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Ear training or aural skills is a music theory study in which musicians learn to identify pitches, intervals, melody, chords, rhythms, solfeges, and other basic elements of music, solely by hearing. The application of this skill is analogous to taking dictation in written/spoken language. As a process, ear training is in essence the inverse of sight-reading, the latter being analogous to reading a written text aloud without prior opportunity to review the material. Ear training is typically a component of formal musical training and is a fundamental, essential skill required in music schools. Functional pitch recognition involves identifying the function or role of a single pitch in the context of an established tonic. Once a tonic has been established, each subsequent pitch may be classified without direct reference to accompanying pitches. For example, once the tonic G has been established, listeners may recognize that the pitch D plays the role of the dominant in the key of G. No reference to any other pitch is required to establish this fact. Many musicians use functional pitch recognition in order to identify, understand, and appreciate the roles and meanings of pitches within a key. To this end, scale-degree numbers or movable-do solmization (do, re, mi, etc.) can be quite helpful. Using such systems, pitches with identical functions (the key note or tonic, for example) are associated with identical labels (1 or do, for example). Functional pitch recognition is not the same as fixed-do solfège, e.g. do, re, mi, etc. Functional pitch recognition emphasizes the role of a pitch with respect to the tonic, while fixed-do solfège symbols are labels for absolute pitch values (do=C, re=D, etc., in any key). In the fixed-do system (used in the conservatories of the Romance language nations, e.g. Paris, Madrid, Rome, as well as the Juilliard School and the Curtis Institute in the USA), solfège symbols do not describe the role of pitches relative to a tonic, but rather actual pitches.
[2]Paolo Prandoni, [3]LCAV, EPFL
The Karplus-Strong algorithm is a simple method to generate synthetic
sounds with arbitrary pitch. In this notebook we will explore a few
variations of the method to illustrate some basic notions of
discrete-time signal processing.
In [1]:
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import IPython
In [2]:
plt.rcParams["figure.figsize"] [2]Paolo Prandoni, [3]LCAV, EPFL
In this notebook we will explore various ways to implement a system
that changes the voice of a speaker. Typical applications for these
techniques, strictly sorted by plausibility, include international spy
missions and prank telephone calls, and devices such as the one
displayed here can be easily bought on the web... or in a novelty shop.
No description has been provided for t [2]Paolo Prandoni, [3]LCAV, EPFL
In this notebook we will look at the practical issues associated to
plotting the DFT and in particular the DFT of real-world signals. We
will examine how to map the DFT coefficients to real-world frequencies
and we will investigate the frequency resolution of the DFT and the
effects of zero padding.
In [1]: