In this paper, we derive a probability model for interaural phase differences at individual spectrogram points. Such a model can combine observations across arbitrary time and frequency regions in a structured way and does not make any assumptions about the characteristics of the sound sources. In experiments with speech from twenty speakers in simulated reverberant environments, this probabilistic method predicted the correct interaural delay of a signal more accurately than generalized cross-correlation methods
Although extensive research has been done in the field of machine-based localization, the degrading ...
Although extensive research has been done in the field of machine-based localization, the degrading ...
Although extensive research has been done in the field of machine-based localization, the degrading ...
A robust acoustic localization model will be presented, which is based on the supervised learning of...
We describe a system for localizing and separating multiple sound sources from a reverberant two-cha...
We present a method for localizing and separating sound sources in stereo recordings that is robust ...
A robust acoustic localization model will be presented, which is based on the supervised learning of...
A robust acoustic localization model will be presented, which is based on the supervised learning of...
A robust acoustic localization model will be presented, which is based on the supervised learning of...
Although extensive research has been done in the field of localization, the degrading effect of reve...
Although extensive research has been done in the field of localization, the degrading effect of reve...
Although extensive research has been done in the field of machine-based localization, the degrading ...
Although extensive research has been done in the field of machine-based localization, the degrading ...
Although extensive research has been done in the field of machine-based localization, the degrading ...
In this paper we present a system for localization and separation of multiple speech sources using p...
Although extensive research has been done in the field of machine-based localization, the degrading ...
Although extensive research has been done in the field of machine-based localization, the degrading ...
Although extensive research has been done in the field of machine-based localization, the degrading ...
A robust acoustic localization model will be presented, which is based on the supervised learning of...
We describe a system for localizing and separating multiple sound sources from a reverberant two-cha...
We present a method for localizing and separating sound sources in stereo recordings that is robust ...
A robust acoustic localization model will be presented, which is based on the supervised learning of...
A robust acoustic localization model will be presented, which is based on the supervised learning of...
A robust acoustic localization model will be presented, which is based on the supervised learning of...
Although extensive research has been done in the field of localization, the degrading effect of reve...
Although extensive research has been done in the field of localization, the degrading effect of reve...
Although extensive research has been done in the field of machine-based localization, the degrading ...
Although extensive research has been done in the field of machine-based localization, the degrading ...
Although extensive research has been done in the field of machine-based localization, the degrading ...
In this paper we present a system for localization and separation of multiple speech sources using p...
Although extensive research has been done in the field of machine-based localization, the degrading ...
Although extensive research has been done in the field of machine-based localization, the degrading ...
Although extensive research has been done in the field of machine-based localization, the degrading ...