By Bozena Kostek
This monograph offers novel insights into cognitive mechanisms underlying the processing of sound and song in several environments. an outstanding figuring out of those mechanisms is essential for various technological purposes similar to for instance details retrieval from disbursed musical databases or construction professional platforms. that allows you to examine the cognitive mechanisms of track notion basics of listening to psychophysiology and ideas of song conception are offered. furthermore, a few computational intelligence tools are reviewed, comparable to tough units, fuzzy common sense, man made neural networks, selection bushes and genetic algorithms. The functions of hybrid determination structures to challenge fixing in track and acoustics are exemplified and mentioned at the foundation of received experimental effects.
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Extra resources for Perception-based data processing in acoustics: applications to music information retrieval and psychophysiology of hearing
For example, Byrd and Crawford (2001) claim that the most informative are melody and rhythm, assigning about 50% of informativeness to a melody, 40% to rhythm and remaining 10% to the rest of such elements as harmony, dynamics, agogics, articulation, etc. (Byrd and Crawford 2002). Therefore, rhythm may be treated as one of the most fundamental components of music. The appearance of rhythm seems to be the first step in the evolution of a musical culture. In the days of ancient Greece and Rome, rhythm – tempo, measures and note duration – were defined by the kind of rhythmical recitation.
Cognitive mapping of music into space and motion is very complex. Such finding may apply to models of pitch space, such as Larson’s (Larson 1997), Schenker’s, and others. It is worth reviewing the issue of the IEEE Proceedings dealing with subjects on Engineering and Music – Supervisory Control and Auditory Communication. Especially valuable may be papers by Johannsen (2004), Canazza et al. (2004), Suzuki and Hashimoto (2004), and others of the same issue (Johannsen 2004). The domain of “Human Supervision and Control”, as suggested by Johannsen, can be analyzed in the engineering sciences and in musicology from different cultural, social, and intellectual perspectives.
The analyses shown below were performed using the autocorrelation method. a. b. c. Fig. 1. Spectrum of C6 violin (a), piano (b), and flute (c) sounds, autocorrelation method p = 28 (AR model), N = 128 As seen from analyses the method presented accurately estimates the pitch of the analyzed sounds. Although the first harmonic is clearly in evidence in all plots, it can be seen that the autocorrelation method reveals fewer peaks than it is expected; higher spectrum partials of less energy are often disregarded and are not shown in the analysis.
Perception-based data processing in acoustics: applications to music information retrieval and psychophysiology of hearing by Bozena Kostek