This monograph presents the author's studies in music recognition aimed at developing a computer system for automatic notation of performed music. The performance of such a system is supposed to be similar to that of speech recognition systems: acoustical data at the input and music score printing at the output. The approach to pattern recognition employed is that of artificial perception, based on self-organizing input data in order to segregate patterns before their identification by artificial intelligence methods. The special merit of the approach is that it finds optimal representations of data instead of directly recognizing patterns.
Artificial Perception and Music Recognition
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First Sentence:
By analogy with speech recognition, by music recognition we understand studies in computer simulation of music perception which contribute to developing systems for automatic notation of performed music. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
chord spectrum, optimal data representation, correlative perception, unique deconvolution, tone spectra, acoustical contour, simple correlation approach, automatic notation, tempo tracking, voice spectra, tempo curve, timing accentuation, inharmonic tones, interval hearing, audio structure, salient intervals, generative group, rhythm recognition, rhythmic segmentation, pitch salience, acoustical processes, music recognition, voice separation, recognizing intervals, chord recognition Key Phrases - Capitalized Phrases (CAPs): (learn more)
Correct Correct Correct Correct, Generative Number of Notation New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
By analogy with speech recognition, by music recognition we understand studies in computer simulation of music perception which contribute to developing systems for automatic notation of performed music. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
chord spectrum, optimal data representation, correlative perception, unique deconvolution, tone spectra, acoustical contour, simple correlation approach, automatic notation, tempo tracking, voice spectra, tempo curve, timing accentuation, inharmonic tones, interval hearing, audio structure, salient intervals, generative group, rhythm recognition, rhythmic segmentation, pitch salience, acoustical processes, music recognition, voice separation, recognizing intervals, chord recognition Key Phrases - Capitalized Phrases (CAPs): (learn more)
Correct Correct Correct Correct, Generative Number of Notation New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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This book cites 44
books:
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- Artificial Perception and Music Recognition (Lecture Notes in Computer Science) by Andranick S. Tanguiane on 4 pages
- Human and Machine Vision (Notes and Reports in Computer Science and Applied Mathematics, 8) by Colo.) Conference on Human and Machine Vision (1981 : Denver in Back Matter (1), Back Matter (2), and Back Matter (3)
- Shape From Shading (Artificial Intelligence) by Berthold K. P. Horn in Back Matter (1), and Back Matter (2)
- Artificial Intelligence (3rd Edition) by Winston in Back Matter (1), and Back Matter (2)
- Progress in Artificial Intelligence: 6th Portuguese Conference on Ai, Epia '93 Porto, Portugal, October 6-8, 1993 : Proceedings by portugal Portuguese Conference on Artificial Intelligence 1993 Porto in Back Matter (1), and Back Matter (2)
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