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Journal ArticleDOI

Separation of speech from interfering speech by means of harmonic selection

Thomas W. Parsons
- 01 Oct 1976 - 
- Vol. 60, Iss: 4, pp 911-918
TLDR
In this paper, the harmonics of the desired voice in the Fourier transform of the input were selected to distinguish between two different voices. But the authors focus on the principal subproblem, the separation of vocalic speech.
Abstract
A common type of interference in speech transmission is that caused by the speech of a competing talker. Although the brain is adept at clarifying such speech, it relies heavily on binaural data. When voices interfere over a single channel, separation is much more difficult and intelligibility suffers. Clarifying such speech is a complex and varied problem whose nature changes with the moment‐to‐moment variation in the types of sound which interfere. This paper describes an attack on the principal subproblem, the separation of vocalic speech. Separation is done by selecting the harmonics of the desired voice in the Fourier transform of the input. In implementing this process, techniques have been developed for resolving overlapping spectrum components, for determining pitches of both talkers, and for assuring consistent separation. These techniques are described, their performance on test utterances is summarized, and the possibility of using this process as a basis for the solution of the general two‐tal...

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Dissertation

The Use of Optimal Cue Mapping to Improve the Intelligibility and Quality of Speech in Complex Binaural Sound Mixtures.

Jingbo Gao
TL;DR: In this paper, an approach to speech segregation based on optimal cue mapping (OCM) is proposed, which is a signal processing method for segregating a sound source based on spatial and other cues extracted from the binaural mixture of sounds arriving at a listener's ears.
Proceedings ArticleDOI

On Discriminative Framework for Single Channel Audio Source Separation.

TL;DR: A generic discriminative learning framework where one source is separate one at a time and the dimension search algorithm is embedded in the training of discrim inative source models to demonstrate a performance improvement in separation for both speech-speech and speechmusic mixture.