CN107845299A - A kind of computer based collective music teaching network method - Google Patents

A kind of computer based collective music teaching network method Download PDF

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Publication number
CN107845299A
CN107845299A CN201711195235.0A CN201711195235A CN107845299A CN 107845299 A CN107845299 A CN 107845299A CN 201711195235 A CN201711195235 A CN 201711195235A CN 107845299 A CN107845299 A CN 107845299A
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electronic music
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刘柳
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    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B5/00—Electrically-operated educational appliances
    • G09B5/08—Electrically-operated educational appliances providing for individual presentation of information to a plurality of student stations
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B15/00—Teaching music

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  • Physics & Mathematics (AREA)
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Abstract

The invention belongs to Web-based instruction field, discloses a kind of computer based collective music teaching network method, teacher's terminal；Electronic music apparatus；Converter；Managing device；Central processing unit；Converter；Electronic music apparatus；Student terminal.The analog audio data of electronic music apparatus output of the present invention is converted to digital audio-frequency data and with information packet transmissions to communication network, the digital audio-frequency data of the packet received from communication network is converted to analog audio data and is input to electronic music apparatus, managing device has path clustering unit, path clustering unit is directed to the digital audio-frequency data transmitted on a communication network, controls the transmission RX path of teacher's terminal and multiple student's terminal rooms；Remotely given lessons by real-time performance teacher, and the control and its management to course can be realized by managing device；The study course of each student is controlled by its path clustering unit, and individually problematic student can be given lessons.

Description

A kind of computer based collective music teaching network method
Technical field
The invention belongs to Web-based instruction field, more particularly to a kind of computer based collective music teaching network method.
Background technology
At present, developing rapidly with network technology, online teaching increasingly become a kind of main trend, stay indoors with regard to energy Attend class increasingly become student's processing can be with a kind of mode of time, but there is certain drawback in online teaching, such as audio Distortion, video/audio it is asynchronous so that the defects of very big be present in present music network course.
In summary, the problem of prior art is present be：Existing network teaching method causes music distortion, and video/audio is not It is synchronous, the needs of instructor can not be met.
The content of the invention
The problem of existing for prior art, the invention provides a kind of computer based collective music teaching network side Method.
The present invention is achieved in that a kind of computer based collective music teaching network method, described based on calculating The collective music teaching network method electronic music apparatus input and output analog audio data of machine；Converter is from electronic music apparatus The analog audio data of output is converted to digital audio-frequency data and with information packet transmissions to communication network, meanwhile, from communication network The digital audio-frequency data of the packet received on network is converted to analog audio data and is input to electronic music apparatus, management dress Put and be directed to the digital audio-frequency data transmitted on a communication network with path clustering unit, path clustering unit, control teacher is whole Transmission RX path between end and multiple student terminals；
The sensor node energy consumption of the communication network is divided into transmitting data energy consumption, receives data energy consumption and aggregated data energy Consumption, the distance of node to receiving point are less than threshold value d0, then using free space model, otherwise, using multipath attenuation model, from And the energy expenditure for launching the receiving point that bit data is to distance is as follows：
Wherein EelecFor radiating circuit energy expenditure, εfsFor energy, ε needed for power amplification circuit under free space modelmp For energy needed for power amplification circuit under multipath attenuation model, bit data energy consumption is received：
ERx(l)=l × Eelec；
It polymerize the energy expenditure of bit data：
EA=l × EDA；
Wherein EDARepresent the energy expenditure of 1 bit data of polymerization；
The human body attitude information one-level index that the student terminal obtains encodes and the method for matching is：
Step 1, it is assumed that the left and right upper arm of human body, the space angle between left and right thigh and upper level bone are respectively ω1, ω2, ω3, ω4,Define the one-level index coding function G (ω of each frame1, ω2, ω3, ω4) be：
Step 2, then, to encoding identical, that is, function G (ω in candidate segment C and exemplary section Q1, ω2, ω3, ω4) value identical consecutive frame polymerize, respectively obtain search sequence Q and candidate sequence C coded sequence Hc and Hq：
One-level index matching is first carried out during retrieval to retrieval fragment and candidate segment, if Hq is consistent with Hc, carries out two level The retrieval of index；
The method that the secondary index value of the human body multidate information of teacher's terminal calculates is：
Step 1, left and right upper arm, left and right thigh, left and right underarm, left and right shank and the upper level bone of present frame human body it Between space angle be (ω1, ω2, ω3, ω4, ω5, ω6, ω7, ω8), the analog value of previous frame isThen the secondary index function of frame is defined as follows：
The value of function is considered as two octuple vector (ω1, ω2, ω3, ω4, ω5, ω6, ω7, ω8) and Space length, because the sampling interval between every two frame is fixed , so L is actually a kind of measurement of front and rear two frame attitudes vibration sizes, L reflects the dynamic of each frame in motion segments Feature；
The path clustering unit calculates reception signal s (t) broad sense second-order cyclic cumulantAs follows Carry out：
Reception signal s (t) characteristic parameter M2Theoretical valueSpecific formula for calculation is：
Understood by calculating, bpsk signal and msk signalIt is 1, QPSK, 8PSK, 16QAM and 64QAM signal 'sBe 0, it is possible thereby to least mean-square error grader by BPSK, msk signal and QPSK, 8PSK, 16QAM, 64QAM signals separate；For bpsk signal, in Generalized Cyclic cumulant amplitude spectrumOn only in carrier frequency position In the presence of an obvious spectral peak, and msk signal respectively has an obvious spectral peak at two frequencies, thus can pass through characteristic parameter M2With Detect Generalized Cyclic cumulant amplitude spectrumSpectral peak number bpsk signal and msk signal are identified；
The wireless receiving station is traveled through using two-dimentional sliding window to the Hough transform matrix obtained in step S2, and Energy accumulation is done in window, so as to obtain test statistics；
The length for setting two-dimentional sliding window P (m, n) first is L, width K, wherein, m and n represent two-dimentional sliding window respectively Abscissa and ordinate；The length that the Hough transform matrix obtained in setting steps S2 is M (ρ, θ) is M, width N, then Hough transform matrix is divided intoBlock, whereinExpression rounds downwards；
Then it is (L, K) to calculate Hough transform matrix points respectively, (2L, K) ..., (pL, K), (L, 2K), (2L, 2K) ..., (pL, 2K) ..., (pL, qK) place window P (m, n) energy and, obtain p × q test statistics Q (m, n), its count Calculation method is as follows：
Another object of the present invention is to provide a kind of base of the computer based collective music teaching network method In the collective music Instructing network of computer, the computer based collective music Instructing network is provided with：Religion Teacher's terminal；Electronic music apparatus；Converter；Managing device；Central processing unit；Converter；Electronic music apparatus；Student terminal.
Teacher's terminal is electrically connected to the electronic music apparatus；The electronic music apparatus is electrically connected to the conversion Device；The converter is electrically connected to the managing device；The managing device is electrically connected to the central processing unit；The center Processor is electrically connected to the converter；The converter is electrically connected to the electronic music apparatus；The electronic music apparatus It is electrically connected to the student terminal；
The electronic music apparatus input and output analog audio data；
The analog audio data exported from electronic music apparatus is converted to digital audio-frequency data and with letter by the converter Breath bag is transferred on communication network, meanwhile, the digital audio-frequency data of the packet received from communication network is converted into mould Intend voice data and be input to electronic music apparatus；
The managing device has path clustering unit, the digital sound that path clustering cell processing is transmitted on a communication network Frequency evidence；
The transmission RX path of control teacher's terminal and multiple student's terminal rooms.
Further, the student terminal, teacher's terminal connect external power source by wire.
Further, the converter of each terminal can be by the analog audio frequency such as the performance sound exported from electronic musical instrument and voice According to being converted to numerical data.
Further, path clustering unit is included inside the managing device.
The present invention is remotely given lessons by real-time performance teacher, and can be realized by managing device control to course and It is managed.The study course of each student is controlled by its path clustering unit, and can be individually to problematic student Give lessons, highly promote the use of.
Brief description of the drawings
Fig. 1 is computer based collective music teaching network method structural representation provided in an embodiment of the present invention；
In figure：1st, teacher's terminal；2nd, electronic music apparatus；3rd, converter；4th, managing device；5th, central processing unit；6th, turn Parallel operation；7th, electronic music apparatus；8th, student terminal.
Embodiment
In order to further understand the content, features and effects of the present invention, hereby enumerating following examples, and coordinate accompanying drawing Describe in detail as follows.
The structure of the present invention is explained in detail below in conjunction with the accompanying drawings.
As shown in figure 1, the computer based collective music teaching network method described in the embodiment of the present invention includes：Teacher Terminal 1；Electronic music apparatus 2；Converter 3；Managing device 4；Central processing unit 5；Converter 6；Electronic music apparatus 7；Student Terminal 8.
Teacher's terminal 1 is electrically connected to the electronic music apparatus 2；The electronic music apparatus 2 is electrically connected to described Converter 3；The converter 3 is electrically connected to the managing device 4；
The managing device 4 is electrically connected to the central processing unit 5；The central processing unit 5 is electrically connected to the conversion Device 6；The converter 6 is electrically connected to the electronic music apparatus 7；It is whole that the electronic music apparatus 7 is electrically connected to the student End 8.
Further, the student terminal 8, teacher's terminal 1 connect external power source by wire.
Further, the converter 6 of each terminal can be by analogue audio frequencies such as the performance sound exported from electronic musical instrument and voices Data are converted to numerical data.
Further, the inside of managing device 4 includes path clustering unit.
The sensor node energy consumption of the communication network is divided into transmitting data energy consumption, receives data energy consumption and aggregated data energy Consumption, the distance of node to receiving point are less than threshold value d0, then using free space model, otherwise, using multipath attenuation model, from And the energy expenditure for launching the receiving point that bit data is to distance is as follows：
Wherein EelecFor radiating circuit energy expenditure, εfsFor energy, ε needed for power amplification circuit under free space modelmp For energy needed for power amplification circuit under multipath attenuation model, bit data energy consumption is received：
ERx(l)=l × Eelec；
It polymerize the energy expenditure of bit data：
EA=l × EDA；
Wherein EDARepresent the energy expenditure of 1 bit data of polymerization；
The human body attitude information one-level index that the student terminal obtains encodes and the method for matching is：
Step 1, it is assumed that the left and right upper arm of human body, the space angle between left and right thigh and upper level bone are respectively ω1, ω2, ω3, ω4,Define the one-level index coding function G (ω of each frame1, ω2, ω3, ω4) be：
Step 2, then, to encoding identical, that is, function G (ω in candidate segment C and exemplary section Q1, ω2, ω3, ω4) value identical consecutive frame polymerize, respectively obtain search sequence Q and candidate sequence C coded sequence Hc and Hq：
One-level index matching is first carried out during retrieval to retrieval fragment and candidate segment, if Hq is consistent with Hc, carries out two level The retrieval of index；
The method that the secondary index value of the human body multidate information of teacher's terminal calculates is：
Step 1, left and right upper arm, left and right thigh, left and right underarm, left and right shank and the upper level bone of present frame human body it Between space angle be (ω1, ω2, ω3, ω4, ω5, ω6, ω7, ω8), the analog value of previous frame isThen the secondary index function of frame is defined as follows：
The value of function is considered as two octuple vector (ω1, ω2, ω3, ω4, ω5, ω6, ω7, ω8) and Space length, because the sampling interval between every two frame is fixed , so L is actually a kind of measurement of front and rear two frame attitudes vibration sizes, L reflects the dynamic of each frame in motion segments Feature；
The path clustering unit calculates reception signal s (t) broad sense second-order cyclic cumulantAs follows Carry out：
Reception signal s (t) characteristic parameter M2Theoretical valueSpecific formula for calculation is：
Understood by calculating, bpsk signal and msk signalIt is 1, QPSK, 8PSK, 16QAM and 64QAM signal 'sBe 0, it is possible thereby to least mean-square error grader by BPSK, msk signal and QPSK, 8PSK, 16QAM, 64QAM signals separate；For bpsk signal, in Generalized Cyclic cumulant amplitude spectrumOn only in carrier frequency position In the presence of an obvious spectral peak, and msk signal respectively has an obvious spectral peak at two frequencies, thus can pass through characteristic parameter M2With Detect Generalized Cyclic cumulant amplitude spectrumSpectral peak number bpsk signal and msk signal are identified；
The wireless receiving station is traveled through using two-dimentional sliding window to the Hough transform matrix obtained in step S2, and Energy accumulation is done in window, so as to obtain test statistics；
The length for setting two-dimentional sliding window P (m, n) first is L, width K, wherein, m and n represent two-dimentional sliding window respectively Abscissa and ordinate；The length that the Hough transform matrix obtained in setting steps S2 is M (ρ, θ) is M, width N, then Hough transform matrix is divided intoBlock, whereinExpression rounds downwards；
Then it is (L, K) to calculate Hough transform matrix points respectively, (2L, K) ..., (pL, K), (L, 2K), (2L, 2K) ..., (pL, 2K) ..., (pL, qK) place window P (m, n) energy and, obtain p × q test statistics Q (m, n), its count Calculation method is as follows：
The electronic music apparatus of the present invention, its input and output analog audio data；Converter, it will be from electronic music apparatus The analog audio data of output is converted to digital audio-frequency data and with information packet transmissions to communication network, meanwhile, will be from communication The digital audio-frequency data of the packet received on network is converted to analog audio data and is input to electronic music apparatus, management Device has path clustering unit, and the path clustering unit is directed to the digital audio-frequency data transmitted on a communication network, control religion The transmission RX path of teacher's terminal and multiple student's terminal rooms.The present invention is remotely given lessons by real-time performance teacher, and And the control and its management to course can be realized by managing device.Control each student's by its path clustering unit Study course, and individually problematic student can be given lessons, the present invention highly promotes the use of.
It is described above to be only the preferred embodiments of the present invention, any formal limitation not is made to the present invention, Every technical spirit according to the present invention belongs to any simple modification made for any of the above embodiments, equivalent variations and modification In the range of technical solution of the present invention.

Claims (5)

  1. A kind of 1. computer based collective music teaching network method, it is characterised in that computer based collective sound Happy teaching network method electronic music apparatus input and output analog audio data；The simulation that converter exports from electronic music apparatus Voice data is converted to digital audio-frequency data and with information packet transmissions to communication network, meanwhile, received from communication network The digital audio-frequency data of packet be converted to analog audio data and be input to electronic music apparatus, managing device has path Control unit, path clustering unit are directed to the digital audio-frequency data that transmits on a communication network, control teacher's terminal and multiple Transmission RX path between student terminal；
    The sensor node energy consumption of the communication network is divided into transmitting data energy consumption, receives data energy consumption and aggregated data energy consumption, Node is less than threshold value d to the distance of receiving point0, then using free space model, otherwise, using multipath attenuation model, so as to The energy expenditure for the receiving point that transmitting bit data is to distance is as follows：
    <mrow> <msub> <mi>E</mi> <mrow> <mi>T</mi> <mi>X</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>l</mi> <mo>,</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mi>l</mi> <mo>&amp;times;</mo> <msub> <mi>E</mi> <mrow> <mi>e</mi> <mi>l</mi> <mi>e</mi> <mi>c</mi> </mrow> </msub> <mo>+</mo> <mi>l</mi> <mo>&amp;times;</mo> <msub> <mi>&amp;epsiv;</mi> <mrow> <mi>f</mi> <mi>s</mi> </mrow> </msub> <mo>&amp;times;</mo> <msub> <mi>d</mi> <mn>2</mn> </msub> </mrow> </mtd> <mtd> <mrow> <mi>d</mi> <mo>&lt;</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>l</mi> <mo>&amp;times;</mo> <msub> <mi>E</mi> <mrow> <mi>e</mi> <mi>l</mi> <mi>e</mi> <mi>c</mi> </mrow> </msub> <mo>+</mo> <mi>l</mi> <mo>&amp;times;</mo> <msub> <mi>&amp;epsiv;</mi> <mrow> <mi>m</mi> <mi>p</mi> </mrow> </msub> <mo>&amp;times;</mo> <msub> <mi>d</mi> <mn>4</mn> </msub> </mrow> </mtd> <mtd> <mrow> <mi>d</mi> <mo>&amp;GreaterEqual;</mo> <msub> <mi>d</mi> <mn>0</mn> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>;</mo> </mrow>
    Wherein EelecFor radiating circuit energy expenditure, εfsFor energy, ε needed for power amplification circuit under free space modelmpTo be more Energy needed for power amplification circuit under path attenuation model, receive bit data energy consumption：
    ERx(l)=l × Eelec；
    It polymerize the energy expenditure of bit data：
    EA=l × EDA；
    Wherein EDARepresent the energy expenditure of 1 bit data of polymerization；
    The human body attitude information one-level index that the student terminal obtains encodes and the method for matching is：
    Step 1, it is assumed that the left and right upper arm of human body, the space angle between left and right thigh and upper level bone is respectively ω1, ω2, ω3, ω4,Define the one-level index coding function G (ω of each frame1, ω2, ω3, ω4) be：
    <mrow> <mtable> <mtr> <mtd> <mrow> <mi>G</mi> <mrow> <mo>(</mo> <msub> <mi>&amp;omega;</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>&amp;omega;</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>&amp;omega;</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>&amp;omega;</mi> <mn>4</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>4</mn> </munderover> <mi>F</mi> <mrow> <mo>(</mo> <msub> <mi>&amp;omega;</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;times;</mo> <msup> <mn>10</mn> <mi>i</mi> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>F</mi> <mrow> <mo>(</mo> <mi>&amp;omega;</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>1</mn> <mo>,</mo> <mi>&amp;omega;</mi> <mo>&amp;Element;</mo> <mo>&amp;lsqb;</mo> <mn>0</mn> <mo>,</mo> <mn>60</mn> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>2</mn> <mo>,</mo> <mi>&amp;omega;</mi> <mo>&amp;Element;</mo> <mo>&amp;lsqb;</mo> <mn>60</mn> <mo>,</mo> <mn>120</mn> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>3</mn> <mo>,</mo> <mi>&amp;omega;</mi> <mo>&amp;Element;</mo> <mo>&amp;lsqb;</mo> <mn>120</mn> <mo>,</mo> <mn>180</mn> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>4</mn> <mo>,</mo> <mi>o</mi> <mi>t</mi> <mi>h</mi> <mi>e</mi> <mi>r</mi> <mi>s</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow> </mtd> </mtr> </mtable> <mo>;</mo> </mrow>
    Step 2, then, to encoding identical, that is, function G (ω in candidate segment C and exemplary section Q1, ω2, ω3, ω4) Value identical consecutive frame polymerize, respectively obtain search sequence Q and candidate sequence C coded sequence Hc and Hq：
    <mrow> <mtable> <mtr> <mtd> <mrow> <mi>H</mi> <mi>c</mi> <mo>:</mo> <mi>G</mi> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mi>G</mi> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>G</mi> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>G</mi> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mi>x</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>H</mi> <mi>q</mi> <mo>:</mo> <mi>G</mi> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mi>G</mi> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>G</mi> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>G</mi> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mi>y</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> <mo>;</mo> </mrow>
    One-level index matching is first carried out during retrieval to retrieval fragment and candidate segment, if Hq is consistent with Hc, carries out secondary index Retrieval；
    The method that the secondary index value of the human body multidate information of teacher's terminal calculates is：
    Step 1, between the left and right upper arm of present frame human body, left and right thigh, left and right underarm, left and right shank and upper level bone Space angle is (ω1, ω2, ω3, ω4, ω5, ω6, ω7, ω8), the analog value of previous frame isThen the secondary index function of frame is defined as follows：
    <mrow> <mi>L</mi> <mo>=</mo> <msup> <mrow> <mo>(</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>8</mn> </munderover> <msup> <mrow> <mo>(</mo> <mrow> <msub> <mi>&amp;omega;</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>&amp;phi;</mi> <mi>i</mi> </msub> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>)</mo> </mrow> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </msup> <mo>;</mo> </mrow>
    The value of function is considered as two octuple vector (ω1, ω2, ω3, ω4, ω5, ω6, ω7, ω8) and Space length, because the sampling interval between every two frame is Fixed, so L is actually a kind of measurement of front and rear two frame attitudes vibration sizes, L reflects each frame in motion segments Behavioral characteristics；
    The path clustering unit calculates reception signal s (t) broad sense second-order cyclic cumulantCarry out as follows：
    <mrow> <msubsup> <mi>GC</mi> <mrow> <mi>s</mi> <mo>,</mo> <mn>20</mn> </mrow> <mi>&amp;beta;</mi> </msubsup> <mo>=</mo> <msubsup> <mi>GM</mi> <mrow> <mi>s</mi> <mo>,</mo> <mn>20</mn> </mrow> <mi>&amp;beta;</mi> </msubsup> <mo>;</mo> </mrow>
    Reception signal s (t) characteristic parameter M2Theoretical valueSpecific formula for calculation is：
    <mrow> <msubsup> <mi>GC</mi> <mrow> <mi>s</mi> <mo>,</mo> <mn>20</mn> </mrow> <mi>&amp;beta;</mi> </msubsup> <mo>=</mo> <mfrac> <mn>1</mn> <mi>N</mi> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mi>a</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mi>a</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <msup> <mrow> <mo>|</mo> <mi>l</mi> <mi>n</mi> <mo>|</mo> <mi>a</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>|</mo> <mo>|</mo> </mrow> <mn>2</mn> </msup> </mrow>
    Understood by calculating, bpsk signal and msk signalIt is 1, QPSK, 8PSK, 16QAM and 64QAM signalIt is 0, it is possible thereby to least mean-square error grader by BPSK, msk signal and QPSK, 8PSK, 16QAM, 64QAM Signal separates；For bpsk signal, in Generalized Cyclic cumulant amplitude spectrumOn only carrier frequency position exist one Individual obvious spectral peak, and msk signal respectively has an obvious spectral peak at two frequencies, thus can pass through characteristic parameter M2It is wide with detection Adopted cyclic cumulants amplitude spectrumSpectral peak number bpsk signal and msk signal are identified；
    The wireless receiving station is traveled through using two-dimentional sliding window to the Hough transform matrix obtained in step S2, and in window Energy accumulation is inside done, so as to obtain test statistics；
    The length for setting two-dimentional sliding window P (m, n) first is L, width K, wherein, m and n represent the horizontal stroke of two-dimentional sliding window respectively Coordinate and ordinate；The length that the Hough transform matrix obtained in setting steps S2 is M (ρ, θ) is M, width N, then Hough Transformation matrix is divided intoBlock, whereinExpression rounds downwards；
    Then it is (L, K) to calculate Hough transform matrix points respectively, (2L, K) ..., (pL, K), (L, 2K), (2L, 2K) ..., (pL, 2K) ..., (pL, qK) place window P (m, n) energy and, obtain p × q test statistics Q (m, n), its computational methods It is as follows：
    <mrow> <mi>Q</mi> <mrow> <mo>(</mo> <mi>m</mi> <mo>,</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>u</mi> <mo>=</mo> <mrow> <mo>(</mo> <mi>m</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mi>L</mi> <mo>+</mo> <mn>1</mn> </mrow> <mrow> <mi>m</mi> <mi>L</mi> </mrow> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>v</mi> <mo>=</mo> <mrow> <mo>(</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mi>K</mi> <mo>+</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> <mi>K</mi> </mrow> </munderover> <mi>M</mi> <mrow> <mo>(</mo> <msub> <mi>&amp;rho;</mi> <mi>u</mi> </msub> <mo>,</mo> <msub> <mi>&amp;theta;</mi> <mi>v</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mi>m</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>p</mi> <mo>,</mo> <mi>n</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>q</mi> <mo>.</mo> </mrow>
  2. A kind of 2. computer based collective sound of computer based collective music teaching network method as claimed in claim 1 Happy Instructing network, it is characterised in that the computer based collective music Instructing network is provided with：Teacher is whole End；Electronic music apparatus；Converter；Managing device；Central processing unit；Converter；Electronic music apparatus；Student terminal.
    Teacher's terminal is electrically connected to the electronic music apparatus；The electronic music apparatus is electrically connected to the converter； The converter is electrically connected to the managing device；The managing device is electrically connected to the central processing unit；The centre Reason device is electrically connected to the converter；The converter is electrically connected to the electronic music apparatus；The electronic music apparatus electricity It is connected to the student terminal；
    The electronic music apparatus input and output analog audio data；
    The analog audio data exported from electronic music apparatus is converted to digital audio-frequency data and with packet by the converter It is transferred on communication network, meanwhile, the digital audio-frequency data of the packet received from communication network is converted into analog audio Frequency evidence is simultaneously input to electronic music apparatus；
    The managing device has path clustering unit, the DAB number that path clustering cell processing is transmitted on a communication network According to；
    The transmission RX path of control teacher's terminal and multiple student's terminal rooms.
  3. 3. computer based collective music Instructing network as claimed in claim 2, it is characterised in that the student is whole End, teacher's terminal connect external power source by wire.
  4. 4. computer based collective music Instructing network as claimed in claim 2, it is characterised in that each terminal Converter the performance sound exported from electronic musical instrument and speech simulation voice data can be converted to numerical data.
  5. 5. computer based collective music Instructing network as claimed in claim 2, it is characterised in that the management dress Putting inside includes path clustering unit.
CN201711195235.0A 2017-11-24 2017-11-24 A kind of computer based collective music teaching network method Pending CN107845299A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112767781A (en) * 2019-10-21 2021-05-07 鼎卓创意科技股份有限公司 Keyboard musical instrument teaching system and keyboard musical instrument teaching method

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101030334A (en) * 2006-02-28 2007-09-05 雅马哈株式会社 Collectivity music teaching system and music network system
US20110290096A1 (en) * 2010-06-01 2011-12-01 Paterson Jennifer M System for monitoring the progress of a musical student
CN104268285A (en) * 2014-10-21 2015-01-07 厦门大学 Motion capture database searching method based on dynamic bending algorithm
CN104618997A (en) * 2015-01-28 2015-05-13 西安电子科技大学 Data aggregation method based on non-uniform grids
CN104658358A (en) * 2015-03-12 2015-05-27 广州东呈电子有限公司 Music teaching system
CN105007130A (en) * 2015-06-12 2015-10-28 西安电子科技大学 Method for detecting LFM signal under low signal-to-noise ratio
CN106901951A (en) * 2017-02-27 2017-06-30 秦艳玲 A kind of Multifunctional bone surgical postoperative care stand

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101030334A (en) * 2006-02-28 2007-09-05 雅马哈株式会社 Collectivity music teaching system and music network system
US20110290096A1 (en) * 2010-06-01 2011-12-01 Paterson Jennifer M System for monitoring the progress of a musical student
CN104268285A (en) * 2014-10-21 2015-01-07 厦门大学 Motion capture database searching method based on dynamic bending algorithm
CN104618997A (en) * 2015-01-28 2015-05-13 西安电子科技大学 Data aggregation method based on non-uniform grids
CN104658358A (en) * 2015-03-12 2015-05-27 广州东呈电子有限公司 Music teaching system
CN105007130A (en) * 2015-06-12 2015-10-28 西安电子科技大学 Method for detecting LFM signal under low signal-to-noise ratio
CN106901951A (en) * 2017-02-27 2017-06-30 秦艳玲 A kind of Multifunctional bone surgical postoperative care stand

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112767781A (en) * 2019-10-21 2021-05-07 鼎卓创意科技股份有限公司 Keyboard musical instrument teaching system and keyboard musical instrument teaching method

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