Criteria To Measure The Quality Of Tvar Estimation For Audio Signals (original) (raw)
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International Journal of Signal Processing, Image Processing and Pattern Recognition, 2014
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Performance Analysis of Basis Functions in TVAR Model
International Journal of Signal Processing, Image Processing and Pattern Recognition, 2014
In this paper Time-varying Auto regressive model (TVAR) based approach for instantaneous frequency (IF) estimation of the nonstationary signal is presented. Timevarying parameters are expressed as a linear combination of constants multiplied by basis functions. Then, the time-varying frequencies are extracted from the time-varying parameters by calculating the angles of the estimation error filter polynomial roots. Since there were many existing basis functions that could be used as basis for the TVAR parameter expansion, one might be interested in knowing how to choose them and what difference they may cause. The performance of different basis functions in TVAR modeling approach is tested with synthetic signals. Our objective is to find an efficient basis for all testing signals in the sense that, for a small number of basis (or) expansion dimension, the basis yields the least error in frequency. In this paper, the optimal basis function of TVAR Model for the instantaneous frequency (IF) estimation of the test signals was obtained by comparing IF estimation precise and anti-noise performance of several types basis functions through simulation.
A New Strategy for Objective Estimation of the Quality of Audio Signals
IEEE Latin America Transactions, 2004
This paper presents a new strategy for the objective assessment of audio signal quality. The resulting method, named Objective Measure of Audio Quality (Medida Objetiva da Qualidade de Audio-MOQA), includes some of the most successful features present in the current audio assessment methods, as well as new techniques resulting from the identification and study of the main limitations of such features. The performance of the new strategy is compared to that one achieved by PEAQ (Perceptual Evaluation of Audio Quality), currently adopted as a standard by International Telecommunication Union (ITU).
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A New Strategy for Objective Estimation of Audio Signals Quality
IEEE Latin America Transactions, 2004
This paper presents a new strategy for the objective assessment of audio signal quality. The resulting method, named Objective Measure of Audio Quality (Medida Objetiva da Qualidade de Audio -MOQA), includes some of the most successful features present in the current audio assessment methods, as well as new techniques resulting from the identification and study of the main limitations of such features. The performance of the new strategy is compared to that one achieved by PEAQ (Perceptual Evaluation of Audio Quality), currently adopted as a standard by International Telecommunication Union (ITU).
Improving the Detection Efficiency of the VMR-WB VAD Algorithm on Music Signals
Speech codecs are usually equipped with voice activity de-tection (VAD) algorithm to enable efficient coding of inac-tive frames and the discontinuous transmission mode (DTX). High VAD efficiency for speech in noisy environments is often traded off against its robustness for music. This is also the case of the VMR-WB codec recently standardized by 3GPP2. Its VAD fails to detect portions of some critical mu-sic samples. In this contribution we propose a method to improve the performance of the VMR-WB VAD on music signals. The idea is to measure the stability of tones in the spectral domain by means of per-tone correlation analysis. By using this approach, the music detection accuracy is in-creased to ~99% and the problem of misclassification is significantly reduced. The proposed method has been im-plemented in the G.718 codec being currently standardized by the ITU-T.
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We study the estimation of time difference of arrival (TDOA) under noisy and reverberant conditions. Conventional TDOA estimation methods such as MUltiple SIgnal Classification (MUSIC) are not robust to noise and reverberation due to the distortion in the spatial covariance matrix (SCM). To address this issue, this paper proposes a robust SCM estimation method, called weighted SCM (WSCM). In the WSCM estimation, each time-frequency (TF) bin of the input signal is weighted by a TF mask which is 0 for non-speech TF bins and 1 for speech TF bins in ideal case. In practice, the TF mask takes values between 0 and 1 that are predicted by a long short term memory (LSTM) network trained from a large amount of simulated noisy and reverberant data. The use of mask weights significantly reduces the contribution of low SNR TF bins to the SCM estimation, hence improves the robustness of MUSIC. Experimental results on both simulated and real data show that we have significantly improved the robustness of MUSIC by using the weighted SCM.