4,767 research outputs found

    Rehaussement du signal de parole par EMD et opérateur de Teager-Kaiser

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    The authors would like to thank Professor Mohamed Bahoura from Universite de Quebec a Rimouski for fruitful discussions on time adaptive thresholdingIn this paper a speech denoising strategy based on time adaptive thresholding of intrinsic modes functions (IMFs) of the signal, extracted by empirical mode decomposition (EMD), is introduced. The denoised signal is reconstructed by the superposition of its adaptive thresholded IMFs. Adaptive thresholds are estimated using the Teager–Kaiser energy operator (TKEO) of signal IMFs. More precisely, TKEO identifies the type of frame by expanding differences between speech and non-speech frames in each IMF. Based on the EMD, the proposed speech denoising scheme is a fully data-driven approach. The method is tested on speech signals with different noise levels and the results are compared to EMD-shrinkage and wavelet transform (WT) coupled with TKEO. Speech enhancement performance is evaluated using output signal to noise ratio (SNR) and perceptual evaluation of speech quality (PESQ) measure. Based on the analyzed speech signals, the proposed enhancement scheme performs better than WT-TKEO and EMD-shrinkage approaches in terms of output SNR and PESQ. The noise is greatly reduced using time-adaptive thresholding than universal thresholding. The study is limited to signals corrupted by additive white Gaussian noise

    Hybrid Multiresolution Analysis Of ‘Punch’ In Musical Signals

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    This paper presents a hybrid multi-resolution technique for the extraction and measurement of attributes contained within a musical signal. Decomposing music into simpler percussive, harmonic and noise components is useful when detailed extraction of signal attributes is required. The key parameter of interest in this paper is that of punch. A methodology is explored that decomposes the musical signal using a critically sampled constant-Q filterbank of quadrature mirror filters (QMF) before adaptive windowed short term Fourier transforms (STFT). The proposed hybrid method offers accuracy in both the time and frequency domains. Following the decomposition transform process, attributes are analyzed. It is shown that analysis of these components may yield parameters that would be of use in both mixing/mastering and also audio transcription and retrieval

    Effective Binaural Multi-Channel Processing Algorithm for Improved Environmental Presence

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    Binaural noise-reduction algorithms based on multi-channel Wiener filter (MWF) are promising techniques to be used in binaural assistive listening devices. The real-time implementation of the existing binaural MWF methods, however, involves challenges to increase the amount of noise reduction without imposing speech distortion, and at the same time preserving the binaural cues of both speech and noise components. Although significant efforts have been made in the literature, most developed methods so far have focused only on either the former or latter problem. This paper proposes an alternative binaural MWF algorithm that incorporates the non-stationarity of the signal components into the framework. The main objective is to design an algorithm that would be able to select the sources that are present in the environment. To achieve this, a modified speech presence probability (SPP) and a single-channel speech enhancement algorithm are utilized in the formulation. The resulting optimal filter also avoids the poor estimation of the second-order clean speech statistics, which is normally done by simple subtraction. Theoretical analysis and performance evaluation using realistic recorded data shows the advantage of the proposed method over the reference MWF solution in terms of the binaural cues preservation, as well as the noise reduction and speech distortion

    HMM-Based Speech Synthesis Utilizing Glottal Inverse Filtering

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