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An Automated System for Sound Localization Testing in Hearing-Impaired Listeners

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Monaural speech segregation based on pitch tracking and amplitude modulation.

Guoning Hu¹, Deliang Wang

¹Biophys. Program, Ohio State Univ., Columbus, OH, USA.

IEEE Transactions on Neural Networks

|February 2, 2008

Summary

This study introduces a new method for separating voiced speech from a single audio source. The novel system effectively segregates high-frequency speech components by treating resolved and unresolved harmonics differently.

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Area of Science:

Speech processing
Auditory perception
Signal processing

Background:

Monaural speech segregation is challenging, particularly for high-frequency speech components.
Existing systems struggle with high-frequency speech due to limitations in auditory scene analysis.
Psychoacoustic evidence indicates distinct mechanisms for processing resolved and unresolved harmonics.

Purpose of the Study:

To propose a novel system for voiced speech segregation that addresses limitations in current methods.
To develop a system that segregates resolved and unresolved harmonics using different strategies.
To improve the segregation of high-frequency speech components.

Main Methods:

The proposed system segregates resolved harmonics based on temporal continuity and cross-channel correlation.
Unresolved harmonics are segregated using common amplitude modulation (AM) and temporal continuity.
A pitch contour, estimated from dominant pitch and refined by psychoacoustic constraints, guides the segregation process.

Main Results:

The novel system demonstrates substantially improved performance compared to previous methods.
Performance gains are particularly significant for the high-frequency components of speech.
The differential treatment of resolved and unresolved harmonics leads to better overall segregation.

Conclusions:

The proposed system offers a significant advancement in monaural voiced speech segregation.
Differentiating the processing of resolved and unresolved harmonics is crucial for effective speech separation.
The system shows promise for applications requiring robust speech extraction from complex acoustic environments.