The principles of human sound localization imply binaural (interaural level and time difference) as well as monaural cues. The latter are captured by the head-related transfer functions (HRTFs), which describe the direction-dependent, spectral shaping of the incident sound wave, and can be exploited to determine the direction. In this paper an accurate talker localization strategy in the horizontal plane using the signal of only one microphone is presented. The sound localization method is developed based on a set of HRTF measurements taken from a dummy head and a statistical model of speech. High-dimensional spectral features (STFT coefficients) are taken and the direction of the sound source is evaluated with Gaussian mixture models (GMMs) using a maximum likelihood (ML) framework. An evaluation of the developed method in a synthetic test environment yields excellent localization results and leads to a promising approach which can be further investigated in future research.

In this paper, we investigate the source-filter-based approach for single-channel speech separation. We incorporate source-driven aspects by multi-pitch estimation in the model-driven method. For multi-pitch estimation, the factorial HMM is utilized. For modeling the vocal tract filters either vector quantization (VQ) or non-negative matrix factorization are considered. For both methods, the final combination of the source and filter model results in an utterance dependent model that finally enables speaker independent source separation. The contributions of the paper are the multi-pitch tracker, the gain estimation for the VQ based method which accounts for different mixing levels, and a fast approximation for the likelihood computation. Additionally, a linear relationship between pitch tracking performance and speech separation performance is shown.

We present a simple and efficient feature modeling approach for tracking the pitch of two simultaneously active speakers. We model the spectrogram features of single speakers using Gaussian mixture models in combination with the minimum description length model selection criterion. To obtain a probabilistic representation for the speech mixture spectrogram features of both speakers, we employ the mixture maximization model (MIXMAX) and, as an alternative, a linear interaction model. A factorial hidden Markov model is applied for tracking pitch over time. This statistical model can be used for applications beyond speech, whenever the interaction between individual sources can be represented as MIXMAX or linear model. For tracking, we use the loopy max-sum algorithm, and provide empirical comparisons to exact methods. Furthermore, we discuss a scheduling mechanism of loopy belief propagation for online tracking. We demonstrate experimental results using Mocha-TIMIT as well as data from the speech separation challenge provided by Cooke We show the excellent performance of the proposed method in comparison to a well known multipitch tracking algorithm based on correlogram features. Using speaker-dependent models, the proposed method improves the accuracy of correct speaker assignment, which is important for single-channel speech separation. In particular, we are able to reduce the overall tracking error by 51% relative for the speaker-dependent case. Moreover, we use the estimated pitch trajectories to perform single-channel source separation, and demonstrate the beneficial effect of correct speaker assignment on speech separation performance.

We introduce two suboptimal search heuristics for reducing the computational burden in single channel source separation. The heuristics approximating the observation likelihood are evaluated using the speaker dependent factorial-max VQ model. One approach extends beam search, whereas the second relies on the iterated conditional modes algorithm. We compare the methods to the hierarchically structured VQ model [1] and to the full search using the Grid Corpus [2]. The first two algorithms reduce the computational costs by almost two orders of magnitude compared to full search, whereas the separation performance shows a slight and insignificant decrease in terms of target-to-masker ratio. Additionally, the heuristics are compared in terms of execution time.

We present a simple and efficient feature modeling approach for tracking the pitch of two speakers speaking simultaneously. We model the spectrogram features of single speakers using Gaussian mixture models in combination with the minimum description length model selection criterion. Furthermore, the mixture maximization (MIXMAX) interaction model is employed to yield a probabilistic representation for the mixture of both speakers. Finally, a factorial hidden Markov model is applied for tracking. We demonstrate experimental results on two databases, and show the excellent performance of the proposed method in comparison to a well known multipitch tracking algorithm based on correlogram features.

We propose a fully probabilistic model for source-filter based single channel source separation. In particular, we perform separation in a sequential manner, where we estimate the source-driven aspects by a factorial HMM used for multi-pitch estimation. Afterwards, these pitch tracks are combined with the vocal tract filter model to form an utterance dependent model. Additionally, we introduce a gain estimation approach to enable adaptation to arbitrary mixing levels in the speech mixtures. We thoroughly evaluate this system and finally end up in a speaker independent model.

We propose a probabilistic factorial sparse coder model for single channel source separation in the magnitude spectrogram domain. The mixture spectrogram is assumed to be the sum of the sources, which are assumed to be generated frame-wise as the output of sparse coders plus noise. For dictionary training we use an algorithm which can be described as non-negative matrix factorization with ℓ0 sparseness constraints. In order to infer likely source spectrogram candidates, we approximate the intractable exact inference by maximizing the posterior over a plausible subset of solutions. We compare our system to the factorial-max vector quantization model, where the proposed method shows a superior performance in terms of signal-to-interference ratio. Finally, the low computational requirements of the algorithm allows close to real time applications. © 2010 ISCA.

We present a new source-filter based method to separate two speakers talking simultaneously at equal level mixed into a single sensor. First, the relation between the spectral whitened mixture and the speakers excitation signals is analyzed. Therefore, a factorial HMM capturing also time dependencies is exploited. Then, the estimated excitation signals are combined with best fitting vocal tract information taken from a trained dictionary. We report results on the database of Cooke considering 108 speech mixtures. The average improvement of 2.9 dB in SIR for all data is lower but not significantly lower compared to the Gaussian mixture method which relies on known pitch-tracks. Although the performance is currently moderate we believe in this approach and its significance towards the development of speaker independent single sensor speech separation.

In this paper an enhanced noise reduction for robust speech recognition is implemented by means of a perceptual wavelet filtering algorithm. The psychoacoustic model is applied to map the universal thresholds to the perceptually universal thresholds for each critical wavelet subband. By improving our quantile filtering method, the change of noise level is tracked more adaptively. The denoising algorithm is compared with well-known noise reduction methods embedded in different state-of-the-art speech recognizers. We achieve almost similar recognition performance with the HTK recognizer on AURORA3 SPEECHDAT-Car corpus and an improvement with the Loquendo recognizer on SNOW-Factory corpus.