Sentence examples for multiple microphone pairs from inspiring English sources

In the first step, the position of a source is estimated from (multiple) microphone pairs.

The second stage utilizes efficient algorithms to produce an unambiguous solution to the hyperbolic equations obtained from multiple microphone pairs.

We adapted the MCCC to the joint DOA and pitch estimation problem, in order to generate an overall 2D pattern using multiple microphone pairs.

Third, using multiple microphone pairs, the multi-channel cross-correlation approach is incorporated to improve the robustness against noise and reverberation.

An intuitive approach to combine multiple microphone pairs is the arithmetic mean of all 2D pattern, as already performed in [22].

Synchronous addition of multiple microphone pair-wise CSP coefficients reduces the noise influence [32].

As a third extension, instead of simply averaging the multiple microphone pair results, the multi-channel cross-correlation (MCCC) method, presented in [34], is adapted to the joint DOA and pitch estimator, leading to a robustness improvement especially for noisy conditions.

Therefore, we introduce a more sophisticated method based on the multi-channel cross-correlation (MCCC) [34], which exploits the redundancy among multiple microphones pairs and can be understood as the generalized multi-channel extension of the cross-correlation.

Typically, the solutions to both single and multiple source localization problems are based on estimations of the Time Difference Of Arrival (TDOA) at different microphone pairs, which are obtained by means of Generalized Cross-Correlation PHAse Transform (GCC-PHAT) [3], also known as Crosspower-Spectrum Phase (CSP) [4].

There exists unique microphone pairs in the set of all pairs.

Using a reference microphone, the phase difference information at point of microphone pairs is stored in the vector (9).