Source: http://www.google.com/patents/US20020172372?dq=5319712
Timestamp: 2014-12-18 04:31:17
Document Index: 156801306

Matched Legal Cases: ['art 32', 'art 36', 'art 36', 'art 36', 'art 36', 'art 36', 'art 39', 'art 39', 'art 40', 'art 39', 'art 40', 'art 81', 'art 82', 'art 81', 'art 82']

Patent US20020172372 - Sound features extracting apparatus, sound data registering apparatus, sound ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsThe present invention implements a method and an apparatus for retrieving a sound data desired by the user on the basis of its subjective impression over the sound data. The subjective impression on the desired sound data is entered by the user and converted to a numerical value. A target sound impression...http://www.google.com/patents/US20020172372?utm_source=gb-gplus-sharePatent US20020172372 - Sound features extracting apparatus, sound data registering apparatus, sound data retrieving apparatus, and methods and programs for implementing the sameAdvanced Patent SearchPublication numberUS20020172372 A1Publication typeApplicationApplication numberUS 10/101,569Publication dateNov 21, 2002Filing dateMar 20, 2002Priority dateMar 22, 2001Also published asDE60237860D1, EP1244093A2, EP1244093A3, EP1244093B1, US7373209Publication number10101569, 101569, US 2002/0172372 A1, US 2002/172372 A1, US 20020172372 A1, US 20020172372A1, US 2002172372 A1, US 2002172372A1, US-A1-20020172372, US-A1-2002172372, US2002/0172372A1, US2002/172372A1, US20020172372 A1, US20020172372A1, US2002172372 A1, US2002172372A1InventorsJunichi Tagawa, Hiroaki Yamane, Masayuki MisakiOriginal AssigneeJunichi Tagawa, Hiroaki Yamane, Masayuki MisakiExport CitationBiBTeX, EndNote, RefManReferenced by (18), Classifications (19), Legal Events (4) External Links: USPTO, USPTO Assignment, EspacenetSound features extracting apparatus, sound data registering apparatus, sound data retrieving apparatus, and methods and programs for implementing the sameUS 20020172372 A1Abstract The present invention implements a method and an apparatus for retrieving a sound data desired by the user on the basis of its subjective impression over the sound data. The subjective impression on the desired sound data is entered by the user and converted to a numerical value. A target sound impression value which is a numerical form of the impression on the sound data is calculated from the numerical value. The target sound impression value is then used as a retrieving key for accessing a sound database where the audio signal and the sound features of a plurality of the sound data are stored. This allows the desired sound data to be retrieved on the basis of the subjective impression of the user on the sound data. Images(14) Claims(28)
[0048] In Step S3, the variations ΔS(t) of all the frames are averaged to determine a spectrum variation rate SFLX. Spectral Fluctuation Rate SFLX is expressed by SFLX = ∑ f = 1 Nall  Δ   S  ( t ) N all (Equation��2) [0049] (2) Attack Point Ratio (AR) [0050] Using a power p(t,f) of each band in the power spectrum S(t) determined at Step S1, a rise rate d(t,f) of a signal component of each band is calculated at step S4 (RC Det). Also, d(t,f) is added in the direction of frequency at the frame time t to determine a rise component D(t). Those measurements d(t,f) and D(t) can be calculated using Equations 3 to 7 with the power p(t,f) at each frequency band f. [0051] (Equation 3) p(t,f)>pp np>pp [0052] (Equation 4) pp=max(p(t−1,f),p(t−1,f�1),p(t−2,f)) [0053] (Equation 5) np=min(p(t−1,f),p(t−1,f�1)) [0054] (Equation 6) d(t,f)=p(t+1,f)−pp if P(t+1,f)>p(t,f) =p(t,f)−pp otherwise D  ( t ) = ∑ f  d  ( t , f ) (Equation��7) [0055] The extraction of the rise rate d(t,f) and the rise component D(t) is explicitly explained in a reference, such as �Beat tracking system for music audio signals� by Gotoh and Muraoka, the Information Processing Society of Japan, Proceeding Vol.94, No.71, pp. 49-56, 1994. In Step S5 (RF Det), the frequency of appearance of the rise rate d(t,f) throughout all the frames is calculated using Equation 8 to determine an Attack Point Ratio AR. AR = mean  ( ∑ f  boolean  ( d  ( t , f ) ) ) (Equation��8) [0056] (3) Attack Noissiness (NZ) [0057] In Step S6 (AF Calc), the auto-correlation function A(m) (m being a delayed frame number) of D(t) is calculated using Equation 9 to determine the periodicity of the rise component. In Step S7, A(m) is Fourier transformed to a power at each band for determining a power spectrum Aspec(K) of A(m) (K being a frequency). In Step S8 (DCC Det), a direct-current component Aspec(0) of Aspec(K) is detected. In Step S9 (Peak Det), the peak Aspec(Kpeak) of Aspec(K) is extracted. In Step S10 (Ratio Calc), the ratio between Aspec(0) and Aspec(Kspec) is calculated to determine an Attack Noissiness NZ using Equation 10. A  ( m ) = ∑ n = 0 N - 1  D  ( t )  D  ( t + m ) (Equation��9) [0058] (Equation 10) NZ=Aspec(0)/Aspec(Kpeak) [0059] (4) Tempo Interval Time (TT) [0060] The Tempo interval Time TT is an inverse of tempo representing the distance between beats or the length of each quarter note of the sound data. The Tempo interval Time TT is detected from the auto-correlation function A(m) of the rise component D(t). In Step S11 (Peak Det), the peak of A(m) or the time length pk(i) where the cycle of rise component is most exhibited is calculated. In Step S12 (BCC Calc), some candidates T1 and T2 of the tempo interval time is calculated from pk(i). In Step S13 (CS Calc), the cycle structure of the sound data is determined. In Step 14 (BC Dec), one of T1 and T2 is selected through referring the Attack Point Ratio AR and the cycle structure and released as the tempo interval time of the sound data. [0061] An example of calculating the tempo interval time is depicted in �An approach to tempo detection from music signals� by Tagawa and Misaki, Japanese Institute of Acoustic Technology Proceeding, pp. 529-530, 2000. [0062] (5) Beat Ratio (BR) [0063] The Beat Ratio is calculated from the relation between the tempo interval time and superior the sound cycle. In Step S15 (Ratio Calc), the time cycle Tkpeak correspond to Aspec(Kpeak) is calculated and then the Beat Ratio BR between the Tempo interval Time TT and the time cycle Tkpeak is determined using Equation 11. [0064] (Equation 11) BR=TT/Tkpeak [0065] (6) Beat Intensity 1 (BI1) [0066] The power of a rise component which appears at intervals of substantially a half the tempo interval time is calculated. In Step S16 (F1 Calc), the frequency f1 equivalent to a half the tempo interval time is calculated from the Tempo interval Time TT. In Step S17 (Value Ref), the peak of Aspec(K) which exhibits maximum adjacent to f1 is referred and assigned as BI1. [0067] (7) Beat Intensity 2 (BI2) [0068] Similarly, the power of a rise component which appears at intervals of substantially {fraction (1/4)} the tempo interval time is calculated. In Step S18 (F2 Calc), the frequency f2 equivalent to half the tempo interval time is calculated from the Tempo interval Time TT. In Step S19 (Value Ref), the peak of Aspec(K) which exhibits maximum adjacent to f2 is referred and assigned as BI2. [0069] (8) Beat Intensity Ratio (IR) [0070] In Step 20 (Ratio Calc), the ratio IR between the beat intensity BI1 and the beat intensity BI2 is calculated using Equation 12. [0071] (Equation 12) IR=BI1/BI2 [0072] The above described sound features are numerical forms of the acoustic features of the sound data which are closely related to the subjective impression perceived by an audience listening to music of the sound data. For example, the tempo interval time is a numerical indication representing the tempo or speed of the sound data. Generally speaking, fast sounds give �busy� feeling while slow sounds give �relaxing�. This sense of feeling can be perceived without consciousness in our daily life. Accordingly, the prescribed features are assigned as the numerical data representing the subjective impressions. [0073] The sound features determined by the SF extractor 33 in FIG. 3 and listed in FIG. 5 are then received by the SIV calculator 34. The SIV calculator 34 converts the features into their impression values using Equation 13. In other words, the features are converted by the SIV calculator 34 into corresponding numerical data which represent the subjective impressions. Ii = ∑ j = 1 N   p  Wij � Pj (Equation��13) [0074] where Ii is the sound impression values based on an impression factor i, Pj is the value a sound features j, Wij is the weighted coefficient representing the relation between the sound features j and the impression factor i, and Np is the number of sound features. This embodiment permits Np=8 as shown in FIG. 5 while Pj depends on the individual sound features. The sound impression values Ii is a numerical form of the subjective impression perceived from the sound which can represent a degree (Ej) of the impression expressed by a particular adjective. For example, when the impression is classified into five different degrees: �hard (E1)�, �groovy (E2)�, �fresh (E3)�, �simple (E4)�, and �soft (E5)�, the sound impression values Ii can be calculated from Ej using Equation 14. Ii = ∑ j = 1 Ni  Yij � Ej (Equation��14) [0075] where Yij is the weighted coefficient representing the relation between Ej and Ii. [0076] The weighted coefficient Yij and the impression factor Ni are preliminarily prepared from Ej measurements determined from some music samples in a series of sensual evaluation tests using a semantic differential (SD) technique. The results of the tests are subjected to factor analysis such as main component analyzing to determine the weighted coefficient Yij and impression factor Ni. The weighted coefficient Wij is calculated by determining Yij from the sensual evaluation and the factor analysis, calculating the impression value Ii of each sample using Equation 14, and examining the relation between the impression value Ii and the sound features Pj by e.g. linear multiple regression analysis. Alternatively, the sound features Pj and the sound impression values Ii may be determined with the use of a non-linear system such as a neutral network. [0077] The sound database 31 shown in FIG. 3 is a multiplicity of records including the sound signal and its attributed data of each music piece. An example of the record stored in the sound database 31 according to this embodiment is illustrated in FIG. 6. The record comprises: [0078] (1) ID data for identifying the record at once; [0079] (2) sound information about a music piece including a title, a singer, and an artist entered from the sound information register 35; [0080] (3) sound features extracted by the SF extractor 33; [0081] (4) sound impression values determined from the sound features by the feature/impression converter; and [0082] (5) sound signal of the music piece received by the sound input part 32. [0083] The action of the SD retrieving apparatus 43 in relation to the function of the SF extractor 33 will now be described. First, the queries for retrieving a music piece desired by the user are entered from the SEQ input part 36. An example of the queries to be entered is shown in FIG. 7. The queries include sets of characters indicating a title and an artist, numerical values representing the �hardness� impression (for example, normalized within a limited range from +1.0 to −1.0), and other requirements such as �want to dance cheerfully�. The queries are entered by the user operating a keyboard, an array of switches, sliders, and volume knobs, or other appropriate controls. [0084] The TSIV calculator 37 then calculates the sound impression values PIi (a target sound impression values) predicted for the target sound data from the subjective impression factors (subjective factors) in the queries entered from the SEQ input part 36. The target sound impression values PIi can be calculated from the weighted coefficient Yij using Equation 15. PI i = ∑ j = 1 Ni  Yij � IEj (Equation��15) [0085] where IEj is the numerical value of subjective impression such as a degree of �hard� impression. The value IEj may be selected from a number of the impression factors of each music piece determined during the calculation of the weighted coefficient Yij. [0086] The other requirement based on two or more of the subjective impressions, such as �want to dance cheerfully�, is preset with a corresponding IEj value. When the requirement is desired, its preset value is used for calculating the target sound impression values PIi from Equation 15. For example, when the subjective impression is graded between the maximum of 1.0 and the minimum of −1.0, the requirement �want to dance cheerfully� may be translated into �highly groovy and highly fresh�. Accordingly, the preset values are IE1=0.5 for �hardness�, IE2=1.0 for �groovy�, IE3=1.0 for �freshness�, IE4=0.0 for �simplicity�, and IE5=0.0 for �softness�. The target impression value PIi is then calculated from these numerals of IEj. [0087] The SIV retriever 38 accesses and reads out a record corresponding to the keys of the sound information and the target sound impression values PIi from the sound database 31. The sound information is examined for matching with the sound information stored as parts of the records in the sound database 31. More specifically, the similar record can be extracted through examining inputted the characters in the sound information. The similarity between the target sound impression values PIi impression values of each record stored in the sound database 31 is evaluated and retrieved. FIG. 8 illustrates a space diagram where the sound impression values are plotted for examining the similarity. [0088] The sound impression values Ii of each music piece in the record is expressed as a vector in the space consisting of an Ni of the impression factor. This space is called an impression space. The impression space shown in FIG. 8 is based on the impression factor Ni=2 where the impression value Ii is a two-dimensional point 44. Similarly, the target sound impression values PIi can also be expressed in the impression space and, for example, a point 45 represents specified subjective impression. The similarity between the target sound impression values PIi and the sound impression values Ii is hence defined by the Euclidean distance of in the impression space which is denoted by L and calculated from the following equation 16. L = ∑ i = 1 Ni  ( PIi - Ii ) 2 (Equation��16) [0089] The distance L is calculated throughout a set of the music pieces to be retrieved. The smaller the distance L, the more the similarity to the target sound impression values is recognized. The music piece having the minimum of the distance L is regarded as the first of the candidates. Candidates of the predetermined number are released as the results. As shown in FIG. 8, the similarity may be defined as a circular area about the sound impression values so that all the candidates in the area are released as the resulting outputs. In the latter case, it is possible that the similarity is limited to a predetermined level and any music piece smaller than the level will be discarded. [0090] The retrieving action with the sound information and the retrieving action with the subjection impression may be carried out separately or in a combination. This may be select by the user through a operation of the SEQ input part 36. [0091] Alternatively, the candidates are selected using the sound information inputted through the SEQ input part 36 and then their sound impression values are utilized as target sound impression values for retrieving another music piece. According to such operations, the user may retrieve other sound data similar in the subjective respects to the target sound data. For example, as a title �B1� is entered by the user, it is then used as the retrieving key for accessing the sound database 31. Once the title �B1� is received from the sound database 31, its impression value is used as the target sound impression value for accessing again the sound database 31. Accordingly, more sound data similar to the first received sound data can be retrieved on the basis of the subjective impression of the retrieved sound data. In the example, the title �B2� which has similar impression to that of the title �B1� can be obtained. [0092] The SEQ input part 36 may also be equipped with an SD input part, an SF extractor, and an SIV calculator identical to those in the SD registering apparatus 42. Accordingly, since the sound features are calculated from a received sound signal and used as the sound impression values for accessing the sound database 31, more sound data similar to the sound data of the received sound signal can favorably be obtained. [0093] A group of the candidates determined by the SIV retriever 38 are further classified by the sound selection part 39. The sound selection part 39 submits the attributed data (a title, an artist, etc.) about the candidates to the user and demands for selection of the sound data to be reproduced. The selection may be conducted through listening to all or parts of each candidate on the sound output part 40. [0094] When the retrieving action is based on the subjective requirement, the similarity between the subjective impression determined by the user and the data of the candidates may be examined from the distance L received from the SIV retriever 38. Also, the similarity may be displayed to the user. The selection from the candidates may automatically be carried out using not a command from the user but a predetermined manner complying to, for example, �the first candidate is the selected sound data�. The display of the relevant data to the user is implemented by means of a display monitor or the like while the command for the selection can be entered by the user operating a keyboard, switches, or other controls. [0095] The sound data selected by the sound selection part 39 is then transferred to the sound output part 40 for providing the user with its audible form. Alternatively, the selected sound data may simply be displayed to the user as the result of retrieval of the sound information, such as a title, without being reproduced. [0096] Embodiment 2 [0097] Embodiment 2 of the present invention will be described in the form of a program for retrieving sound data. More particularly, this embodiment is a computer program for implementing the above described function of Embodiment 1. FIG. 9 is a block diagram of a procedure of signal processing showing the program for retrieving sound data of Embodiment 2. The program for retrieving sound data comprises a program for registering 51, a program for retrieving 52, and a sound database 53. The other arrangements and their functions are identical to those shown in the block diagram of Embodiment 1. [0098] The program for registering 51 and the program for retrieving 52 are saved as a computer program for a personal computer or a microcomputer in a storage area (a memory, a hard disk drive, a floppy disk, etc.) of the computer. The sound database 53 like that of Embodiment 1 is an array of sound data stored in a recording medium, such as a hard disk drive or a CD-ROM, of the computer. [0099] The program for registering 51 includes a sound data input process 54, a sound feature extracting process 55, an impression value calculating process 56, and a sound information input process 57. The program for registering 51 is initiated for extracting from a sound signal received by the computer a sound data and its attributed data which are then registered as retrieving data in the sound database 53. The data to be saved in the sound database 53 by the action of this program include a sound signal, sound features, sound impression values, and sound information. [0100] The program for retrieving 52 includes a retrieving query input process 58, a predictive impression values calculating process 59, a sound impression values retrieving process 60, a sound data selection process 61, and a sound data output process 62. The program for retrieving 52 is initiated for entering queries from the user and calculating the sound impression values (target sound impression values) of a predicted sound data. Then, the retrieving queries and the target impression values are used as the retrieving key for retrieving the attributed data of the sound data stored in the sound database 53. As some of the sound data of which the attributed data are corresponded to the retrieving key have been read out as the candidates. They are examined for selection as the final sound data to be played back with reference to other criterion including the selection parameters translated by symbolizing from the selection controlling actions of the user and the predetermined sequence of the sound data. The finally selected sound data is then released as a result of the retrieving process. [0101] Using the programs, any desired sound data can be accessed and received by the user entering the retrieving queries. The program for registering 51 and the program for retrieving 52 may be saved in removable mediums such as a CD-ROM 63, DVD-RAM, or DVD-ROM shown in FIG. 10 or a storage device of another computer over a computer network. Alternatively, the program for registering 51 and the program for retrieving 52 may be operated on two different computers respectively to access the sound database 53 having common storage areas. Also, the sound database 53 is produced and saved in any removable medium such as a floppy disk or an optical disk by the program for registering 51 and can be accessed by the program for retrieving 52 operated on another computer. [0102] Embodiment 3 [0103] A tempo extracting method and its apparatus which represent one of the sound features extracting technologies will now be described. FIG. 11 is a block diagram showing an arrangement of the tempo extracting apparatus. The tempo extracting apparatus comprises a sound attack point detector 71 (referred to as an SAP detector hereinafter), an autocorrelation calculator 72 (referred to as an ACR calculator throughout the drawing), a peak point detector 73, a beat structure analyzer 74, a temporary tempos calculator 75, and a correct tempo detector 76 (referred to as a CC detector throughout the drawings). [0104] The tempo extracting apparatus of this embodiment is designed for receiving a portion (about 30 seconds) of an audio signal as the input signal from a CD or a broadcast station. The SAP detector 71 detects the input signal for extracting the rise or onset time of sound components of e.g. snare drum, bass drum, guitar, and vocal. The SAP detector 71 generates a onset time sequence signal of the sound data based on the time and the amplitude. [0105] An exemplary method of detecting the onset time in the audio signal is depicted in �Beat tracking system for music, audio signal-selection of the music knowledge depending on the detection of the number of measures and the presence of percussion sounds� by Gotoh and Muraoka, the Information Processing Society of Japan, Proceeding 97-MUS-21-8, Vol. 97, No. 67, pp. 45-52, 1997. In the method, an FFT (or DFT) process is performed to the inputted audio signal at each frame of a given length to determine the power of each frequency component. The rise of sound is thus detected by examining a degree of difference in the power between the frames. As a result, the onset time of each sound component can be assumed. A time sequence audio signal of the inputted sound data can be generated by aligning on the time base the assumed onset time of each time component and the power level at the time. [0106] The ACR calculator 72 calculates an auto-correlation function of the time sequence audio signal of the sound data. Assuming that the time sequence audio signal is x[n], the delay time is m frames, and the calculation time takes N frames, the auto-correlation function A[m] based on the frame number m of the delay time can be calculated as the following Equation 17. A  ( m ) = ∑ N = 0 N - 1  x  ( n )  � ( n + m ) (Equation��17) [0107] An example of the auto-correlation function determine by the above manner is shown in FIG. 12. Tempo is detected based on these auto-correlation function. The peak point detector 73 calculates the peak or maximum of the auto-correlation function. In the example of FIG. 12, the peaks are denoted by the white dots. [0108] The beat structure analyzer 74 analyzes a beat structure of the inputted audio signal through examining the peaks of the autocorrelation function received from the peak point detector 73. The auto-correlation function determined by the ACR calculator 72 represents the periodicity of sound components in the inputted audio signal. For example, when sound components of the bass drum are contained in the audio signal and beaten at equal intervals of a quarter note length, the peaks at every quarter note position in the auto-correlation function may appear. Accordingly, by monitoring the peaks and their levels in the auto-correlation function, the periodicity of the onset time or beat of each sound component in the audio signal can successfully be analyzed. The beat structure is hence a rhythm system of each sound component of the music and can be expressed by the frequency and the intensity of locations of the beat or the note (sixteenth note, eighth note, quarter note, half note, etc.). In the example of FIG. 12, the beat structure is understood to be composed of first to fourth beat layers from the periodicity and output levels of peaks. Each beat layer represents the intensity of the beat corresponding to the note of a given length (e.g. a quarter note). [0109]FIG. 13 is a block diagram showing an arrangement of the beat structure analyzer 74A. The beat structure analyzer 74A includes a sorting part 81, a grouping part 82, and a beat structure parameters calculator 83 (referred to as a BSP calculator hereinafter and throughout the drawings). A procedure of beat structure analyzing in the arrangement is then explained. The sorting part 81 sorts the peak points of the auto-correlation function received from the peak point detector 73 shown in FIG. 11 in an order of amplitude. The peaks having similar amplitudes can then be grouped. The grouping part 82 separates the peaks into different amplitude groups. The BSP calculator 83 assigns the number of the groups as a beat layer number (four in this embodiment shown in FIG. 12) which is a parameter for defining the beat structure. [0110]FIG. 14 is a block diagram showing another beat structure analyzer 74B. The beat structure analyzer 74B includes a histogram generator 84 and a BSP calculator 85. This arrangement is different from that of the beat structure analyzer 74A shown in FIG. 13 by the fact that the histogram generator 84 is provided for grouping the peaks of the auto-correlation function. The histogram generator 84 generates a histogram based on the amplitude of the peaks. Thus, the histogram exhibits its maximum where a number of the peaks which are similar in amplitude is maximum. The BSP calculator 85 calculates the beat structure parameter from the peaks of the maximum histogram used for determining a distribution of the groups. [0111] The action of the tempo extracting apparatus having the above described arrangement will now be explained. The temporary tempos calculator 75 calculates some tempo candidates for which is though the tempo of the inputted audio signal from the peaks determined by the peak point detector 73. In common, the sound components are beaten at equal intervals of one measure, tow beats (a half note), or one beat (a quarter note) with accents. Accordingly, the candidate for the tempo can be determined from the maximum of the peaks of the auto-correlation function. For example, modern popular music often has snare drum sounds beaten at every second and fourth timings (at intervals of two tempo interval times) for the accent. It is hence assumed that the peak in the audio signal of such a music becomes maximum at the timings equivalent to the intervals of the two tempo interval time. [0112] In the example of FIG. 12, the peak P1 represents the maximum and the distance of time between the two peaks is equal to a length of one measure, two beats, or one beat. The tempo candidate is calculated from the number of quarter notes per minute determined by the duration to the peak P1 (100 frames, one frame being 86 ms). Accordingly, when duration of the peak P1 at equal intervals of one measure, two beats, and one beat, the tempo will be 207 BPM, 103 BPM, and 52 BPM, respectively. BPM stands for beats per minute as is a unit expressing the number of quarter notes per minute. The three measurements are now treated as the temporal tempo in FIG. 12. [0113] With reference to the beat structure, e.g. the number of beat layers, obtained from the beat structure analyzer 74, the CC detector 76 selects the correct tempo, which is most appropriate for the inputted audio signal, from the candidates determined by the temporary tempos calculator 75. The number of beat layers in the beat structure is one of the major parameters for determining the tempo. It is known throughout a series of previous analyzing processes over various popular music scores that when the tempo of the music piece is fast, then the number of levels in the beat structure is low in number (namely, not greater than approximately three). For example, in case the candidates for the temporary tempo are 220 BPM and 105 BPM, and the number of beat layers in the beat structure is four, it is then judged that the tempo of 105 BPM is most probable. It is because a deep beat layer sounds or sixteenth notes rarely appear periodically and frequently in sound of a fast tempo as 220 BPM. This is very common among most popular music scores. [0114]FIG. 12 illustrates beat layer 1 including another peak P2 which is similar in the amplitude to the peak P1 but doubled in the cycle. Beat layers 2 to 4 contains the peaks which are declined in the amplitude at every half the cycle. It is then concluded that beat layer 1 shows peaks of a cycle corresponding to two tempo interval time (a half note length), beat layer 2 shows peaks of a cycle corresponding to one tempo interval time (a quarter note length), level 3 shows peaks of a cycle corresponding to 0.5 tempo interval time (an eighth note length), and beat layer 4 shows peaks of a cycle corresponding to 0.25 tempo interval time (a sixteenth note length). [0115] Beat layer 1 may be at cycles of one full measure. It is however known in this case that beat layer 2 or lower may include a higher amplitude of the peak derived from the autocorrelation function of each common audio signal. Therefore, this embodiment is preferably arranged to assign the two tempo interval time to beat layer 1. Therefore, 103 BPM, which is one of the tempory tempos in case the beat layer 1, namely peak P1 is at the two tempo interval time is selected as a tempo of the inputted audio signal. [0116] This embodiment is explained as to the audio signal having the autocorrelation function shown in FIG. 12 an example, but the present invention can be applied with equal success to any other audio signal having another autocorrelation function pattern. [0117] It is to be understood that although the present invention has been described with regard to preferred embodiments thereof, various other embodiments and variants may occur to those skilled in the art, which are within the scope and spirit of the invention, and such other embodiments and variants are intended to be covered by the following claims. [0118] The text of japanese priority applications no. 2001-082150 filed on Mar. 22, 2001 and no. 2001-221240 filed on Jul. 23, 2001 is hereby incorporated by reference. Referenced byCiting PatentFiling datePublication dateApplicantTitleUS7053290Jun 17, 2004May 30, 2006Matsushita Electric Industrial Co., LtdMusic reproducing apparatus and music reproducing methodUS7203558 *Jun 3, 2002Apr 10, 2007Open Interface, Inc.Method for computing sense data and device for computing sense dataUS7260226 *Aug 25, 2000Aug 21, 2007Sony CorporationInformation retrieving method, information retrieving device, information storing method and information storage deviceUS7460919Sep 12, 2005Dec 2, 2008Panasonic CorporationMusic contents reproducing apparatusUS7534951 *Jul 13, 2006May 19, 2009Sony CorporationBeat extraction apparatus and method, music-synchronized image display apparatus and method, tempo value detection apparatus, rhythm tracking apparatus and method, and music-synchronized display apparatus and methodUS7563971 *Sep 30, 2004Jul 21, 2009Stmicroelectronics Asia Pacific Pte. 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