Speech Enhancement Algorithm for Digital Hearing Aids on the basis of Auditory Scene Analysis Masahiro Sunohara, Kiyoaki Terada, Takatoshi Okuno & Takashi Iwakura, RION CO., LTD gp,0 gp,1 gp,2 gp,3 F0 Computer Simulation Introduction Japanese female speech with white noise weighted by speech power spectrum (SN +10dB). Normal hearing people have an instinct ability to segregate complexly-mixed sound. That’s why they can communicate with others in a noisy environments. The ability has been summarized as the basis of psychoacoustical knowledge called Auditory Scene Analysis: ASA (Bregman 1990). Most of the hearing impaired people have a difficulty in understanding speech in a noisy environments even if they wear their hearing aids… It can be assumed that the ability of ASA for the hearing impaired people is getting worse compared with the normal listener. We propose a new speech enhancement algorithm which can help the impaired ability of ASA for better understandings of speech in noise. In our system, the fundamental frequency F0 and its harmonics for speech are enhanced with a low computational cost. Fig.3 A result of the F 0 extraction for speech plotted on the spectrogram. (Japanese male speech) However … Time-variant flexible comb filter Fig.8 Waveforms of input (blue) and enhanced signal (red). Frequency [Hz] Magnitude [dB] Time [sec] Fig.5 Spectrum of the comb filter and multi-level filterbank. To enhance the F0 and its harmonics, a time-variant flexible comb filter is designed to deliver a natural enhanced sound with a low computational cost. Fig.4 shows a block diagram of the filter. Controlling time-variant gains To improve the hearing ability … The optimal characteristic for the comb filter is reconsidered from the perspective of the Wiener filter shown in Fig.6. (Xp(z)) ① ② Fig.9 3D views of the response of comb filter (left) , spectrogram of input (center) and enhanced signal (right). (H(jω)) Fig.4 Block diagram of a flexible comb filter. Evaluation The N-band multi-level filterbank in Fig.4 consists of a tree structured allpass filters. The transfer functions HM(z) from Xp(z) to each Cp,n(z) is defined as follows. Fig.6 Block diagram of the speech enhancement method from the perspective of the Wiener filter. The prototype hearing aid with the algorithm are prepared. The word intelligibility test for 6 hearing impaired subjects with prototypes is carried out. Sound source is 20 test words (4 moras) with a multi-talker noise so that SN ratio is +10 dB. Fig.1 Block diagram of the speech enhancement system. This algorithm involves two processing sections such as … ① F0 extraction for speech ② Time-variant flexible comb filter The amplitude of the enhancement filter |H(jωp)| for the peak is estimated by minimizing the mean-squared error E[e2(n)] along with the Wiener-Hopf equation and Wiener-Khinchin theorem as (1) (4) F 0 extraction where Rl is an l×l Hadamard matrix, denotes the Kronecker product of matrices, Ak(z) is an allpass filter providing the doubly complementary filter pairs. The entire transfer function C(z) of the comb filter is defined as Since Eq.(4) is a simple theoretical equation, the gain fluctuates rapidly when SNR stays around 0dB. To prevent this, the amplitude characteristic is modified using the sigmoid function as follows Improvement score [%] An algorithm of the F0 extraction is proposed based on spectro- temporal approach. Computational cost of the method is rather small because of curtailing the use of the autocorrelation function. First, an input signal is filtered through the filterbank consisted of 4 bandpass filters whose center frequencies are 145, 197, 256 and 323 Hz. Next, the filtered signals are quantized to binary sequences. The candidates of F0 are obtained by detecting the period of rising edges from binary sequences. Finally, the most likely candidate is chosen via a conditional branching. Fig.3 shows a result of F0s. (2) (5) Fig.10 Improvement scores for each subject. The word intelligibility score for 5 out of 6 subjects are improved or almost same. However, score of sub.F is dropped. Some subjects complain of the whistle sound or echoic feeling. where L is the number of samples corresponding to a reciprocal of F0. The amplitude characteristic |C(ejωpT)| of the comb filter at each peak frequency is as follows, Fig.7 shows the above two gain functions calculated from Eq.(4) and (5). Conclusions (3) A speech enhancement algorithm for digital hearing aid is proposed. The F0 of speech can be estimated robustly by the algorithm. The F0 and its harmonics can be enhanced naturally by the use of the proposed comb filter. The evaluation results show that the word intelligibility scores are slightly improved in a noisy case. Eq.(3) indicates that the amplitude characteristics of each peak correspond to the one of the multi-level filterbank. The amplitudes are determined by the gain vector gp only, and is completely independent of the gd. Fig. 5 shows the amplitude spectrum of the comb filter derived from the Eq.(3) and multi-level filterbank. Fig.2 Block diagram of the F 0 extraction. Fig.7 Two gains as a function of the SNR .