Multiple pitch estimation consists of inferring the fundamental frequencies and the salience of the notes forming a music signal over short time frames. This mid-level representation can be exploited as a front-end for higher-level applications, such as music-to-score transcription or chord detection. One approach is to decompose the short-term magnitude spectrum of the signal into a sum of bas...

This paper proposes two methods for robust automatic speech recognition (ASR) in reverberant environments. Unlike other methods which mostly apply inverse filtering by blindly estimated room impulse responses to achieve dereverberation, the proposed methods are based on the utilization of the characteristics of speech. The first method Harmonicity based Feature Analysis – takes advantage of the...

Two experiments investigated listeners' ability to use a difference of two semitones in fundamental frequency (F0) to segregate a target voice from harmonic complex tones, with speech-like spectral profiles. Masker partials were in random phase (experiment 1) or in sine phase (experiment 2) and stimuli were presented over headphones. Target's and masker's harmonicity were each distorted by F0 m...

In this article, we investigate anti-invariant Riemannian and Lagrangian submersions onto manifolds from the Lorentzian para-Sasakian manifold. We demonstrate that, for these submersions, horizontal distributions are not integrable their fibers totally geodesic. As a result, they geodesic maps. The harmonicity of such is also examined. specifically prove that harmonic when Reeb vector field hor...

Despite the use o f noise robustness techniques, automatic speech recognition (ASR) systems make many more recognition errors than humans, especially in very noisy circumstances. We argue that this inferior recognition performance is largely due to the fact that in ASR speech is typically processed on a frameby-frame basis preventing the redundancy in the speech signal to be optimally exploited...

In this paper, we consider the harmonic extension problem, which is widely used in many applications of machine learning. We find that the transitional method of graph Laplacian fails to produce a good approximation of the classical harmonic function. To tackle this problem, we propose a new method called the point integral method (PIM). We consider the harmonic extension problem from the point...