Most of the existing loudness models are based on the diotic listening hypothesis,though human beings always hear in dichotic listening conditions.In this situation,the arithmetic mean of loudness at both ears is usua...Most of the existing loudness models are based on the diotic listening hypothesis,though human beings always hear in dichotic listening conditions.In this situation,the arithmetic mean of loudness at both ears is usually taken as the approximate value of overall perceived loudness,unaffected by the interaural level difference(ILD).The present work investigated the overall perceived loudness for pure tones in dichotic listening conditions through a subjective experiment.Two experimental procedures and systematic errors were investigated to prove the accuracy of the subjective test.The results showed that fluctuation was insignificant in the low frequency range,while apparent fluctuation of overall loudness could be observed at a high frequency.The overall loudness deviated from the arithmetic mean value as the ILD became larger.A revised model for overall loudness in dichotic listening conditions was proposed.The proposed model and experiment are consistent.展开更多
Most existing algorithms for the underdetermined blind source separation(UBSS) problem are two-stage algorithm, i.e., mixing parameters estimation and sources estimation. In the mixing parameters estimation, the previ...Most existing algorithms for the underdetermined blind source separation(UBSS) problem are two-stage algorithm, i.e., mixing parameters estimation and sources estimation. In the mixing parameters estimation, the previously proposed traditional clustering algorithms are sensitive to the initializations of the mixing parameters. To reduce the sensitiveness to the initialization, we propose a new algorithm for the UBSS problem based on anechoic speech mixtures by employing the visual information, i.e., the interaural time difference(ITD) and the interaural level difference(ILD), as the initializations of the mixing parameters. In our algorithm, the video signals are utilized to estimate the distances between microphones and sources, and then the estimations of the ITD and ILD can be obtained. With the sparsity assumption in the time-frequency domain, the Gaussian potential function algorithm is utilized to estimate the mixing parameters by using the ITDs and ILDs as the initializations of the mixing parameters. And the time-frequency masking is used to recover the sources by evaluating the various ITDs and ILDs. Experimental results demonstrate the competitive performance of the proposed algorithm compared with the baseline algorithms.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.10674104)
文摘Most of the existing loudness models are based on the diotic listening hypothesis,though human beings always hear in dichotic listening conditions.In this situation,the arithmetic mean of loudness at both ears is usually taken as the approximate value of overall perceived loudness,unaffected by the interaural level difference(ILD).The present work investigated the overall perceived loudness for pure tones in dichotic listening conditions through a subjective experiment.Two experimental procedures and systematic errors were investigated to prove the accuracy of the subjective test.The results showed that fluctuation was insignificant in the low frequency range,while apparent fluctuation of overall loudness could be observed at a high frequency.The overall loudness deviated from the arithmetic mean value as the ILD became larger.A revised model for overall loudness in dichotic listening conditions was proposed.The proposed model and experiment are consistent.
基金supported by the National Natural Science Foundation of China(Grant Nos.61162014,61210306074)the Natural Science Foundation of Jiangxi Province of China(Grant No.20122BAB201025)the Foundation for Young Scientists of Jiangxi Province(Jinggang Star)(Grant No.20122BCB23002)
文摘Most existing algorithms for the underdetermined blind source separation(UBSS) problem are two-stage algorithm, i.e., mixing parameters estimation and sources estimation. In the mixing parameters estimation, the previously proposed traditional clustering algorithms are sensitive to the initializations of the mixing parameters. To reduce the sensitiveness to the initialization, we propose a new algorithm for the UBSS problem based on anechoic speech mixtures by employing the visual information, i.e., the interaural time difference(ITD) and the interaural level difference(ILD), as the initializations of the mixing parameters. In our algorithm, the video signals are utilized to estimate the distances between microphones and sources, and then the estimations of the ITD and ILD can be obtained. With the sparsity assumption in the time-frequency domain, the Gaussian potential function algorithm is utilized to estimate the mixing parameters by using the ITDs and ILDs as the initializations of the mixing parameters. And the time-frequency masking is used to recover the sources by evaluating the various ITDs and ILDs. Experimental results demonstrate the competitive performance of the proposed algorithm compared with the baseline algorithms.
