Where do your hands point in a clap catch? I always enjoy finding new questions to ask, particularly when I find that experienced coaches disagree but there’s never been much discussion of it (that I’ve seen). Apart from good performance, advantages of HFA compared to state of the art dereverberation approaches are real time processing (no adaptation time) and robustness against changes of the room impulse response.Here’s something I never really thought about until after I started writing this series of articles. ![]() Evaluation results show significant improvement of the recognition performance over a wide range of reverberant conditions while using HFA in connection with reverberant training. HFA works on the basis of fundamental frequency estimation and voiced/unvoiced decision. (iii) high frequency regions are not affected by HFA since they have negligible effect on the recognition rate. (ii) suppression of disturbing reverberation in unvoiced spectra coming from previous voiced sections. The method is initially named Harmonicity based Feature Analysis (HFA) and implements the following three ideas: (i) reconstruction of a spectrum from the harmonic components (assumed to be undistorted) of a voiced speech spectrum. ![]() This article proposes a new signal analysis method for automatic speech recognition designed to aim high robustness against distortions caused by room reverberation. Because of its adaptive nature,īayesian learning is shown to serve as a unified approach for a wide Prior density estimation issues are discussedįor two classes of applications-parameter smoothing and modelĪdaptation-and some experimental results are given illustrating the Likelihood estimation algorithms, namely, the forward-backward algorithmĪnd the segmental k-means algorithm, are expanded, and MAP estimationįormulas are developed. Product of Dirichlet and normal-Wishart densities. State observation densities as an example, it is assumed that the priorĭensities for the HMM parameters can be adequately represented as a Specification of the parameters of prior densities, and the evaluation Of MAP estimation, namely, the choice of prior distribution family, the In this paper, a framework for maximum a posteriori (MAP)Įstimation of hidden Markov models (HMM) is presented. Finally, practical steps for measurements with a handclap as an acoustic source are suggested. Other acoustic parameters (Early Decay Time, Clarity) were measured with greater deviations for reasons discussed in the text. Reverberation time was measured across different spaces and positions with a deviation less than 3 JND (just noticeable difference) for signal to noise ratio within or near ISO 3382-1 limits for each corresponding octave band. ![]() This configuration produced usable acoustic parameter measurements in the low frequency range in common room background levels unlike other configurations. Results indicate that the optimal hand configuration (among 11) is with the hands cupped and held at an angle due to the superior low frequency spectrum. All measurements performed with a handclap and a dodecahedron speaker for comparison. For this purpose the following steps were performed: investigation of the optimal hand configuration for acoustic measurements, measurements at different microphone-source distances and at different spaces and positions. ![]() This study sets to explore its optimal application and limitations for acoustic measurements as well for other possible utilizations. Handclap is a convenient and useful acoustic source.
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