose the voice data into even symmetrical components on the basis of the center peak. At this time, the weight window function used may be Hamming window or Hanning window. As shown in  FIG. 2C , only half of the entire even symmetrical components are used in order to avoid information redundancy in the following process. 
   In the pitch determination unit  130 , the local peak detection unit  131  detects local peaks with a value greater than 0, that is, candidate pitches, from the even number symmetrical components as shown in  FIG. 2C  provided by the decomposition unit  120 . If the actual value of the center peak determined in the center peak determination unit  115  is a negative number, even symmetrical components are multiplied by −1 and then, local peaks with a value greater than 0, that is, candidate pitches, are detected. 
   The correlation value calculation unit  133  obtains a segment correlation value, ρ(L), between a reference point, that is, sample location ‘0’ and each of local peaks (L) detected by the local peak detection unit  131 . At this time, by applying any one of the methods disclosed in an article by Y. Medan, E. Yair, and D. Chazan, “Super resolution pitch determination of speech signals” (IEEE Trans. Signal Processing, ASSP-39(1), pp 40-48, 1991), and the method disclosed in an article by P. C. Bagshaw, S. M. Hiller, and M. A. Jack, “Enhanced pitch tracking and the processing of F0 contours for computer aided intonation teaching” (pp. 1003-1006, Proc. 3rd. European Conference on Speech Communication and Technology, vol. 2, Berlin), the segment correlation values can be obtained. When the method shown by Y. Medan et al. is used, it can be shown as the following equation 6: 
   
     
       
         
           
             
               
                 
                   
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   Here, L denotes the location of each local peak, that is, a sample location. 
   The pitch period determination unit  135  selects a maximum segment correlation value among the segment correlation values between a reference point and each local peak calculated in the correlation value calculation unit  133 , and if the maximum segment correlation value is greater than a predetermined threshold, determines the location of the local peak used to obtain the maximum segment correlation value, as a pitch period. Meanwhile, if the maximum segment correlation value is greater than the predetermined threshold, it is determined that the corresponding voice signal is voiced sound. 
     FIG. 3  is a flowchart of operations performed by an embodiment of a pitch detection method according to an aspect of the present invention, and the method includes rearranging voice data  310 , decomposition  320 , detecting a maximum segment correlation value  330 , and pitch period determination  340 . 
   Referring to  FIG. 3 , in the rearranging voice data  310 , voice data being input is formed in units of frames in operation  311 . It is preferable, but not necessary, that one frame be about 40 ms that is twice a minimum pitch period. In operation  313 , the frame number is set to 1 so that the following operations can be performed for the voice data of the first frame. In operation  315 , a center peak in a single frame is determined. For this, voice data in a single frame is multiplied by a predetermined weight window function, and a location where the absolute value of the result of the multiplication is a maximum is determined as a center peak. In operation  317 , voice data in a single frame is shifted on the basis of the center peak so that the voice data is rearranged. Though it is not shown, low pass filtering of voice data being input can be performed before operation  311 . 
   In the decomposition  320 , the rearranged voice data is decomposed into even symmetrical components on the basis of the center peak in operation  310 . As another embodiment, the rearranged voice data can be multiplied by a predetermined weight window function and then decomposed into even symmetrical components on the basis of the center peak in operation  310 . In this case, pitch determination errors such as pitch doubling can be reduced greatly. 
   In the detecting a maximum segment correlation value  330 , local peaks are detected from the even symmetrical components decomposed in operation  320 , in operation  331 . If the value of the center peak is a negative number, the sample locations of local peaks have values less than 0, and if the value of the center peak is a positive number, the sample locations of local peaks have values greater than 0. In operation  333 , the segment correlation value between a reference point, that is, sample location 0, and a sample location corresponding to each of local peaks is calculated. In operation  335 , a maximum segment correlation value is detected among the segment correlation values of all local peaks. 
   In the pitch period determination  340 , in operation  341 , it is determined whether or not the maximum segment correlation value detected in operation  330  is greater than a predetermined threshold, and if the determination result indicates that the maximum segment correlation value is less than or equal to the predetermined threshold, it means that a pitch period is not detected for the corresponding frame, and operation  347  is performed. Meanwhile, if the determination result of operation  341  indicates that the maximum segment correlation value is greater than the predetermined threshold, the location of a local peak corresponding to the maximum segment correlation value, that is, the sample location, is determined as a pitch period in operation  343 . In operation  345 , the pitch period determined in operation  343  is stored as the pitch period for the current frame. In operation  347 , it is determined whether or not voice data input is finished, and if the determination result of operation  347  indicates that voice data input is finished, the method of the flowchart is finished, and if the voice data input is not finished, operation  347  is performed to increase frame number by 1, and then operation  315  is performed so that a pitch period for the next frame is detected. 
   The invention can also be embodied as computer readable codes on a computer readable recording medium. The computer readable recording medium is any data storage device that can store data which can be thereafter read by a computer system. Examples of the computer readable recording medium include read-only memory (ROM), random-access memory (RAM), CD-ROMs, magnetic tapes, floppy disks, and optical data storage devices. The computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion. Also, functional programs, codes, and code segments for accomplishing the present invention can be easily construed by programmers skilled in the art to which the present invention pertains. 
   In order to evaluate the performance of the pitch detection method according to an aspect of the present invention as described above, experiments were carried out under conditions of a 20 kHz sampling rate of voice samples, and 16-bit resolution of analog-to-digital conversion, and the characteristics of voices spoken by 5 male speakers and 5 female speakers are as shown in tables 1 and 2: 
   
     
       
         
             
             
             
             
             
             
           
             
               TABLE 1 
             
             
                 
             
             
                 
                 
               Voiced 
                 
                 
                 
             
             
                 
                 
               sound 
             
             
               Male 
               Entire length 
               interval 
               Average 
               Minimum 
               Maximum 
             
             
               speakers 
               (sec) 
               (sec) 
               pitch (Hz) 
               pitch (Hz) 
               pitch (Hz) 
             
             
                 
             
           
          
             
               M1 
               37.4 
               18.4 
               100 
               57 
               180 
             
             
               M2 
               31.9 
               14.0 
               134 
               53 
               232 
             
             
               M3 
               27.2 
               14.6 
               135 
               58 
               183 
             
             
               M4 
               33.7 
               16.3 
                94 
               57 
               259 
             
             
               M5 
               40.3 
               20.7 
               107 
               59 
               182 
             
             
                 
             
          
         
       
     
   
   
     
       
         
             
             
             
             
             
             
           
             
               TABLE 2 
             
             
                 
             
             
                 
                 
               Voiced 
                 
                 
                 
             
             
                 
                 
               sound 
             
             
               Female 
               Entire length 
               interval 
               Average 
               Minimum 
               Maximum 
             
             
               speakers 
               (sec) 
               (sec) 
               pitch (Hz) 
               pitch (Hz) 
               pitch (Hz) 
             
             
                 
             
           
          
             
               M1 
               32.2 
               15.1 
               195 
               63 
               263 
             
             
               M2 
               33.7 
               19.0 
               228 
               68 
               333 
             
             
               M3 
               30.5 
               15.6 
               192 
               78 
               286 
             
             
               M4 
               31.6 
               17.8 
               233 
               56 
               400 
             
             
               M5 
               38.7 
               18.6 
               229 
               78 
               351 
             
             
                 
             
          
         
       
     
   
   When the cut off frequency of the used low pass filter is 460 Hz, the results of detecting pitch periods by applying the pitch detection method according to an aspect of the present invention, prior art 1 (SegCor) using segment correlation, and prior art 2 (E_SegCor) using improved segment correlation, respectively, to the voice samples shown in tables 1 and 2, are shown in expression of voiced error rate (VER) and global error rate (GER) in table 3. Here, SegCor denotes the method disclosed by the article by Y. Medan, E. Yair, and D. Chazan, and E_SegCor denotes the method disclosed by the article by P. C. Bagshaw, S. M. Hiller and M. A. Jack described above. 
   
     
       
         
             
             
             
             
             
             
             
           
             
                 
               TABLE 3 
             
           
          
             
                 
                 
             
             
                 
               Prior art 1 
                 
               Prior art 2 
                 
               Present 
                 
             
             
                 
               (SegCor) 
                 
               (E_SegCor) 
                 
               invention 
             
          
         
         
             
             
             
             
             
             
             
             
          
             
                 
                 
               VER 
               GER 
               VER 
               GER 
               VER 
               GER 
             
             
                 
                 
             
          
         
         
             
             
             
             
             
             
             
             
          
             
                 
               Male 
               10.91 
               3.97 
               11.18 
               3.15 
               3.22 
               1.97 
             
             
                 
               speaker 
             
             
                 
               Female 
               3.79 
               8.77 
               4.16 
               3.21 
               0.75 
               2.12 
             
             
                 
               speaker 
             
             
                 
               Average 
               7.32 
               6.49 
               7.64 
               3.18 
               1.97 
               2.05 
             
             
                 
                 
             
          
         
       
     
   
   Referring to table 3, when the pitch detection method of the present invention is applied, VER decreased by 73% and 74% and GER decreased by 68% and 36% compared to prior arts 1 and 2, respectively. 
   Next, when the cut off frequency of the used low pass filter is 230 Hz, the results of detecting a pitch by applying the pitch detection method according to the present invention, prior art 1 (SegCor) using segment correlation, and prior art 2 (E_SegCor) using improved segment correlation, respectively, to the voice samples shown in tables 1 and 2, are shown in expression of voiced error rate (VER) and global error rate (GER) in table 4: 
   
     
       
         
             
             
             
             
             
             
             
           
             
                 
               TABLE 4 
             
           
          
             
                 
                 
             
             
                 
               Prior art 1 
                 
               Prior art 2 
                 
               Present 
                 
             
             
                 
               (SegCor) 
                 
               (E_SegCor) 
                 
               invention 
             
          
         
         
             
             
             
             
             
             
             
             
          
             
                 
                 
               VER 
               GER 
               VER 
               GER 
               VER 
               GER 
             
             
                 
                 
             
          
         
         
             
             
             
             
             
             
             
             
          
             
                 
               Male 
               5.46 
               4.84 
               7.20 
               3.22 
               3.22 
               1.97 
             
             
                 
               speaker 
             
             
                 
               Female 
               2.65 
               10.8 
               2.78 
               0.75 
               0.75 
               2.12 
             
             
                 
               speaker 
             
             
                 
               Average 
               4.04 
               7.90 
               4.97 
               2.35 
               1.97 
               2.05 
             
             
                 
                 
             
          
         
       
     
   
   Referring to table 4, when the pitch detection method of the present invention is applied, VER decreased by 51% and 60% and GER decreased by 74% and 13% compared to prior arts 1 and 2, respectively. 
   According to an aspect of the present invention as described above, by using even symmetrical components, pitch detection is performed such that the number of samples analysed in a single frame is reduced and the accuracy of pitch detection is greatly raised. Accordingly, voiced error rate (VER) and global error rate (GER) can be greatly reduced. In addition, by performing segment correlation of a reference point and a local pitch, the number of segments used in segment correlation is reduced compared to the prior art such that complexity of the calculation can be decreased and the time taken for performing the correlation can be reduced. 
   While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention as defined by the following claims.