Voice Leading

(redirected from Auditory streaming)
The following article is from The Great Soviet Encyclopedia (1979). It might be outdated or ideologically biased.

Voice Leading


the principles governing the progression (or development) of the various voice parts in multiphonic music. There are three types: polyphonic, harmonic, and heterophonic.

In polyphonic voice leading, each voice has a pronounced melodic invididuality and an importance equal to that of the others. In harmonic voice leading, one voice, usually the upper, is given the lead, with the others subordinated to it and serving as accompaniment; thus the leading voice stands out to the fullest and is provided with a definite harmonic structure. In heterophonic voice leading, the voices represent modified versions of one and the same melody.

In a narrower sense, there is vertical voice leading (movement of the voices in one direction, up or down), opposite (movement of one voice up, the other down), and oblique (some voices move, while the others remain unchanging). Parallel voice leading (vertical movement of the voices at the same interval) is a variant of vertical movement. The term “voice leading” is also employed in vocal pedagogy (for the progression of the vocalist’s line).

The Great Soviet Encyclopedia, 3rd Edition (1970-1979). © 2010 The Gale Group, Inc. All rights reserved.
References in periodicals archive ?
These patients are also impaired in voice identity recognition (39), and perform worse than non-hallucinators and controls for pitch discrimination of unmodulated tones and auditory streaming (40).
These spatial cues and spectral data are used for auditory streaming and contribute to improvement in speech perception.
Despite the obvious importance of this stage and the plethora of papers published each year on the topics of selective attention, auditory streaming, object formation, and spatial hearing, the inclusion of people with hearing loss and who wear hearing aids remains relatively slim.
What is more, it illustrates how research aimed at exploring one variable in isolation (e.g., neural mechanisms underlying auditory streaming) falls short of understanding the many interactive stages that are involved in auditory streaming in a person who wears a hearing aid.
investigate the effect of observer intention on the reported percepts for auditory streaming and visual plaids, and propose that auditory streaming can be considered as an instance of perceptual bistability.
Acoustic parameters of the sound do not in themselves allow disruption to be predicted; rather, they need to be augmented by reference to the rules of auditory streaming. Necessarily, these rules are approximate (but see Beauvois & Meddis, 1996, for a possible computational implementation).
Organisational factors in selective attention: The interplay of acoustic distinctiveness and auditory streaming in the irrelevant sound effect.
In addition to further reinforcing the idea that the content of the irrelevant stream is not the important factor (since the different degrees of disruption are produced by two circumstances in which the phonological composition of the irrelevant material is, overall, identical), this finding points to the pivotal role of auditory streaming in modulating the effect of irrelevant sound.
At the same time it has become clear, through the analysis of auditory streaming phenomena, that the organization of unattended sound bears a striking resemblance to that of attended sound.
Although this mode of presentation might have some utility for conveying similarity in functional relationships between two or more variables and time, such as phase relationships in periodic variation for economic or EEG data (Mayer-Kress, 1995), this method introduces problems with divided attention and auditory streaming. In our laboratory, informal pilot experimentation with this method of presentation suggested that it would not be particularly effective for conveying simple magnitude and direction of correlation between two sets of observations, nor would it effectively reveal other features important to exploratory data analysis such as nonlinearity and presence of outliers.