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  1. 1. Volume 3, Number 4, December 2012 Journal of Convergence Novel Query-by-Humming/Singing Method with Fuzzy Inference System Yo-Ping Huang Shin-Liang Lai Department of Electrical Engineering Department of Computer Science and Engineering National Taipei University of Technology Tatung University Taipei, Taiwan 10608 Taipei, Taiwan 10451 yphuang@ntut.edu.tw sinla.lai@gmail.comAbstract—Music Information Retrieval (MIR) is a crucial topic in means for a MIR inquiry can obtain a desirable effect withoutthe domain of information retrieval. According to the major other apparatus. However, compared with other methods, it ischaracteristics of music, the Query-by-Humming system retrieves more difficult to retrieve related music and it is alwaysinteresting music by finding melodies that contains similar or returned with a lower accuracy, especially when using singingequal melodies to the humming query. Basing on the fuzzy as the way of query the accuracy [6].inference model designed in this paper, a novel Query-by-Humming/Singing system is proposed to extract pitch contour To translate the hummed melody into the contrastinginformation from WAV and MIDI files. To verify the MIDI format, many researchers have described multi Query-effectiveness of the presented work, the MIREX QBSH Database by-Humming systems with melody comparison [12]. MIDIis employed as our experimental database and a large amount of can be regarded as a music format expressed in words orhuman vocal data is used as queries to test the robustness of the numbers. Therefore, most researchers change the melody intoMIR. Then, the Longest Common Subsequence (LCS) is used as a series of symbolic representations to be compared with thean approximate matching algorithm to identify the top 5 music MIDI. When a query has been changed into symbolicsamples as an evaluation standard for the system. Experimental representation, it can be proceeded to contrast with a melodyresults show that the proposed system achieves 85% accuracy in that has been previously coped with. Several methods havethe top 5 retrievals. been proposed for comparison, such as granular events [13], Keywords-MIDI; query-by-humming; pitch contour; fuzzy hidden Markov models [14] [15], string matching [16] [17],inference system. dynamic programming [18] [19], dynamic time warping (DTW) [20] [21], and tree-based searching [22] [23]. At the I. INTRODUCTION end of the contrasting procedures, MIR can then select the item that matches the guiding rules. With its extensive resources, Internet services prevail over The remainder of this paper is organised as follows.whole areas, including the music market. The Internet is Section 2 starts with some basic definitions and notations usedsomething like a huge database that satisfies all kinds of needs throughout the paper, and provides a brief look at the previousof its client at all times. For instance, many researchers study work on this subject. Section 3 presents the details of ourMusic Information Retrieval (MIR) for the purpose of finding approach. In Section 4, a novel MIR is presented and testquickly and correctly the programs a user needs from a vast results are discussed. Finally, Section 5 concludes the papermusic database. To achieve the goal, researchers have probed and gives a perspective on further study on this subject.into all types of music format, including MIDI, MP3, Wave,and Voice. In recent years, more and more researchers rely on II. RELATED WORKMIDI (Musical Instrument Data Interface) as their research Ghias et al. [25] built a MIR to process MIDI files in 1995.focus due to the fact that MIDI has superiority over other They used three symbols (U, D and S) to depict the threemusic formats. For example, MIDI can be played with different levels of pitch contour. U represents up, D is downelectronic synthesizers and the pitch or length can be changed and S is the same. In any time series graph those are the onlyaccording to a user’s needs. With a smaller size of file forms, three possibilities for a graph to grow, shrink or remain theMIDI is easier to record and thus is widely applied in karaoke. same.With this advantage, MIDI has its characteristics in saving Many researchers ameliorate afterwards with this, such asstorage space, speeding up enquiries, and raising the accuracy Typke et al. [26] who researched the Parsons Code. They alsoof enquiry results. use the pitch contour from humming, and after acquiring the However, the introduction of MIR is accompanied with pitch contour represent them as U, D, and R strings. McNab etquestions on query methods when considering how to extract al. [27] incorporated rhythm with the idea of pitch contours.and represent features of a query melody. Previous studies Sonoda et al. [28], Li et al. [7], and Mo et al. [29] consideredhave developed many query models, including Query-by- the characteristic of duration, and used L (longer), R (Repeat),Humming (Singing) [1]–[7], Query-by-Tapping [8], Query- and S (Same) to represent the changes.by-Example [9], Query-by-Tag [10], and Query-by- Tom et al. [30] used a dynamically-calculated threshold,Description [11]. Many researchers are interested in Query-by- applied it to get the contour of the signal to segment notes, andHumming (QBH), because humming is the simplest and most used autocorrelation to detect pitches. Then notes aredirect way for people to express music. Using QBH as the Copyright ⓒ 2010 Future Technology Research Association International 1
  2. 2. Journal of Convergence Volume 3, Number 4, December 2012represented as a string sequence of U, D, and R. Raju et al. audio input in real-time, by tracking note dynamics and pitch[31] proposed a similar approach to represent the melody. The bends, and using different harmonic models to improve themelodies in the database are indexed by the U, D, and S recognition of appropriate instruments. In the meantime, v3.0strings. They used a time-domain autocorrelation function for has increased a lot in terms of identification rate. Hence wepitch extraction and gave a dynamic programming-based edit also use the Akoff Music Composer as a tool for transformingdistance algorithm as a similarity metric. But these techniques WAV to MIDI as shown in Fig. 2.are time-consuming. Therefore, in this paper we propose different pitchcontour coding methods that use a large amount of human WAV File MIDIvocal data as the query and combine Fuzzy Inference System Database(FIS) to search the MIDI database for testing the accuracy ofMIR. III. THE PROPOSED APPROACH Translate Melody to Since the user’s query is an important factor that will MIDI Pitch Contour Extractioninfluence the accuracy of the results, massive and diverse Databasequery data are experimented with to test the accuracy of thesystem. To solve the above problems, the MIREX QBSH Fuzzy FuzzyDatabase is employed as our experimental database, which is Inference Inferenceunique in that it contains a large amount of query data that System LCS Systemconsists of different individuals and sexes. In general, there Stringare several steps to establish a Query-by-Humming system: Matching Pitch Pitch 1. Building a music database: whatever the music format, Contour Contour the first step is to collect massive music programs in the database, proceed to pre-processing or format transference after comparison, and save the processed Ranked List results into the database. 2. Inputting query: based on the above discussions, the Figure 1. System framework. query may have multi modes, for example picking up the features of the query after the user inputs the query and then transforming them into pre-defined formats for comparison. 3. Comparing procedures: utilise the algorithm to compare the query and melody in the database. Most systems transformed both into symbolic representation for a handy contrast, but it takes more time as well as occupies more database storage space. 4. Returning results: based on the results of the comparison, the Query-by-Humming system can return results that are sorted by similarities. Usually it will return the top five or top ten results to the user to differentiate whether the results fit the request. This is an important index to determine the accuracy of the Query-by-Humming Figure 2. Example of transforming WAV to MIDI. system. According to the above discussions, the system B. Pitch Contourframework is shown in Fig. 1 where the blue dotted rectanglecontains the pre-process steps and the red rectangle indicates After translating WAV to MIDI, we then analyse the pitchthe query processing steps. The comparing section that interval in each note. Different from [25], which divided theemploys the Longest Common Subsequence (LCS) will be pitch interval into U, D, and S, two neighbouring pitchexpatiated in the following. intervals are divided into six symbols as given in Table 1.A. WAV to MIDI Table 1: Pitch Contour Symbol Zhu and Shasha [32] mentioned that rather than employingsome unreliable note-segmentation algorithm, they would Symbol Pitch Intervalrather use the best commercial software – Akoff Music H >8Composer v2.0 – to record and transcribe notes from a user’s R 5~8query. The Akoff Music Composer software by Akoff SoundLabs is designed for the recognition of polyphonic music from U 1~4audio sources and its conversion to a MIDI score. Recognition D -1 ~ -4is performed from pre-recorded WAV files or directly from2 Copyright ⓒ 2010 Future Technology Research Association International
  3. 3. Volume 3, Number 4, December 2012 Journal of Convergence B -5 ~ -8 L < -8 If a user’s query includes several consecutive same pitchesit may produce segment note errors during the processing fromWAV to MIDI, which is particularly obvious in melodies withfaster tempos. To exclude this problem, we do not record themelody when the pitch interval is zero, but record the variationin pitch interval only to later reduce the timing of comparingword strings. An example is listed in Fig. 3. Figure 4. Input membership functions for query. Figure 3. Pitch contour example.C. Fuzzy Inference System In general, fuzzy inference systems are based on fourmajor modules for operation. 1. Fuzzification process: transforms the system inputs, which are crisp numbers, into fuzzy sets. This is done by applying the membership functions to calculate the membership degrees. 2. Knowledge base: stores fuzzy operations, fuzzy rule bases, etc. Figure 5. Output membership functions for symbol. 3. Conflict resolution process: more than one rule may be fired by an input datum, and this module resolves conflicts by predefined operations. Table 2: Parametric Values of Membership Functions 4. Defuzzification process: uses the predefined defuzzification method, such as the centre of gravity, to Pitch Interval Parametric Value transform the fuzzy regions obtained by the inference large up 9.5 procedure into a crisp output value. medium up 6.5 In this paper, we use FIS to transform pitch intervals in little up 2.5the query into symbolic representation. After a WAV file istransformed to a MIDI, none of any software or algorithm can little down -2.5be 100% accurate in retrieval. Hence, we utilise the medium down -6.5characteristics of fuzzy sets to blur the pitch contour so as tooffset the errors made while transforming it, to favour the large down -9.5underlying contrasting procedures. In the fuzzy membership function settings, frequently used According to Table 2, six membership functions arefunctions are triangular, Gaussian, or trapezoidal membership defined for the input query variable. Those six terms arefunctions. Among them, curves of Gaussian membership labelled by mf1 to mf6 and correspond to the outputfunctions are smoother and therefore have better nonlinear membership functions L, B, D, U, R, and H, respectively. Wetraits. The general formulae are shown below: define six fuzzy rules accordingly, and the users’ queries can   x  m 2  be transformed into pitch contours by these rules. Finally, the  A ( x )  exp      (1) pitch contours of the users’ queries and repeating patterns are        compared in the matching process. Fig. 6 illustrates how the Here, m is the mean and is the centre point of the Gaussian proposed fuzzy system infers the output from themembership function. σ is the standard deviation which corresponding input.corresponds to the width of the Gaussian membership function.This study chose Gaussian membership functions as the test Rule 1: IF pitch interval is large down,membership functions to process pitch contour transform for THEN pitch contour symbol is L.the underlying comparison procedures. Fig. 4 and Fig. 5 show Rule 2: IF pitch interval is medium down,the membership functions for query and symbol, respectively, THEN pitch contour symbol is B.and the parametric values of membership functions are givenin Table 2. Rule 3: IF pitch interval is little down, Copyright ⓒ 2010 Future Technology Research Association International 3
  4. 4. Journal of Convergence Volume 3, Number 4, December 2012 THEN pitch contour symbol is D. for i = 1 to m Rule 4: IF pitch interval is little up, for j = 1 to n if X[i] = Y[j] THEN pitch contour symbol is U. C[i][j] = C[i-1][j-1] + 1 Rule 5: IF pitch interval is medium up, else THEN pitch contour symbol is R. C[i][j] = max(C[i][j-1], C[i-1][j]) Rule 6: IF pitch interval is large up, Return C[m][n] THEN pitch contour symbol is H. For example, we can compare two strings “U, R, R, D, R, D, R, B” and “U, D, U, R, B”, and the length of the LCS is 4 as shown in Fig. 7. Then, we can use this algorithm to calculate the length of LCS. Last, we can compare the query and the melody in the database to the length of LCS as a basis for sorting and selecting the top ten from the system as candidate sets. U U R R R R D D R R D D R R B B U U D D R R B B U U D D U U R R B B Figure 7. LCS example. Figure 6. Illustration of defuzzification process. IV. EXPERIMENTAL RESULTS AND DISCUSSIONS The MIREX QBSH Database is used as our experimentalD. Longest Common Subsequence database, which includes 48 MIDI and 718 WAV files. There When we finished the query translation and MIDI database are 35 subjects’ recorded vocal fragments in WAV files,representation, the next task is to match the symbolic strings including one subject who has recorded 20 records inbetween the query string and the strings in the database. The humming, and the rest of the subjects in singing. Each sectionLongest Common Subsequence (LCS) algorithm is a widely- of WAV file lasts 7 seconds, and includes male and femaleused and famous dynamic programming algorithm to voices to test the accuracy of the query system. In addition, weimplement approximate matching. Like other dynamic employ the remaining 48 MIDI files as the music database inprogramming methods, LCS also resolved the problems by a which the programs include Chinese and western folk songs.recurrence relation. For example, X(1, 2, ..., m) and Y(1, 2, ..., The original WAV files translated to MIDI via the Akoffn) are string sequences of length m and n, respectively. Xi Music Composer can use FIS to translate them into pitchrepresents a subsequence of X(1, 2, ..., i) or the prefix of the contours. After the original query transforms from the FIS ofsequence X of length i and xi represents the ith element of the this system to pitch contours, its length distribution scope fallssubsequence. The LCS of X and Y is described in the between 15 and 24. The number of all length query statistics isfollowing formula: shown in Fig. 8, where the horizontal axis is the length of the pitch contour and the vertical axis is its accumulated number.  if i  0 or j  0 LCS ( X i , Y j )  ( LCS ( X i 1 , Y j 1 ), xi ) if xi  y j longest( LCS ( X , Y ), LCS ( X , Y )) if x  y (2)  i j 1 i 1 j i j Then, we can use the following pseudo codes to calculatethe length of LCS. Lastly we can contrast the query and themelody in the database to the length of the LCS as a basis forsorting and selecting the top five from the system as candidatesets. Function LCSLen(X[1, …, m], Y[1, …, n]) Initialize C[m][n] to zeroes for i = 0 to m C[i][0] = 0 for j = 0 to n Figure 8. Length statistics of pitch contour. C[0][j] = 04 Copyright ⓒ 2010 Future Technology Research Association International
  5. 5. Volume 3, Number 4, December 2012 Journal of Convergence The FIS is implemented by MATLAB. There are 6membership functions as previously mentioned for either inputor output variables. The system output is divided into 6 groups,as Top 1, Top 2, Top 3, Top 4, Top 5, and Top 5+ based onthe LCS computing results to calculate their recall rates forevaluation. To compare the effectiveness of the FIS in the proposedsystem to other methods, we contrast the results with andwithout using the FIS. The experimental results from the topfive and beyond retrieval are summarised in Table 3, and thehistograms for each retrieval level are compared in Fig. 9. Figure 10. Histograms of accumulated retrieval results. V. CONCLUSION AND FUTURE WORK Unlike research in the past, in this paper we employ a massive singing query as the experimental data via diverse vocal records to test the robustness of the proposed system. In the pitch interval coding, we change the past U, S, and D coding method to L, B, D, U, R, and H to improve the accuracy of the coding. Besides, we also abandon the same pitch coding to reduce situations that occur due to consecutive Figure 9. Retrieval results from the top five and beyond levels. same pitch note segmentation errors. In the processing of coding, a fuzzy inference model is used as a coding tool to reduce the errors produced when WAV files were translated to We can see that although 11 records in the FIS are not MIDI files via their characteristics of blurring. LCS is alsosuccessfully retrieved compared to the normal method in the applied as an approximate matching algorithm to locate theTop 1 level in Table 3, the accumulated figure reached nearly Top 5 retrieval results in the system as a standard fortwice the retrieval in the Top 2 level. Apparently the MIR evaluating the system’s performance.effect can indeed be improved after applying FIS to the system. During the experiment, we compare the differences withMoreover, we can see the accumulated results from each and without the addition of FIS. From the results, we can seeretrieval level in Fig. 10, where the accumulated results from that the amount increases radically in the Top 2 samples afterboth the Top 1 and Top 2 levels in FIS find 332 records. This FIS is added, which means that FIS has indeed fulfilled itsfigure indicates that almost half the query can be successfully function to raise the system’s accuracy. It reaches 65% in theretrieved. Table 4 shows the percentage accumulated is 65% Top 3 retrievals, which indicates that the proposed system hasfrom the Top 1 to Top 3 levels, and this rises to 85% if the a great effect on Query-by-Humming/Singing.Top 1 to Top 5 levels are considered. Consequently, this result We are not satisfied with the current performance. Moreverifies that using FIS can indeed raise the system accuracy efforts are needed in the future to increase the size of theand fit the needs of the user query. melody database to test the system’s scalability and to improve the WAV (voice) to MIDI (string) translation process Table 3: Total Number of Queries in Each Group as well as the incorporation of new features for the basis of analysis to improve the system’s accuracy. Top 1 Top 2 Top 3 Top 4 Top 5 Top 5+ ACKNOWLEDGMENTS This work was supported in part by the National Science Normal 117 137 158 90 86 130 Council, Taiwan under Grant NSC100-2221-E-027-110, and in part by the joint project between the National Taipei FIS 106 226 137 72 73 104 University of Technology and Mackay Memorial Hospital under Grant NTUT-MMH-99-03 and Grant NTUT-MMH- Table 4: Retrieval Percentage of Each Group 100-09. REFERENCES Top 1 Top 2 Top 3 Top 4 Top 5 Top 5+ [1] N. Ben Salem, and J. P. Hubaux, “Securing wireless mesh networks,” IEEE Comm. Mag., vol. 13, no. 2, pp. 50–55, April Normal 16% 19% 22% 13% 12% 18% 2006. [2] N. H. Adams, M. A. Bartsch, and G. H. Wakefield, “Note FIS 15% 31% 19% 10% 10% 15% segmentation and quantization for music information retrieval,” Copyright ⓒ 2010 Future Technology Research Association International 5
  6. 6. Journal of Convergence Volume 3, Number 4, December 2012 IEEE Trans. on Audio, Speech, and Language Processing, vol. [19] J.-S. R. Jang, and H.-U. Lee, “A general framework of 14, no. 1, pp. 131–141, January 2006. progressive filtering and its application to query by singing/humming,” IEEE Trans. on Audio, Speech, and[3] B. D. Roger, P. B. William, P. Bryan, H. Ning, M. Colin, and T. Language Processing, vol. 16, no. 2, pp. 350–358, February 2008. George, “A comparative evaluation of search techniques for query-by-humming using the MUSART testbed,” J. Am. Soc. [20] T. Nishimura, H. Hashiguchi, J. Takita, J. X. Zhang, M. Goto, Info. Sci. Technol., vol. 58, no. 5, pp. 687–701, March 2007. and R. Oka, “Music signal spotting retrieval by a humming query using start frame feature dependent continuous dynamic[4] E. Unal, E. Chew, P. G. Georgiou, and S. S. Narayanan, programming,” Proc. Int. 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Kim, “MUSEMBLE: A novel Engineering, vol. 5, Los Angeles, California, USA, pp. 266–270, music retrieval system with automatic voice query transcription March-April 2009. and reformulation,” J. Syst. Softw., vol. 81, no. 7, pp. 1065–1080, July 2008. [23] U. Bagci, and E. Erzin, “Automatic classification of musical genres using inter-genre similarity,” IEEE Signal Process. Lett.,[7] P. Li, M. Zhou, X. Wang, X. Wang, N. Li, and L. Xie, “A novel vol. 14, no. 8, pp. 521–524, August 2007. MIR framework and application with automatic voice processing, database construction and fuzzy matching,” Proc. 2nd Int. Conf. [24] J. Shen, D. Tao, and X. Li, “QUC-Tree: Integrating query Computer and Automation Engineering, Singapore, vol. 1, pp. context information for efficient music retrieval,” IEEE Trans. 20–24, February 2010. on Multimedia, vol. 11, no. 2, pp. 313–323, February 2009.[8] K. Kichul, R. P. Kang, S.-J. Park, S.-P. Lee, and Y. K. Moo, [25] A. Ghias, J. Logan, D. Chamberlain, and B. Smith, “Query by “Robust query-by-singing/humming system against background humming – Musical information retrieval in an audio database,” noise environments,” IEEE Trans. on Consumer Electronics, vol. Proc. ACM Multimedia, San Francisco, California, USA, pp. 57, no. 2, pp. 720–725, May 2011. 231–236, November 1995.[9] H. Pierre, and R. Matthias, “Query by tapping system based on [26] R. Typke, and L. Prechelt, “An interface for melody input,” alignment algorithm,” Proc. IEEE Int. Conf. Acoustics, Speech, ACM Trans. on Computer-Human Interaction, vol. 8, no. 2, pp. and Signal Processing, Taipei, Taiwan, pp. 1881–1884, April 133–149, June 2001. 2009. [27] R. J. McNab, L. A. Smith, I. H. Witten, and C. L. Henderson,[10] F. Pereira, A. Vetro, and T. Sikora, “Multimedia retrieval and “Tune retrieval in the multimedia library,” Multimedia Tools delivery: Essential metadata challenges and standards,” Proc. Appl., vol. 10, pp. 113–132, April 2000. 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Christian, K. Jimmy, W. N. Lau,[13] N. Hu, and R. B. Dannenberg, “A comparison of melodic and R. Thomas, “A system for recognition of hummed tunes,” database retrieval techniques using sung queries,” Proc. 2nd Proc. COST G-6 Conf. on Digital Audio Effects, Limerick, ACM/IEEE-CS Joint Conf. Digital Libraries, Portland, Oregon, Ireland, pp. 203–206, December 2001. USA, pp. 301–307, July 2002. [31] M. A. Raju, B. Sundaram, and P. Rao, “TANSEN: A query-by-[14] J. T. Yao, and Y. Y. Yao, “Information granulation for web humming based music retrieval system,” Proc. of National Conf. based information retrieval support systems,” Proc. Int. Society on Communications, Madras, India, pp. 75–79, January-February for Optical Engineering, Orlando, Florida, USA, pp. 138–146, 2003. April 2003. [32] Y. Zhu and D. Shasha, “Query by humming: A time series[15] J.-S. R. Jang, C.-L. Hsu, and H.-R. Lee, “Continuous HMM and database approach,” Proc. ACM Special Interest Group on its enhancement for singing/humming query retrieval,” Proc. Int. Management of Data, San Diego, California, USA, June 2003. Symp. Music Information Retrieval, London, UK, pp. 546–551, September 2005. BIOGRAPHIES[16] H. Takeda, N. Saito, T. Otsuki, M. Nakai, H. Shimodaira, and S. Sagayama, “Hidden Markov model for automatic transcription of Yo-Ping Huang received his PhD MIDI signals,” Proc. IEEE Workshop on Multimedia Signal Processing, St. Thomas, Virgin Islands, USA, pp. 428–431, in Electrical Engineering from December 2002. Texas Tech University, Lubbock,[17] K. Lemström, String Matching Techniques for Music Retrieval, TX, USA. He is currently a PhD Thesis, Department of Computer Science, Faculty of Professor in the Department of Science, University of Helsinki, 2000. Electrical Engineering at National[18] C. Parker, “Towards intelligent string matching in query-by- Taipei University of Technology humming systems,” Proc. IEEE Int. Conf. Multimedia and Expo, (NTUT), Taiwan. He also serves vol. 2, Baltimore, Maryland, USA, pp. 25–28, July 2003. as CEO of the Joint Commission of Technological and Vocational6 Copyright ⓒ 2010 Future Technology Research Association International
  7. 7. Volume 3, Number 4, December 2012 Journal of ConvergenceCollege Admission Committee in Taiwan. He was SecretaryGeneral at NTUT, Chairman of IEEE CIS Taipei Chapter, andVice Chairman of IEEE SMC Taipei Chapter. He wasProfessor and Dean of the College of Electrical Engineeringand Computer Science, Tatung University, Taipei, beforejoining NTUT. His research interests include medicalknowledge mining, intelligent control systems, and handhelddevice application systems design. Prof. Huang is a seniormember of the IEEE and a fellow of the IET. Shin-Liang Lai received BS and MS degrees in Mathematics from the National Taipei University of Education, Taipei, Taiwan, in 1998 and 2002, respectively. In 2012, he received his PhD in Computer Science from the Department of Computer Science and Engineering, Tatung University, Taipei, Taiwan. His research interests include content-based music informationretrieval, data mining, and fuzzy inference systems. Copyright ⓒ 2010 Future Technology Research Association International 7
  8. 8. Journal of Convergence Volume 3, Number 4, December 2012 This page is intentionally left blank8 Copyright ⓒ 2010 Future Technology Research Association International