Convolutive mixtures of signals, which are common in acoustic environments, can be difficult to separate into their component sources. Here we present a uniform probabilistic framework to separate convolutive mixtures of acoustic signals using independent vector analysis (IVA), which is based on a joint distribution for the frequency components originating from the same source and is capable of preventing permutation disorder. Different gaussian mixture models (GMM) served as source priors, in contrast to the original IVA model, where all sources were modeled by identical multivariate Laplacian distributions. This flexible source prior enabled the IVA model to separate different type of signals. Three classes of models were derived and tested: noiseless IVA, online IVA, and noisy IVA. In the IVA model without sensor noise, the unmixing matrices were efficiently estimated by the expectation maximization (EM) algorithm. An online EM algorithm was derived for the online IVA algorithm to track the movement of the sources and separate them under nonstationary conditions. The noisy IVA model included the sensor noise and combined denoising with separation. An EM algorithm was developed that found the model parameters and separated the sources simultaneously. These algorithms were applied to separate mixtures of speech and music. Performance as measured by the signal-to-interference ratio (SIR) was substantial for all three models.

This paper presents a novel probabilistic approach to speech enhancement. Instead of a deterministic logarithmic relationship, we assume a probabilistic relationship between the frequency coefficients and the log-spectra. The speech model in the log-spectral domain is a Gaussian mixture model (GMM). The frequency coefficients obey a zero-mean Gaussian whose covariance equals to the exponential of the log-spectra. This results in a Gaussian scale mixture model (GSMM) for the speech signal in the frequency domain, since the log-spectra can be regarded as scaling factors. The probabilistic relation between frequency coefficients and log-spectra allows these to be treated as two random variables, both to be estimated from the noisy signals. Expectation-maximization (EM) was used to train the GSMM and Bayesian inference was used to compute the posterior signal distribution. Because exact inference of this full probabilistic model is computationally intractable, we developed two approaches to enhance the efficiency: the Laplace method and a variational approximation. The proposed methods were applied to enhance speech corrupted by Gaussian noise and speech-shaped noise (SSN). For both approximations, signals reconstructed from the estimated frequency coefficients provided higher signal-to-noise ratio (SNR) and those reconstructed from the estimated log-spectra produced lower word recognition error rate because the log-spectra fit the inputs to the recognizer better. Our algorithms effectively reduced the SSN, which algorithms based on spectral analysis were not able to suppress.

This paper presents a new approximate Bayesian estimator for enhancing a noisy speech signal. The speech model is assumed to be a Gaussian mixture model (GMM) in the log-spectral domain. This is in contrast to most current models in frequency domain. Exact signal estimation is a computationally intractable problem. We derive three approximations to enhance the efficiency of signal estimation. The Gaussian approximation transforms the log-spectral domain GMM into the frequency domain using minimal Kullback-Leiber (KL)-divergency criterion. The frequency domain Laplace method computes the maximum a posteriori (MAP) estimator for the spectral amplitude. Correspondingly, the log-spectral domain Laplace method computes the MAP estimator for the log-spectral amplitude. Further, the gain and noise spectrum adaptation are implemented using the expectation-maximization (EM) algorithm within the GMM under Gaussian approximation. The proposed algorithms are evaluated by applying them to enhance the speeches corrupted by the speech-shaped noise (SSN). The experimental results demonstrate that the proposed algorithms offer improved signal-to-noise ratio, lower word recognition error rate, and less spectral distortion.

This paper presents a novel adaptive approach to the separation of convolutedly mixed acoustic signals based on independent vector analysis (IVA). IVA, as an extension of independent component analysis (ICA) from univariate components to multivariate components, provides an efficient framework for avoiding the well-known permutation problem in frequency-domain blind source separation (BSS). However, since IVA has been mostly employing pre-specified and simple source priors which are good fits to speech signals, the performance degrades when the mixture includes unknown sources other than speech. Also, sensor noise has not been considered. To tackle these limitations, we employ multivariate Gaussian mixture model (GMM) as the source priors and add sensor noise into the model. We derive an expectation maximization (EM) algorithm that estimates the separating matrices and the parameters of the unknown source prior together. The performance is demonstrated by experimental results that include the comparison with the IVA results using fixed source priors.

For emotion recognition, we selected pitch, log energy, formant, mel-band energies, and mel frequency cepstral co-efficients (MFCCs) as the base features, and added veloc-ity/acceleration of pitch and MFCCs to form feature streams. We extracted statistics used for discriminative classifiers, as-suming that each stream is a one-dimensional signal. Extracted features were analyzed by using quadratic discriminant analy-sis (QDA) and support vector machine (SVM). Experimental results showed that pitch and energy were the most important factors. Using two different kinds of databases, we compared emotion recognition performance of various classifiers: SVM, linear discriminant analysis (LDA), QDA and hidden Markov model (HMM). With the text-independent SUSAS database, we achieved the best accuracy of 96.3 % for stressed/neutral style classification and 70.1 % for 4-class speaking style clas-sification using Gaussian SVM, which is superior to the previ-ous results. With the speaker-independent AIBO database, we achieved 42.3 % accuracy for 5-class emotion recognition. 1.