Similar to Figure 2, the input to the microphone y(n) can be described by (3).
where σ a 2 denotes the (long-term averaged) signal power of the signal a, xs,p denotes the target component contained in p th microphone, and n ̂ s denotes the target residual contained in the noise estimate.
Accordingly, the term (sum _{tau = T_{bot }}^{T_{top }} {boldsymbol {G}_{tau }[f]}^{H} boldsymbol {y}_{n - tau }[f]) represents the late reverberant components contained in microphone signals y n [f], and e n [f] corresponds to the mixture of clean speech signal and early reflection components.
Most importantly, this term depends only on the signals that are available within the AEC application, i.e., the microphone signal, d(n), and the output of the adaptive filter, (widehat {y}(n)).
Other ways to improve the DOA resolution is by increasing microphone spacing d N which will increase the size of the array.
The ΔfSNR and ΔCD values depicted in Fig. 5c, d show that using the conventional filter length (L^{mathrm {t}}_{g}) in MINT yields a significantly worse quality than the unprocessed microphone signal x1(n) for all NPMs.
While an increase in robustness is obtained for all NPMs using (L^{text {opt}}_{g}), for most considered NPMs, the performance in terms of ΔfSNR is still worse than for the unprocessed microphone signal x1(n).
While PMINT always improves the perceptual speech quality in comparison to the unprocessed microphone signal x1(n), RMCLS sometimes fails to yield an improvement, as indicated by the negative ΔfSNR for systems S2 and S3.
First, it can be observed that MINT using (L^{text {opt}}_{g}) results in the lowest performance in terms of all performance measures, often worsening the perceptual speech quality in comparison to the unprocessed microphone signal x1(n).
Furthermore, the ΔfSNR and ΔCD values depicted in Fig. 7c, d show that while PMINT using (L^{mathrm {t}}_{g}) worsens the perceptual speech quality in comparison to the unprocessed microphone signal x1(n), using (L^{text {opt}}_{g}) results in a significantly better performance.
Table 2 Complexity evaluation of the wireless microphone detectors for Ns samples Complexity in number of complex multiplication Energy detector Ns Teager-Kaiser detector 2*Ns Autocorrelation detector Ns*Ns Frequency domain energy detector Ns*log2(Ns) + Ns.
