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.

The syllables of speech contain information about the vocal tract length (VTL) of the speaker as well as the phonetic message. Ideally, the pre-processor used for automatic speech recognition (ASR) should segregate the phonetic message from the VTL information. This paper describes a method to calculate VTL-invariant auditory feature vectors from speech, using a method in which the message and the VTL are segregated. Spectra produced by an auditory filterbank are summarized by a Gaussian mixture model (GMM) to produce a low-dimensional feature vector. These features are evaluated for robustness in comparison with conventional mel-frequency cepstral coefficients (MFCCs) using a hidden-Markov-model (HMM) recognizer. A dynamic, compressive gammachirp (dcGC) auditory filterbank is also introduced. The dcGC provides a level-dependent spectral analysis, with near instantaneous compression, and two-tone suppression.

This paper presents a packet loss recovery method that uses an incomplete secondary encoding based on scalar quantization as redundancy. The method is redundancy bit rate scalable and allows an adaptation to varying loss scenarios and a varying packeting strategy. The recovery is performed by minimum mean squared error estimation incorporating a statistical model for the quantizers to facilitate real-time adaptation. A bit allocation algorithm is proposed that extends 'reverse water filling' to the problem of scalar encoding dependent variables for a decoder with a final estimation stage and available side information. We apply the method to the encoding of line-spectral frequencies (LSFs), which are commonly used in speech coding, illustrating the good performance of the method.

We propose a packet loss recovery method that uses an incomplete secondary encoding as redundancy. The recovery is performed by minimum mean squared error estimation. The method adapts to the loss scenario and is rate scalable. It incorporates a statistical model for the quantizers to facilitate real-time adaptation. We apply the method to the encoding of line-spectral frequencies, which are commonly used in speech coding, illustrating the good performance of the method.

In this work we deal with a speech coder which is based on a model of the human peripheral auditory system and uses a neural auditory representation as its code. This representation consists of multi-channel sparse pulse trains but is still highly overcomplete and therefore not efficient in terms of its data compression capability. The emphasis of this paper is on answering the question ‘How sparse can we make the auditory representation?’, i.e., on finding a bound for the number of pulses which can be omitted without degrading the quality of the reconstructed speech signal. For this purpose we incorporate a second auditory model which allows to decide whether a pulse is needed or not based on the excitation pattern caused by the signal when a single pulse is resynthesized. This model accounts for both simultaneous and temporal masking. We also propose a method for compensating for the loss of energy due to the elimination of pulses which makes a possible spectral distortion inaudible. Results show that about 74% of the pulses can be omitted while maintaining the original speech quality.