Premananda B S

In mobile phones, perceived quality of speech signal deteriorates significantly in the presence of near-end/surrounding noise as it arrives at the near-end listener's ears. There is a need to improve the intelligibility and quality of the received speech signal in noisy environments by developing speech enhancement algorithms. Paper focuses on the impact of near-end noises on signal degradation. Gain adjustment process employing psychoacoustic has been adopted to improve the intelligibility and quality of the speech signal in the noisy environments by automatically enhancing the speech signals when the noise dominates. Intelligibility and quality of enhanced speech signal are measured using SII and PESQ. Experimental results show intelligibility and quality improvement of speech signal with the proposed method over the unprocessed far-end speech signal. Approach is more efficient in overcoming the degradation of speech signals in presence of near-end noise. � 2014 IEEE.

In mobile phones, perceived speech signals deteriorates significantly in the presence of background noise as it arrives at the near-end listener's ears. There is an inherent need to improve the intelligibility and quality of the received speech signal in noisy environments by developing speech enhancement algorithms. This paper focuses on the speech enhancement method by incorporating temporal masking properties of the human ear. The gain adjustment process employing both pre- and post-masking are adopted to improve the intelligibility and quality of the speech signal This is implemented by automatically enhancing the speech signal when the noise is dominant. The intelligibility and quality of the speech signal are measured using SII and PESQ respectively. Experimental results show improvement in the intelligibility and quality of the speech signal with the proposed method over unprocessed far-end speech signal The proposed approach is efficient in overcoming the degradation of speech signals in the presence of background noise. � 2014 IEEE.

In mobile phones, perceived quality of speech signal deteriorates significantly in the presence of background noise since near-end/surrounding noise also arrives at the near-end listener's ears. The quality of the received signal varies widely depending upon signal strength and unavoidable background noise in the user environment. There is a need to improve the quality of received speech signal in noisy conditions by developing the speech enhancement algorithms. This paper focuses on the impact of the various background noises on signal perception and mechanisms to mitigate the noise impact for improved signal perception. Gain adjustment process with simple time domain approach has been adapted to improve the quality and intelligibility of the speech signal in the noisy environments by automatically increasing output levels when the noise dominates. Since time domain approach has been used, it is less complex and consumes very less battery power of the mobile and also efficiently masks the background noise. This paper studies the effect of various signal parameters on automatic gain adjustment of the received signal to enhance its perception. � 2013 Springer Science+Business Media.

In mobile phones, perceived speech signal deteriorates significantly in the presence of background noise as noise reaches directly at the listener's ears. There is an inherent need to increase the intelligibility of the received speech signal in noisy environments by incorporating speech enhancement algorithm at the receiverend. This paper focus on the enhancement of selective speech samples for the intelligibility improvement, when the near-end noise dominates. Audible speech samples are selected by consideringthreshold of hearing and auditory masking properties of the human ear. Intelligibility of enhanced speech signalis measured using Speech Intelligibility Index. Experimental results show the intelligibility improvement of the speech signal in lower SNR with the proposed approach to the unprocessed far-end speech signal. This approach is efficient in overcoming the degradation of speech signals in very noisy environments. � 2015 The Authors. Published by Elsevier B.V.