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Learning an appropriate distance metric using the available class labels or some other supervisory information is a very active research area. It has been shown that the metric learning based methods outperforms the traditionally used distance metrics such as the Euclidean distance metric. In kernelized version of metric learning algorithms, the data is implicitly transferred into a new feature space...
Classifying an unknown object in image retrieval systems using the nearest neighbour classifier would be very time consuming when the number of the objects within the associated database is high. Generating a dendrogram using a Hierarchical Agglomerative Clustering (HAC) algorithm and searching the database images from coarse to fine resolutions using image pyramids are two important groups of techniques...
Using data communication networks for telephony services has evolved the telecommunication industry. The basic requirement in this field is achieving confidence about the quality of the telephony service. In this paper, a practical approach based on the quantitative and qualitative analysis is presented in order to evaluate the readiness of an Ethernet network for telephony service. The aim is to...
Adaptive noise cancellation (ANC) is a well-known technique for background noise reduction in automobile and vehicular environments. The noise fields in automobile and other vehicle interior obey the diffuse noise field model closely. On the other hand, the ANC does not provide sufficient noise reduction in the diffuse noise fields. In this paper, a new multistage post-filter is designed for ANC as...
In this paper speech-music separation using blind source separation is discussed. The separating algorithm is in the time domain and based on the mutual information minimization. Also the natural gradient algorithm is used for its minimization. In order to do that, score function estimation from observation signal samples is needed. The accuracy and the speed of the mentioned estimation will affect...
This paper proposes a new robust adaptive beamformer applicable to microphone arrays. The proposed beamformer is a Generalized Sidelobe Canceller (GSC) with a single-channel noise reduction stage. The single-channel stage can be either Optimally Modified Log-Spectral Amplitude (OMLSA) estimator or Adaptive Minimum Mean-Square (AMMSE) spectral amplitude estimator. These hybrid structures, named GSC-OMLSA/...
In this paper, we propose a new algorithm for automatic transcription of music signals. This algorithm consists of two main stages: the first stage eliminates the harmonics of music signal and only passes the fundamental part. This will extremely simplify the modeling of music signal. The second stage estimates and tracks the fundamental frequency of music signal by means of an Extended Kalman Filter...
In this paper, we have considered a frequency tracking method based on Extended Kalman Filtering (EKF). The method uses a state space model to estimate and track the frequency of a harmonic signal embedded in broad-band noise. This signal is generally characterized by time-varying frequency and amplitude (nonstationary noisy harmonic signal). In this paper we have proposed a new state-space model...
This paper deals with the problem of Adaptive Noise Cancellation (ANC) for the speech signal corrupted with an additive white Gaussian noise. After explaining the least Mean Square (LMS)-based adaptive filter and Kalman filter, we examine the hybrid Kalman-based LMS (KLMS) technique for adaptation of the ANC. The proposed technique suggests a way to normalize LMS algorithm using Kalman filter. Our...
This paper addresses the problem of intelligibility and quality improvement for the speech corrupted due to reverberation. Several speech de-reverberation algorithms (both single- and two-channel methods) are studied and compared. Based on this study, we propose some hybrid methods to improve both intelligibility and quality of reverberated speech. The performance of the techniques are examined and...
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