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Internet Resources Discovery (IRD)

Internet Resources Discovery (IRD). Music IR. Music IR. MELDEX - The New Zealand Digital Library MELody inDEX - Musical IR stages Reminder - sound basics. Musical Information Retrieval Stages. 3.Retrieving Tunes. 1. Melody Transcription: Conversion of sound to coded representation.

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Internet Resources Discovery (IRD)

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  1. Internet Resources Discovery (IRD) Music IR T.Sharon

  2. Music IR • MELDEX - The New Zealand Digital Library MELody inDEX - Musical IR stages • Reminder - sound basics T.Sharon

  3. Musical Information Retrieval Stages 3.Retrieving Tunes 1. Melody Transcription: Conversion of sound to coded representation 2. Searching Musical Databases: pattern matching T.Sharon

  4. Sound Basics: Amplitude, Frequency Air Pressure + Amplitude Time - One Period T.Sharon

  5. Melody Transcription • Pitch tracking and note segmentation • Pitch representation • Adapting to the user’s tuning CDAC... T.Sharon

  6. Preparation • Convert to standard 22kHz • Quantization to 8bit • Low pass filter T.Sharon

  7. Pitch tracking and note segmentation (1) • Segmentation - Determine start and end of consonant • Standard • Adaptive • Pitch tracksegmentation Typical ‘ah’ waveform T.Sharon

  8. Pitch tracking and note segmentation (2) • Amplitude segmentation T.Sharon

  9. Rhythm value assignment • By quantizing each note to the minimal duration note closer determined by user. • User must specify: • Metronome speed • Minimum note • Minimum rest duration • Can use defaults. T.Sharon

  10. Pitch Representation • In western music - transcript each note identified to closest semitone. • Represent each note as distance (cents) from MIDI 0 (8.176Hz). T.Sharon

  11. Problems Searching musical databases • Folk songs have many variations: • Classical music is more liable to source. • Inaccurate performance: • Users don’t remember songs well. • Users don’t sing well. • Where to start? • Users usually start from song begin. • Commercial songs have “hook” at the chorus, users remember them. T.Sharon

  12. Searching Musical Databases • Using pattern matching. • Need to approximate string matching. • Need fast algorithm. T.Sharon

  13. Adapting Search criteria • Ignore key: • search only according to pitch ratios. • use musical intervals. • Interval direction is an important factor -“melodic contour” or “pitch profile”: • * represents the first note • D Descending • U Ascending • R Repetition T.Sharon

  14. Approximate string matching for music • Search minimal edit distance: • Delete • Insert • Substitute • Can use different “costs” or “weights” to operations or symbols. • Two more music-related operations: Consolidation and fragmentation. ADEAAA ABAACDEAAAD T.Sharon

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