Iterative synchronisation and DC-offset estimation using superimposed training

In this paper, we propose a new iterative approach for superimposed training (ST) that improves synchronisation, DC-offset estimation and channel estimation. While synchronisation algorithms for ST have previously been proposed in [2],[4] and [5], due to interference from the data they performed sub-optimally, resulting in channel estimates with unknown delays. These delay ambiguities (also present in the equaliser) were estimated in previous papers in a non-practical manner. In this paper we avoid the need for estimation of this delay ambiguity by iteratively removing the effect of the data “noise”. The result is a BER performance superior to all other ST algorithms that have not assumed a-priori synchronisation

Audiovisual head orientation estimation with particle filtering in multisensor scenarios

This article presents a multimodal approach to head pose estimation of individuals in environments equipped with multiple cameras and microphones, such as SmartRooms or automatic video conferencing. Determining the individuals head orientation is the basis for many forms of more sophisticated interactions between humans and technical devices and can also be used for automatic sensor selection (camera, microphone) in communications or video surveillance systems. The use of particle filters as a unified framework for the estimation of the head orientation for both monomodal and multimodal cases is proposed. In video, we estimate head orientation from color information by exploiting spatial redundancy among cameras. Audio information is processed to estimate the direction of the voice produced by a speaker making use of the directivity characteristics of the head radiation pattern. Furthermore, two different particle filter multimodal information fusion schemes for combining the audio and video streams are analyzed in terms of accuracy and robustness. In the first one, fusion is performed at a decision level by combining each monomodal head pose estimation, while the second one uses a joint estimation system combining information at data level. Experimental results conducted over the CLEAR 2006 evaluation database are reported and the comparison of the proposed multimodal head pose estimation algorithms with the reference monomodal approaches proves the effectiveness of the proposed approach.Postprint (published version

Probabilistic Latent Variable Models as Nonnegative Factorizations

This paper presents a family of probabilistic latent variable models that can be used for analysis of nonnegative data. We show that there are strong ties between nonnegative matrix factorization and this family, and provide some straightforward extensions which can help in dealing with shift invariances, higher-order decompositions and sparsity constraints. We argue through these extensions that the use of this approach allows for rapid development of complex statistical models for analyzing nonnegative data

Speech Enhancement via EMD

WOSInternational audienceIn this study, two new approaches for speech signal noise reduction based on the empirical mode decomposition (EMD) recently introduced by Huang et al. (1998) are proposed. Based on the EMD, both reduction schemes are fully data-driven approaches. Noisy signal is decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs), using a temporal decomposition called sifting process. Two strategies for noise reduction are proposed: filtering and thresholding. The basic principle of these two methods is the signal reconstruction with IMFs previously filtered, using the minimum mean-squared error (MMSE) filter introduced by I. Y. Soon et al. (1998), or thresholded using a shrinkage function. The performance of these methods is analyzed and compared with those of the MMSE filter and wavelet shrinkage. The study is limited to signals corrupted by additive white Gaussian noise. The obtained results show that the proposed denoising schemes perform better than the MMSE filter and wavelet approach

DWT-DCT-Based Data Hiding for Speech Bandwidth Extension

The limited narrowband frequency range, about 300-3400Hz, used in telephone network channels results in less intelligible and poor-quality telephony speech. To address this drawback, a novel robust speech bandwidth extension using Discrete Wavelet Transform- Discrete Cosine Transform Based Data Hiding (DWTDCTBDH) is proposed. In this technique, the missing speech information is embedded in the narrowband speech signal. The embedded missing speech information is recovered steadily at the receiver end to generate a wideband speech of considerably better quality. The robustness of the proposed method to quantization and channel noises is confirmed by the mean square error test. The enhancement in the quality of reconstructed wideband speech of the proposed method over conventional methods is reasserted by subjective listening and objective tests

Spectral analysis for nonstationary audio

A new approach for the analysis of nonstationary signals is proposed, with a focus on audio applications. Following earlier contributions, nonstationarity is modeled via stationarity-breaking operators acting on Gaussian stationary random signals. The focus is on time warping and amplitude modulation, and an approximate maximum-likelihood approach based on suitable approximations in the wavelet transform domain is developed. This paper provides theoretical analysis of the approximations, and introduces JEFAS, a corresponding estimation algorithm. The latter is tested and validated on synthetic as well as real audio signal.Comment: IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, In pres

Extended Nonnegative Tensor Factorisation Models for Musical Sound Source Separation

Recently, shift-invariant tensor factorisation algorithms have been proposed for the purposes of sound source separation of pitched musical instruments. However, in practice, existing algorithms require the use of log-frequency spectrograms to allow shift invariance in frequency which causes problems when attempting to resynthesise the separated sources. Further, it is difficult to impose harmonicity constraints on the recovered basis functions. This paper proposes a new additive synthesis-based approach which allows the use of linear-frequency spectrograms as well as imposing strict harmonic constraints, resulting in an improved model. Further, these additional constraints allow the addition of a source filter model to the factorisation framework, and an extended model which is capable of separating mixtures of pitched and percussive instruments simultaneously