0000000000274233
AUTHOR
Rahul Jaiswal
showing 4 related works from this author
Towards a Non-Intrusive Context-Aware Speech Quality Model
2020
Understanding how humans judge perceived speech quality while interacting through Voice over Internet Protocol (VoIP) applications in real-time is essential to build a robust and accurate speech quality prediction model. Speech quality is degraded in the presence of background noise reducing the Quality of Experience (QoE). Speech Enhancement (SE) algorithms can improve speech quality in noisy environments. The publicly available NOIZEUS speech corpus contains speech in environmental background noise babble, car, street, and train at two Signal-to-noise ratio (SNRs) 5dB and 10dB. Objective Speech Quality Metrics (OSQM) are used to monitor and measure speech quality for VoIP applications. Th…
Implicit Wiener Filtering for Speech Enhancement In Non-Stationary Noise
2021
Speech quality is degraded in the presence of background noise, which reduces the quality of experience (QoE) of the end-user and therefore motivates the usage of speech enhancement algorithms. A large number of approaches have been proposed in this context. However most of them have focused on the case where the noise is stationary, an assumption that seldom holds in practice. For instance, in mobile telephony, noise sources with a marked non-stationary spectral signature include vehicles, machines, and other speakers to name a few. On the other hand, the usage of frequency-domain information in existing algorithms for speech enhancement in non-stationary noise environments can be made mor…
Speech Activity Detection under Adverse Noisy Conditions at Low SNRs
2021
Speech originating from the noisy environments degrades the speech quality and intelligibility, thus reducing the human perceived Quality of Experience (QoE). For example, surveillance using drone during natural catastrophe needs an efficient speech recognition device to recognise the speech of the frozen human in presence of drone noise to save their life. Therefore, it often requires to pre-process the noisy speech in order to reduce the noise artifacts and enhance the speech. This paper detects the speech activity using Voice Activity Detection (VAD). The VAD distinguishes speech activity (speech presence) and speech inactivity (silence/noise) by extracting the speech features and compar…
Influence of Silence and Noise Filtering on Speech Quality Monitoring
2021
With the exponential increase of mobile users and internet subscribers, the utilization of voice over internet protocol (VoIP) application is increasing dramatically. People exploit different VoIP applications for effective communication, for example, Google Meet, Microsoft Skype, Zoom video conferencing applications, etc. The single-ended speech quality metrics are employed for measuring and monitoring the quality of speech. However, different types of degradations present in the surroundings distort the quality of speech. In order to meet the desired quality of experience (QoE) level of end-user while using VoIP applications, it is necessary to reduce VoIP degradations and obtain the opti…