Energy-Efficient Design for Relay-Aided MIMO-OFDM Cognitive Radio Networks

With the explosive growth of high-rate multimedia services and promptly boomed energy consumption in wireless networks, energy-efficient design is become more and more important. In this paper, we investigate energy-efficient design for relay-aided multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) cognitive radio networks. We formulate an energy-efficient power allocation problem, which takes a form of nonlinear fractional programming. To solve the problem, we first make a joint concave approximation to the original problem which facilitates the optimal algorithm development. Then, we derive an equivalent parametric optimization problem of the approximated problem. Finally, an iteration energy-efficient power allocation algorithm is presented. Numerical results reveal that the proposed algorithm can improve energy efficiency over traditional capacity maximization method

A Novel DBN Feature Fusion Model for Cross-Corpus Speech Emotion Recognition

The feature fusion from separate source is the current technical difficulties of cross-corpus speech emotion recognition. The purpose of this paper is to, based on Deep Belief Nets (DBN) in Deep Learning, use the emotional information hiding in speech spectrum diagram (spectrogram) as image features and then implement feature fusion with the traditional emotion features. First, based on the spectrogram analysis by STB/Itti model, the new spectrogram features are extracted from the color, the brightness, and the orientation, respectively; then using two alternative DBN models they fuse the traditional and the spectrogram features, which increase the scale of the feature subset and the characterization ability of emotion. Through the experiment on ABC database and Chinese corpora, the new feature subset compared with traditional speech emotion features, the recognition result on cross-corpus, distinctly advances by 8.8%. The method proposed provides a new idea for feature fusion of emotion recognition

A Design Method for Gammachirp Filterbank for Loudness Compensation in Hearing Aids

Because the hearing impaired often experience different degrees of hearing loss along with the loss of frequencies, the loudness compensation algorithm in hearing aids decomposes the speech signal and compensates with different frequency bands based on their audiograms. However, the speech quality of the compensated signal is unsatisfactory because the traditional filterbanks fail to fully consider the characteristics of human hearing and personalized hearing loss. In this study, an effective design for the gammachirp filterbank for the loudness compensation algorithm was proposed to improve the speech quality of hearing aids. Firstly, a multichannel gammachirp filterbank was employed to decompose the signals. Then, the adjacent bands were merged into one channel, guided by the proposed combination method. After obtaining the personalized filterbank, each band conducted a loudness compensation to match the requirements of the audiograms. The excellent advantage of the gammachirp filterbank is that it can simulate the characteristics of the basilar membrane. Furthermore, the novel channel combination method considers the information from the audiograms and the relationship between frequency ranges and speech intelligibility. The experimental results showed that the proposed multichannel gammachirp filterbank achieves better speech signal decomposition and synthesis, and good performance can be gained with fewer channels. The loudness compensation algorithm based on the gammachirp filterbank effectively improves sentence intelligibility. The sentence recognition rate of the proposed method is higher than that of a system with a gammatone filterbank by approximately 13%

Piecewise-Linear Frequency Shifting Algorithm for Frequency Resolution Enhancement in Digital Hearing Aids

In human hearing, frequency resolution is a term used to determine how well the ear can separate and distinguish two sounds that are close in frequency. This capability of breaking speech sounds into various frequency components plays a key role in processing and understanding speech information. In this paper, a piecewise-linear frequency shifting algorithm for digital hearing aids is proposed. The algorithm specifically aims at improving the frequency resolution capability. In the first step, frequency discrimination thresholds are processed by a computer testing software. Then, the input signal is parsed through the proposed piecewise-linear frequency shifting algorithm, which comprises of linearly stretching and compressing the frequency content at different frequency ranges. Experimental results showed that by using the proposed frequency shifting algorithm, the separation of formant tracks was increased in the stretching region and slightly squeezed in the adjacent compression region. Subjective assessment on six hearing-impaired persons with V-shaped audiograms demonstrated that nearly a 10% improvement of speech discrimination score was achieved for monosyllabic word lists tested in a quiet acoustic setting. In addition, the speech reception threshold was also improved by 2~8 dB when disyllabic word listswere tested in a noisy acoustic scenario

Piecewise-Linear Frequency Shifting Algorithm for Frequency Resolution Enhancement in Digital Hearing Aids

In human hearing, frequency resolution is a term used to determine how well the ear can separate and distinguish two sounds that are close in frequency. This capability of breaking speech sounds into various frequency components plays a key role in processing and understanding speech information. In this paper, a piecewise-linear frequency shifting algorithm for digital hearing aids is proposed. The algorithm specifically aims at improving the frequency resolution capability. In the first step, frequency discrimination thresholds are processed by a computer testing software. Then, the input signal is parsed through the proposed piecewise-linear frequency shifting algorithm, which comprises of linearly stretching and compressing the frequency content at different frequency ranges. Experimental results showed that by using the proposed frequency shifting algorithm, the separation of formant tracks was increased in the stretching region and slightly squeezed in the adjacent compression region. Subjective assessment on six hearing-impaired persons with V-shaped audiograms demonstrated that nearly a 10% improvement of speech discrimination score was achieved for monosyllabic word lists tested in a quiet acoustic setting. In addition, the speech reception threshold was also improved by 2~8 dB when disyllabic word listswere tested in a noisy acoustic scenario