QUALITY ASPECTS OF DIGITAL HEARING AID ALGORITHMS

For several modern signal-processing schemes for hearing aids (e.g. singleor multi-channel noise reduction schemes, dynamic compression, dereverberation) technical measures are used during the development process for performance prediction that are either inappropriate or can only approximately predict the success for hearing impaired (and also normal hearing) people. In this context, not only the speech intelligibility, but also the subjective signal quality is of importance. In this study we present and compare subjective and objective assessment methods. Particularly, the objective quality measure PEMO-Q that is based on modern auditory models is evaluated for different hearing aid algorithms. This approach appears to be significantly superior to the classic SNR-based approaches in some quality aspects. 1. ALGORITHMS AND SIGNALS In this exemplary study, STSA algorithms according to Ephraim and Malah’s weighting rules [1] where employed as single channel state-of-the-art algorithms. These algorithms are characterized by a strong reduction of noise while introducing only little of the well known musical tones or musical noise that result from subtracting an average noise spectrum from a non-stationary frame-based spectral estimate. A detailed description of the involved filter parameters can be found in Cappé [2]. The most important parameters are two signal-to-noise ratio (SNR) estimates: An instantaneously estimated (a posteriori) SNR and an a priori SNR estimate that is calculated by a recursive smoothing of preceding a posteriori values. The considered algorithms need a reliable noise power estimation. Here, the minimum statistics method (MinStat) by Martin [3] and a Voice Activity Detection (VAD) algorithm by Marzinzik [4] are used. The speech signals used here were taken from the Oldenburg Logatome Speech Corpus (OLLO) [5] and consisted of six sentences spoken by German male and female speakers. The noise signals were speech-shaped noise, cafeteria noise, icra7 noise (speech like modulated noise) and white gaussian noise. All signals had an approximate duration of 20 seconds and a sampling rate of 16 kHz. In the simulation system the signals were mixed at a SNR of 0 dB and 5 dB. The calculation of the time-variant filter was made on this mixture while the filtering process was also done on the separate speech and noise signals for subsequent quality assessment and the calculation of the SNRE and other quality measures. 2. OBJECTIVE MEASURES Several objective measures were assessed in this study. The quality prediction method PEMO-Q is based on a psychoacoustically validated quantitative model of the ”effective” peripheral auditory processing by Dau et al. [6]. The perceptual similarity measure (PSM), obtained from PEMO-Q, is a correlation measure between two so called internal representations of acoustic stimuli, i.e., the output of the modeled peripheral auditory system. PSM serves to predict the perceived similarity between two given signals, generally a reference signal and a test signal whose quality is to be measured. As another psychoacoustical measure, the ITU standard measure PESQ is used. PESQ stands for ”Perceptual Evaluation of Speech Quality” and is an enhanced perceptual quality measurement for voice quality in telecommunications [7]. PSM and PESQ were varied with different options and reference signals (e.g. SNR PSM, PSM b, ∆PESQ,), for details see [8]. Besides the above perceptual measures also more ”technically based” quality measures where incorporated (see [8] for more details). These were signal-to-noise ratio enhancement (SNRE), coherence, a critical bandwidth weighted SNRE (freq. wt. SNRE), and the quality evaluation measures defined by Hansen and Pellom [9]: segmental SNR, LogArea Ratio (LAR), Log-Likelihood Ratio (LLR) and the Itakura-Saito Distance (ISD). The last three measures are based on a LPC-Model. 3. EXPERIMENTS AND RESULTS The recursive smoothing parameter τ of the STSA algorithm was varied in the range from 0 − 800ms to cover a broad range of noise reduction and signal quality. All signals were processed with the noise estimators Minstat and VAD, respectively. For each setting, the above mentioned quality measures were calculated for a number of speech signals mixed with different types of noise. Subjective listening tests were done according to ITU-T Recommendation P.835 [10] which describes a methology for evaluating the subjective quality of speech in noise and is particularly appropriate for the evaluation of noise suppression algorithms. The data from subjective listening tests was then correlated with the quality predictions of the objective measures (see table 1 for the overall quality prediction). The best correlated measures (see Fig. 1) were chosen to predict the subjective rating curves. The objective curves have been linearly fitted to match the scaling of the subjective curves. As for the background noise rating, the highest correlations are gained by the SNRE. This means that SNRE is a good measure to rate the amount of noise reduction by an algorithm, independent of the speech signal quality. In terms of speech-signal rating the perceptual measures with the noisy reference (SNR PSM, SNR PESQ) have the highest correlations with subjective data. The best correlation in terms of overall quality rating show the perceptual measures with clean speech reference (PSM b and PESQ). Correlation with overall quality rating Cafeteria noise White noise Speechshaped noise ICRA7 noise OverallCorrelation SNRE 0.35 0.66 0.41 0.29 0.35 Coherence 0.83 0.88 0.93 0.93 0.65 seg. SNRE 0.74 0.89 0.89 0.71 0.53 freq. wt. SNRE -0.17 0.40 -0.05 -0.11 0.00 mean LAR -0.46 -0.90 -0.45 -0.28 -0.07 mean LLR -0.75 -0.94 -0.61 -0.68 -0.43 mean ISD -0.24 -0.79 -0.04 -0.16 -0.34 PSM 0.82 0.92 0.93 0.87 0.70 PSM_b 0.85 0.91 0.94 0.93 0.76 PESQ 0.85 0.92 0.94 0.94 0.81 ΔPSM 0.71 0.69 0.58 0.54 0.39 ΔPESQ 0.86 0.92 0.48 0.65 0.47 SNR_PSM 0.12 -0.04 0.09 0.01 0.04 SNR_PESQ 0.09 -0.36 0.05 0.07 0.00 Table 1: Correlation between objective and subjective measures for the overall signal quality. (from [8]) 0 200 400 600 800 2 2.5 3 3.5 4 4.5 tau / ms ra tin g Subjective data for MinStat + speech−shaped noise Background noise rating Speech signal rating Overall quality rating 0 200 400 600 800 2 2.5 3 3.5 4 4.5 tau / ms ra tin g Subjective data for VAD + speech−shaped noise Background noise rating Speech signal rating Overall quality rating 0 200 400 600 800 1.5 2 2.5 3 3.5 4 4.5 5 tau / ms ra tin g Subjective data for MinStat + ICRA7 noise Background noise rating Speech signal rating Overall quality rating 0 200 400 600 800 1.5 2 2.5 3 3.5 4 4.5 tau / ms ra tin g Subjective data for VAD + ICRA7 noise Background noise rating Speech signal rating Overall quality rating 0 200 400 600 800 2 2.5 3 3.5 4 4.5 tau / ms ra tin g Prediction from best correlated objective measures