Sentence examples for statistical acoustic from inspiring English sources

State-of-the-art speech recognition systems are based on statistical acoustic models.

First, a new method named statistical acoustic energy flow (SAEF) is proposed, which can be applied to the full-spectrum HST interior noise simulation (including low, medium, and high frequencies) with only one model.

Through the subjective evaluation experiment, calculation of psychoacoustic parameters and data statistical tests, acoustic comfort evaluation model is established based on MLR.

The output of this beamformer is then processed by a single-channel spectral enhancement scheme, based on statistical room acoustics, minimum statistics, and temporal cepstrum smoothing, to suppress residual noise and reverberation.

The proposed scheme computes a real-valued spectral gain, combining the clean speech amplitude estimator presented in [22], the noise PSD estimator based on minimum statistics (MS) [13], and an estimator of the (late) reverberant PSD based on statistical room acoustics [15, 23].

This method uses a statistical room acoustics approach in contrast to the deterministic numerical approach of the older method.

In Section 2, we recall the Gaussian framework for audio source separation and we present a result of the theory of statistical room acoustics.

In this article, we propose two probabilistic priors over the source spatial covariance matrices or the subsource mixing matrices which are consistent with the theory of statistical room acoustics.

Due to the high complexity and low resilience in creating an exact model of the reverberant RIR, it is often described by means of statistical room acoustics (SRA) [15 18].

We propose two alternative probabilistic priors over the spatial covariance matrices which are consistent with the theory of statistical room acoustics and we derive expectation-maximization algorithms for maximum a posteriori (MAP) estimation.

In contrast with classical ML estimation of the spatial parameters, we proposed two priors exploiting a result from the theory of statistical room acoustics and we derived closed-form MAP updates.