Evolving multimedia systems are increasingly being adopted in virtual reality and gaming applications. Such systems emphasize immersion to engage users by bridging the gap between real and virtual content. In this context, visual and acoustic stimuli are the two key media that dictate such immersion. While visual 3D rendering is advancing rapidly, the same is not true for audio, where most research is limited to the reconstruction of the room impulse response (RIR) using omnidirectional audio or, at best, binaural. Such methods do not adequately account for the directions and orientations of the acoustic signals with respect to either the source or the listener, thereby compromising immersion quality. In this work, we explore the effect of adding such "directionality" to the training data to improve the estimation of the room’s acoustic parameters. A more accurate set of such parameters implies in fact a more realistic predicted RIR, leading to a more immersive experience of the acoustic scene. Specifically, we propose a novel framework driven by a suitable loss function to account for directionality in ambisonic microphones, and novel variants of loss functions for both omnidirectional and ambisonic cases. We also propose to account for microphone characteristics and their contribution to the predicted RIRs. Experiments were performed using two datasets of real recordings and the results established the efficacy of the proposed methods

In recent developments, speech intelligibility has been improved through an optimal trapezoidal transformation function, which performed normal to Lombard speech conversion via formant shifting. Despite performing well, the optimization took very long to converge and led to artifacts in the modified signal due to aggressive formant shifts in unvoiced frames. Therefore, transfer learning was used to rapidly modify the optimized parameters for a target language to bypass re-optimization for a new language. However, such transfer across noises was left unaddressed. This work proposes a Gaussian transformation function to perform statistically guided normal to Lombard speech conversion. Optimizing fewer parameters ensures faster convergence than before. The new transformation function generates fewer artifacts during voice modification while performing at par with the earlier function. This work enhances transfer learning performance by mitigating the directional nature in case of language mismatch. We also propose the transfer learning across noises using the comparative estimations of noise magnitude spectra, which was not feasible earlier. The simultaneous transfer of parameters across languages and noises is now feasible via the proposed Gaussian transformation function. We also explore the statistical difference between formant shifts produced by the Gaussian transformation function and its predecessor and their effect on intelligibility improvement. All experiments were conducted on exhaustive combinations of three languages, four noise types, and three SNR levels.