Digital hearing aid users often complain of difficulty in understanding speech in the presence of background noise. To improve speech perception in a noisy environment, various speech enhancement algorithms have been applied in digital hearing aids. In this study, a speech enhancement algorithm using modified spectral subtraction and companding is proposed for digital hearing aids. We adjusted the biases of the estimated noise spectrum, based on a subtraction factor, to decrease the residual noise. Companding was applied to the channel of the formant frequency based on the speech presence indicator to enhance the formant. Noise suppression was achieved while retaining weak speech components and avoiding the residual noise phenomena. Objective and subjective evaluation under various environmental conditions confirmed the improvement due to the proposed algorithm. We tested segmental SNR and Log Likelihood Ratio (LLR), which have higher correlation with subjective measures. Segmental SNR has the highest and LLR the lowest correlation of the methods tested. In addition, we confirmed by spectrogram that the proposed method significantly reduced the residual noise and enhanced the formants. A mean opinion score that represented the global perception score was tested; this produced the highest quality speech using the proposed method. The results show that the proposed speech enhancement algorithm is beneficial for hearing aid users in noisy environments.

Recently many researches concerning heart sound analysis are being processed with development of digital signal processing and electronic components. But there are few researches about recognition of heart sound, especially full cardiac cycled heart sound. In this paper, three new recognition methods about full cardiac cycled heart sound were proposed. The first method recognizes the characteristics of heart sound by integrating important peaks and analyzing statistical variables in time domain. The second method builds a database by principal components analysis on training heart sound set in time domain. This database is used to recognize new input of heart sound. The third method builds the same sort of the database not in time domain but in time-frequency domain. We classify the heart sounds into seven classes such as normal (NO) class, pre-systolic murmur (PS) class, early systolic murmur (ES) class, late systolic murmur (LS) class, early diastolic murmur (ED) class, late diastolic murmur (LD) class and continuous murmur (CM) class. As a result, we could verify that the third method is better efficient to recognize the characteristics of heart sound than the others and also than any precedent research. The recognition rates of the third method are 100% for NO, 80% for PS and ES, 67% for LS, 93 for ED, 80% for LD and 30% for CM.

Howling is very annoying problem to the hearing-aid users and it limits the maximum usable gain of hearing aids. We propose a new feedback cancellation system by inserting a time-varying decorrelation filter in the forward path. We use a second-order all-pass filter with control parameters whose time variation is implemented using a low-frequency modulator. A noticeable reduction of weight-vector misalignment is achievable using our proposed method.