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Music Analyser

Music Analyser. MSc in Information Engineering. Jean-Marie Froux MSc Information Engineering 2003. Abstract. Transcription of music : from the sound to the paper through computer. The technologies used to develop : C#, C++, DirectX. Contents. Introduction 1: Theory

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Music Analyser

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  1. Music Analyser MSc in Information Engineering Jean-Marie Froux MSc Information Engineering 2003

  2. Abstract Transcription of music : from the sound to the paper through computer. The technologies used to develop : C#, C++, DirectX Music Analyser - JM Froux City University - 2003

  3. Contents • Introduction • 1: Theory • 2: Wave file analyser (in C#) • 3: Music analyser (in C++) • Conclusion • Questions Music Analyser - JM Froux City University - 2003

  4. Introduction • What is music transcription? • Music and computers • Why is it interesting ? • The programs developed Music Analyser - JM Froux City University - 2003

  5. 1/ Musical Theory • 1.1 The physic of sound • 1.2 The Fourier Analysis Music Analyser - JM Froux City University - 2003

  6. 1.1 The physic of sound • There are 4 parameters to describe a sound: • Duration, (s). • Intensity, (dB). • Height, (Hz). Music Analyser - JM Froux City University - 2003

  7. 1.1 The physic of sound Frequency and notes (1) Ear sensibility : 20 Hz to 20 kHz represents 10 octaves A flute can play between 130.81 Hz and 1046.5 Hz Change octave by doubling frequency Music Analyser - JM Froux City University - 2003

  8. 1.1 The physic of sound Frequency and notes (2) • 12 intervals in an octave • Tone is an interval of T = 62 • Semitone is an interval of T = 122(=1.0594) • We obtain thus the scale for Major C: Music Analyser - JM Froux City University - 2003

  9. 1.1 The physic of sound Frequency and notes (3) All change possible with sharps and flats. The basic (reference) note is “A3” 440Hz. Other are deduced by multiplication with T= 122 Music Analyser - JM Froux City University - 2003

  10. The physic of sound • There are 4 parameters to describe a sound: • Duration, (s). • Intensity, (dB). • Height, (Hz). • Tone Music Analyser - JM Froux City University - 2003

  11. 1.1 The physic of sound The tone (1) • A sound is made of many frequencies : • The fundamental • Harmonics (multiples of the fundamental) Music Analyser - JM Froux City University - 2003

  12. Magnitude Magnitude 2 4 6 8 Frequency 3 5 7 Frequency 1.1 The physic of sound The tone (2) Each instrument produces a specific spectrum : The same sound played by a oboe A sound played by a clarinet Music Analyser - JM Froux City University - 2003

  13. 1.2 Fourier transform • Any pseudoperiodic signal can be broken up into an infinite sum of sinusoidal signals of frequency multiple of a basic frequency. Music Analyser - JM Froux City University - 2003

  14. 1.2 Fourier transform Interest • The FFT allows us to see the frequencies and magnitudes of sinusoidal signals. Magnitude Frequency frequency time Music Analyser - JM Froux City University - 2003

  15. 2/ Wave file Analyser • 2.1 Design • 2.2 Implementation • 2.3 Testing Music Analyser - JM Froux City University - 2003

  16. 2.1 Wave Analyser Design 2.1 Design Record data Analyse data Write score Music Analyser - JM Froux City University - 2003

  17. 2.1 Wave Analyser Design Record data Wave format tag (compression) Select capture device Average bytes per second Samples per second Create capture buffer Bits per sample Block Align Start Channel(s) Music Analyser - JM Froux City University - 2003

  18. 2.1 Wave Analyser Design Analysis Analyse the data chunk Open the file Get the file size Decode the header chunk Create an array of the same size Copy the data in the array Music Analyser - JM Froux City University - 2003

  19. 2.1 Wave Analyser Design Analysis (2) • The program the should analyse the sample with an FFT and find the notes. • We will see the algorithm with the second program. • Once the notes found, they are stored in a file. Music Analyser - JM Froux City University - 2003

  20. 2.1 Wave Analyser Design Write notes Open file title Read file signature notes Draw the score Music Analyser - JM Froux City University - 2003

  21. 2.1 Wave Analyser Design The .notes file Rules : • First line: Title: Title • Second line : Signature1: Int • Third line : Signature: Int • Then the notes described by letters A to G. • 3 octaves lower (add: l), medium (m=default), and higher (h) • Sharp (s) and flat (f) • Timing: 16th (add x), 8th (add e), quarter is default, half (a), three-quarter (t), whole (w). Music Analyser - JM Froux City University - 2003

  22. 2.2 Wave Analyser Testing Testing • The software: Music Analyser - JM Froux City University - 2003

  23. 2.2 Wave Analyser Testing Testing • Capture devices and record: Music Analyser - JM Froux City University - 2003

  24. 2.2 Wave Analyser Testing Testing • Analysis Music Analyser - JM Froux City University - 2003

  25. 2.2 Wave Analyser Testing Testing • The score: Music Analyser - JM Froux City University - 2003

  26. 2.Wave Analyser Conclusion • C# is a very good programming language, but it needs the latest version of DirectX9. • As it is very new, the help is poor. • That’s why I have decided to change my way, and I passed to C++. Music Analyser - JM Froux City University - 2003

  27. 3/ The sound analyser in real time • 3.1 Sampling. • 3.2 FFT. • 3.3 Search for the note. • 3.4 Display. • 3.5 Save. • 3.6 The recognition of rhythm. Music Analyser - JM Froux City University - 2003

  28. 3/ The sound analyser in real time 3.1 Sampling • tell the soundcard to start sampling the input from the microphone • store the samples in a client buffer • The client creates a separate thread which is suspended waiting on an event • Once the buffer is full, notifies the client • The client thread does the necessary work and waits for the next event. Music Analyser - JM Froux City University - 2003

  29. 3/ The sound analyser in real time 3.2 FFT • The FFT buffer is filled with samples in bit-reversed order • The bit reversal is done, converts real samples into complex numbers (with the imaginary part set to zero) • Then the FFT calculation is organized into bunches. Music Analyser - JM Froux City University - 2003

  30. 3/ The sound analyser in real time 3.3 Find the note • For each FFT Recover the frequency and magnitude of the highest peak if magnitude is above the noise, seek if there is a peak at frequency divided by 2. Music Analyser - JM Froux City University - 2003

  31. 3/ The sound analyser in real time 3.3 Find the note (2) • If yes: The peak of higher magnitude isn’t the fundamental (but the first harmonic) Example with a guitar: spectrum of an A2: In this case, the highest peak is the first harmonic, we obtain the fundamental by dividing by 2 this frequency Music Analyser - JM Froux City University - 2003

  32. 3/ The sound analyser in real time 3.3 Find the note (3) • If not: seek a peak at frequency multiplied by 2 (the first harmonic). If there is one, the fundamental is the peak of higher magnitude. Then find the note with the table of frequencies Music Analyser - JM Froux City University - 2003

  33. 3/ The sound analyser in real time 3.3 Display the note (1) • Problem: not display several times the same note played once. • If for 2 consecutive FFT • The note found is the same • The magnitude of the second is not higher It is the same note. • If the magnitude changes, it means that the note is played again. Music Analyser - JM Froux City University - 2003

  34. 3/ The sound analyser in real time 3.3 Display the note (2) • The notes are represented by an “o” and a # if needed. To help the position of the note, I have adapted it to the octave: • The notes go from the "G" of octave 1 until "A" of octave 3 • For the too high or too low notes the octave is modified in order to display the note on the scale. Music Analyser - JM Froux City University - 2003

  35. 3/ The sound analyser in real time 3.3 Save the notes (1) • Important to allow musician to recover their work in another software. • First step to MIDI file, write the data in a .txt file Music Analyser - JM Froux City University - 2003

  36. 3/ The sound analyser in real time 3.3 Save the notes (2) • Example: MThd | Format=0 | # of Tracks=1 | Division=96 Track #0 **************************************** Time Event 1: 1: 0 |On Note | chan= 1 | pitch=D 1 | vol=127 2: 0 |Off Note | chan= 1 | pitch=d 1 | vol=100 | On Note | chan= 1 | pitch=A 2 | vol=127 3: 0 |Off Note | chan= 1 | pitch=a 2 | vol=100 |End of track| Music Analyser - JM Froux City University - 2003

  37. 3/ The sound analyser in real time 3.4 Rhythm recognition • Metronome • Help the musician when recording • Can give the tempo of the piece • Trace.txt: Save the tempo, the note, the value of the timer and the amplitude. • E.g.: 60. C. 0. 608. C#. 47. 1407. D. 94. 1212. D. 141. 1675. D. 188. 1880. Next step is the analysis of this file. Music Analyser - JM Froux City University - 2003

  38. 3/ The sound analyser in real time Testing • The software: Music Analyser - JM Froux City University - 2003

  39. 3/ The sound analyser in real time Testing • The software: Music Analyser - JM Froux City University - 2003

  40. 3/ The sound analyser in real time Conclusion • With C++ I found complete help on the Internet • It gives a program which works well. • Some improvements can be done to continue this work. Music Analyser - JM Froux City University - 2003

  41. Further Work • Rhythm recognition • Detection of tonality • Polyphonic recognition? Music Analyser - JM Froux City University - 2003

  42. QUESTIONS ??? ??? Music Analyser - JM Froux City University - 2003

  43. Thank you for your attention Music Analyser Jean-Marie FROUX MSc Project City University Supervisor: D J STYLES Music Analyser - JM Froux City University - 2003

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