A significant proportion of the world's population speaks Urdu and Hindi, with many individuals being bilingual in both English and these languages. Still, no multilingual spoofing dataset exists to capture the conversational style of bilingual speakers who frequently code-switch while communicating. This paper presents a multilingual spoofed speech (MSS) dataset comprising 472,486 utterances from 154 speakers. We specifically considered bona fide utterances from Urdu and Hindi speakers, where language alternation occurs within a single audio. Spoofed samples are generated using voice conversion techniques to preserve the speaking accents and conversation styles of bilingual individuals. Further, we propose and evaluate an anti-spoofing framework called WavSpeech-AASIST, which incorporates self-supervised models (wav2vec and UniSpeech) into the AASIST network. Our comparative analysis underscores the significance of the MSS dataset and demonstrates the effectiveness of WavSpeech-AASIST for audio spoofing detection.

High dynamic range (HDR) content provides a better quality of experience than the standard dynamic range (SDR) content due to a wide luminance range, enhanced contrast, and saturated colors. Emotional experience analysis while watching SDR content has been an active research area in affective computing. However, the impact of HDR content on human emotional experience is not explored. This paper presents a statistical analysis of emotional experience in response to HDR and SDR content. To this end, SDR and HDR versions of four audio-visual clips are shown to two different groups, each comprising of 20 male and 10 female subjects. Each subject's emotional experience is recorded in terms of valence, arousal, and dominance scores after watching each clip. A t-test reveals that HDR and SDR content is statistically different in valence, arousal, and dominance scores for overall and gender-based analysis. The subject ratings show that the HDR content enhances the emotional experience in terms of valence and dominance scores.

In the absence of a commercial High Dynamic Range (HDR) distribution pipeline, two-layer backward-compatible HDR video coding is a viable solution for the imminent transition from Low Dynamic Range (LDR) to HDR content transmission. However, the performance of a two-layer coding solution is governed by the extension layer coding performance. In this paper, we propose an improved two-layer backward-compatible HDR video coding solution based on an adaptive residual mapping for the extension layer, keeping in view the performance of High Efficiency Video Coding (HEVC) being used to code this information. The proposed solution outperforms the reference method achieving averaged PU-PSNR improvements of up to 5.05 dB. The proposed method also shows potential of achieving the same HDR quality as the single layer coding solution with a minimum bitrate overhead and acceptable LDR quality in the base layer.

High Dynamic Range (HDR) is a key technology envisioned to provide the end-users a sense of “being there”. With the advent of HDR displays in the near future, it is not clear if the existing content distribution environment will be capable of supporting HDR. Although it is generally argued that single-layer HDR coding solutions are superior to two-layer backward-compatible techniques, these coding strategies have not been fully investigated in terms of their rate distortion trade-off. In this paper, three main HDR coding techniques are implemented to analyze their performance and to evaluate the potential of HDR content distribution, while minimizing the impact on existing infrastructure, i.e., in terms of bitrate and HDR quality trade-off. The results reveal that backward-compatible solutions can outperform the single-layer approach achieving PSNR_DE improvements of up to 3.05dB on average. The HDR-VDP-2 quality indexes show similar improvements further confirming the superior performance of the backward-compatible solutions.