The harmonicity constraint is particularly beneficial for automatic transcription because, in this way, each basis can define a single pitch.
In this paper, each basis represents the exemplar of the speech or noise signal, and the collection of exemplar W and the weight vector h l are referred to as "dictionary" and "activity", respectively.
The number of basis functions (bf) and of free parameters (k) for a model containing j conditions and N trials are noted in brackets for each basis set.
In this framework, we assume that each basis in the dictionary represents a phoneme part and the activity matrix represents the phonetic information of the utterance, which is independent of the speaker.
In addition, the coefficients for each basis are constrained by prior information in the Bayesian approach such as Akaike's Bayesian Information Criterion (ABIC) (e.g. Ide and Takeo, 1997; Sekiguchi et al., 2000), and the constraint results in the slip distribution being smoothed.
This formulation results in the discretization of nonlinear systems with unknown coefficients in the approximate subspace of each basis level.
In our proposed MP operation, each basis particle makes multiple predictions according to the system model to track the uncertain system state.
Chennapragada tells me that all of this happens on an opt-in basis.
Clients usually visit the clinic on a "walk-in" basis.
Syringes are available anonymously on a walk-in basis.
