Download Artificial Perception and Music Recognition by Andranick S. Tanguiane PDF

By Andranick S. Tanguiane

ISBN-10: 3540573941

ISBN-13: 9783540573944

This monograph offers the author's reports in song acceptance aimed toward constructing a working laptop or computer procedure for computerized notation of played track. The functionality of one of these procedure is meant to be just like that of speech reputation platforms: acoustical information on the enter and tune scoreprinting on the output. The method of trend acceptance hired is thatof synthetic notion, in keeping with self-organizing enter info with the intention to segregate styles earlier than their identity by way of synthetic intelligencemethods. The precise benefit of the technique is that it unearths optimum representations of information rather than without delay spotting patterns.

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Example text

One can easily see that the duality of contours and trajectories in audio is the same as that in vision (Palmer 1983). On the other hand, the approach to recognizing the structure of chords by finding similar constituents in their spectra meets Witkin & Tenenbaum's (1983b) idea that the visual structure should be based on identity of some elements. Note that both a polyphonic part and a chord are definied in terms of translations of a voice spectral pattern with no reference to the concept of pitch.

The recognition of chords is based on the same principles as the recognition of contours from texture. The "common fate" principle together with the simplicity principle are applied to the recognition of rhythm and tempo. The task is understood as finding an optimal representation of time events in terms of generative rhythmic patterns and their transformations associated with the tempo curve. 5. CONTRIBUTION TO MUSIC RECOGNITION 23 of data enables overcoming the interdependence between rhythm and tempo.

For example, in Fig. 1 one can see a collection of pixels (stimuli) which form symbols A (low-level patterns) which in turn form a contour of symbol B (high-level pattern). Obviously, instead of storing all the pixels, it is more efficient to store their configuration for one symbol A and then to store the contour of B. Another important property of such a representation is that high-level patterns are stable with respect to changes of low-level patterns. For example, the substitution of Z's for A's in Fig.

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Artificial Perception and Music Recognition by Andranick S. Tanguiane


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