Cocktail party processing based on interaural parameters

Noise reduction algorithms have been proposed which imitate aspects of the binaural processing in the hearing system. Based on a two-channel input, it is theoretically possible to cancel one interfering directional noise source at a time and to cancel reverberation based on coherence measures. In practice, however, no significant increase in speech intelligibility was found in complex environments (diffuse noise, reverberation). Therefore, a localization algorithm is introduced, which uses a statistical description of the binaural signal differences in order to increase the robustness of sound localization in noisy environments. Possible noise reduction schemes based on this localization algorithm are discussed. INTRODUCTION Two general types of algorithms for binaural noise reduction can be identified: Quasi-stationary spatial filtering (e.g. blind source separation (BSS, [1,2]) and adaptive beamforming [3,4,5]). Time-variant envelope filtering (e.g., Wiener filtering [6] and directional filtering [7]). ‘Quasi-stationary’ means in this case that the filter characteristics do not change with the dynamics of speech, but with the slower changes in the spatial configuration of the sound sources. In conjunction with minor filter constraints, this property guarantees an almost artifactfree output signal. However, complex transfer functions with rather long impulse responses have to be applied for spatial filtering in order to achieve satisfactory source separation. Because the adaptation process is generally unconstrained and relies on statistical properties of the signal (e.g., higher order statistics [8]), the filter adaptation time is long compared to possible changes in the spatial environment. Furthermore, only two sound sources can be separated by using a binaural input. Thus, the filter effect is limited for multi-talker configurations and target suppression often occurs in these configurations due to filter convergence errors. Envelope filtering, on the other hand, requires filter adaptation and variation with a much higher temporal resolution of about the syllable rate, but uses shorter impulse responses, i.e. simpler filters. In general, these algorithms estimate the envelope of the target source in several frequency bands, which is equivalent to short-time signal-to-noise ratio (SNR) estimation. The target is defined by the frontal direction, in most cases. The signal is then modulated with the estimated envelope in order to reconstruct the target’s envelope and thereby suppress the other sources. When defining source separation based on the envelope rather than the complete fine structure of the signals, binaural envelope filtering is not limited to suppressing one noise source only. In multi-talker configurations including reverberation, however, the SNR estimator shows significant errors. Envelope filtering is therefore prone to generate disturbing artifacts due to the limited information on the true target envelope. In the following, one example of an envelope filtering algorithm [9] is briefly examined, which shows the above mentioned limited effect in complex source configurations. Then, methods are developed that should allow in principle for a combination of spatial and envelope filtering in order to avoid the respective problems outlined above. DIRECTIONAL FILTERING AND DEREVERBERATION The algorithm [9] considers the two source configurations depicted in Fig. 1: A. nearly frontal target signal perturbed by diffuse noise (left panel) or by a lateral interfering signal (right panel). In order to reduce diffuse noise, the algorithm [9] measures the coherence between left and right channels in 21 frequency bands (log. scaled) and filters the envelope in each band such that signal segments with low coherence are attenuated (dereverberation). In order to reduce lateral noise sources, the timevarying interaural level and phase differences are compared to reference values for the frontal target. Signal segments with interaural differences close to the reference are identified as target and segments with deviating differences are identified as noise. The latter are attenuated, whereas the former pass the filter unchanged (directional filtering). Attack and release time constants are about 2040 ms, respectively, allowing for filtering the envelope at the syllable rate. Due to errors in estimating the target envelope from the interaural parameters, directional filtering was found to generate artifacts especially in diffuse noise conditions or multi-talker configurations. Therefore, a coherence measure is used in [9] to identify the acoustical situation and to gradually switch off the directional filtering with increasing complexity (selective algorithm). Fig. 2 shows results from speech intelligibility measurements with 8 hearing impaired subjects. The SRT (speech reception threshold, SNR at which 50% sentence intelligibility is reached) was measured unaided, with linear hearing loss compensation (linear), with dereverberation and directional filtering (linear fixed) and with dereverberation and gradual switching of the directional filtering (linear selective). Fig. 1: Source configurations considered by [9]. Fig. 2: Median value, quartile range and total range of speech-reception-thresholds (SRT) from 8 hearing impaired subjects. Lower values denote improved intelligibility. See text for details. (from [9]) Configurations were a frontal target signal in lateral unmodulated noise (left group of bars), in lateral speech (mixture of 4 talkers, middle group of bars) and in diffuse noise (right group of bars), respectively. Fixed directional filtering decreases intelligibility, whereas a small but not significant improvement is shown for the selective algorithm in the diffuse noise condition. Subjective preference data (not shown here) reveal an advantage of the binaural noise suppression (selective filtering) compared to linear filtering. It can be concluded that binaural filtering, especially dereverberation, might help by reducing the listening effort in noisy environments, but intelligibility improvement is still lacking. NOTION OF‘DISJOINT ORTHOGONALITY’ Jourjine et al. [10] propose a method for signal separation based on the assumption that the signals are ‘disjoint orthogonal’, which means that target and noise do not overlap in a suitable signal space (e.g., the spectrotemporal domain for acoustical signals). In this case, unmixing two signals just means to identify the subset within the space that belongs to the target and reconstruct the target from this subset only. The directional filter (cf. section above) can be described in this way: The identification of the target FFT bins is based on whether the interaural parameters fit with the reference or not. However, it is unclear to what extent disjoint orthogonality is met in real acoustical signals. In this section, we therefore introduce an empirical method to quantify the overlap between target and noise signals. We define the global disjoint orthogonality measure cdjo by ( ) ( ) 1 2 ( , ) , , djo s t f X t f X t f = ⋅