Gabriel Mittag

Gabriel Mittag, Babak Naderi, Vishak Gopal et al.

Current state-of-the-art video quality models, such as VMAF, give excellent prediction results by comparing the degraded video with its reference video. However, they do not consider temporal distortions (e.g., frame freezes or skips) that occur during videoconferencing calls. In this paper, we present a data-driven approach for modeling such distortions automatically by training an LSTM with subjective quality ratings labeled via crowdsourcing. The videos were collected from live videoconferencing calls in 83 different network conditions. We applied QR codes as markers on the source videos to create aligned references and compute temporal features based on the alignment vectors. Using these features together with VMAF core features, our proposed model achieves a PCC of 0.99 on the validation set. Furthermore, our model outputs per-frame quality that gives detailed insight into the cause of video quality impairments. The VCM model and dataset are open-sourced at https://github.com/microsoft/Video_Call_MOS.

Aashish Gottipati, Sami Khairy, Gabriel Mittag et al.

Real-time video applications require accurate bandwidth estimation (BWE) to maintain user experience across varying network conditions. However, increasing network heterogeneity challenges general-purpose BWE algorithms, necessitating solutions that adapt to end-user environments. While widely adopted, heuristic-based methods are difficult to individualize without extensive domain expertise. Conversely, online reinforcement learning (RL) offers ease of customization but neglects prior domain expertise and suffers from sample inefficiency. Thus, we present Merlin, an imitation learning-based solution that replaces the manual parameter tuning of heuristic-based methods with data-driven updates to streamline end-user personalization. Our key insight is that transforming heuristic-based BWE algorithms into neural networks facilitates data-driven personalization. Merlin utilizes Behavioral Cloning to efficiently learn from offline telemetry logs, capturing heuristic policies without live network interactions. The cloned policy can then be seamlessly tailored to end user network conditions through online finetuning. In real intercontinental videoconferencing calls, Merlin matches our heuristic's policy with no statistically significant differences in user quality of experience (QoE). Finetuning Merlin's control policy to end-user environments enables QoE improvements of up to 7.8% compared to the heuristic policy. Lastly, our IL-based design performs competitively with current state-of-the-art online RL techniques but converges with 80% fewer videoconferencing samples, facilitating practical end-user personalization.

Aashish Gottipati, Sami Khairy, Yasaman Hosseinkashi et al.

Real-time video applications require dynamic bitrate adjustments based on network capacity, necessitating accurate bandwidth estimation (BWE). We introduce Ivy, a novel BWE method that leverages offline meta-learning to combat data drift and maximize user Quality of Experience (QoE). Our approach dynamically selects the most suitable BWE algorithm for current network conditions, enabling effective adaptation to changing environments without requiring live network interactions. We implemented our method in Microsoft Teams and demonstrated that Ivy can enhance QoE by 5.9% to 11.2% over individual BWE algorithms and by 6.3% to 11.4% compared to existing online meta heuristics. Additionally, we show that our method is more data efficient compared to online meta-learning methods, achieving up to 21% improvement in QoE while requiring significantly less training data.

Gabriel Mittag, Babak Naderi, Assmaa Chehadi et al.

In this paper, we present an update to the NISQA speech quality prediction model that is focused on distortions that occur in communication networks. In contrast to the previous version, the model is trained end-to-end and the time-dependency modelling and time-pooling is achieved through a Self-Attention mechanism. Besides overall speech quality, the model also predicts the four speech quality dimensions Noisiness, Coloration, Discontinuity, and Loudness, and in this way gives more insight into the cause of a quality degradation. Furthermore, new datasets with over 13,000 speech files were created for training and validation of the model. The model was finally tested on a new, live-talking test dataset that contains recordings of real telephone calls. Overall, NISQA was trained and evaluated on 81 datasets from different sources and showed to provide reliable predictions also for unknown speech samples. The code, model weights, and datasets are open-sourced.

Gabriel Mittag, Ross Cutler, Yasaman Hosseinkashi et al.

Classic public switched telephone networks (PSTN) are often a black box for VoIP network providers, as they have no access to performance indicators, such as delay or packet loss. Only the degraded output speech signal can be used to monitor the speech quality of these networks. However, the current state-of-the-art speech quality models are not reliable enough to be used for live monitoring. One of the reasons for this is that PSTN distortions can be unique depending on the provider and country, which makes it difficult to train a model that generalizes well for different PSTN networks. In this paper, we present a new open-source PSTN speech quality test set with over 1000 crowdsourced real phone calls. Our proposed no-reference model outperforms the full-reference POLQA and no-reference P.563 on the validation and test set. Further, we analyzed the influence of file cropping on the perceived speech quality and the influence of the number of ratings and training size on the model accuracy.

Sami Khairy, Gabriel Mittag, Vishak Gopal et al.

The quality of experience (QoE) delivered by video conferencing systems is significantly influenced by accurately estimating the time-varying available bandwidth between the sender and receiver. Bandwidth estimation for real-time communications remains an open challenge due to rapidly evolving network architectures, increasingly complex protocol stacks, and the difficulty of defining QoE metrics that reliably improve user experience. In this work, we propose a deployed, human-in-the-loop, data-driven framework for bandwidth estimation to address these challenges. Our approach begins with training objective QoE reward models derived from subjective user evaluations to measure audio and video quality in real-time video conferencing systems. Subsequently, we collect roughly $1$M network traces with objective QoE rewards from real-world Microsoft Teams calls to curate a bandwidth estimation training dataset. We then introduce a novel distributional offline reinforcement learning (RL) algorithm to train a neural-network-based bandwidth estimator aimed at improving QoE for users. Our real-world A/B test demonstrates that the proposed approach reduces the subjective poor call ratio by $11.41\%$ compared to the baseline bandwidth estimator. Furthermore, the proposed offline RL algorithm is benchmarked on D4RL tasks to demonstrate its generalization beyond bandwidth estimation.

Aditya Soni, Mayukh Das, Anjaly Parayil et al.

The difficulty of exploring and training online on real production systems limits the scope of real-time online data/feedback-driven decision making. The most feasible approach is to adopt offline reinforcement learning from limited trajectory samples. However, after deployment, such policies fail due to exogenous factors that temporarily or permanently disturb/alter the transition distribution of the assumed decision process structure induced by offline samples. This results in critical policy failures and generalization errors in sensitive domains like Real-Time Communication (RTC). We solve this crucial problem of identifying robust actions in presence of domain shifts due to unseen exogenous stochastic factors in the wild. As it is impossible to learn generalized offline policies within the support of offline data that are robust to these unseen exogenous disturbances, we propose a novel post-deployment shaping of policies (Streetwise), conditioned on real-time characterization of out-of-distribution sub-spaces. This leads to robust actions in bandwidth estimation (BWE) of network bottlenecks in RTC and in standard benchmarks. Our extensive experimental results on BWE and other standard offline RL benchmark environments demonstrate a significant improvement ($\approx$ 18% on some scenarios) in final returns wrt. end-user metrics over state-of-the-art baselines.

Gabriel Mittags, Sebastian Möller

In this paper, we present a full-reference speech quality prediction model with a deep learning approach. The model determines a feature representation of the reference and the degraded signal through a siamese recurrent convolutional network that shares the weights for both signals as input. The resulting features are then used to align the signals with an attention mechanism and are finally combined to estimate the overall speech quality. The proposed network architecture represents a simple solution for the time-alignment problem that occurs for speech signals transmitted through Voice-Over-IP networks and shows how the clean reference signal can be incorporated into speech quality models that are based on end-to-end trained neural networks.

Gabriel Mittag, Sebastian Möller

In this paper, we present a new objective prediction model for synthetic speech naturalness. It can be used to evaluate Text-To-Speech or Voice Conversion systems and works language independently. The model is trained end-to-end and based on a CNN-LSTM network that previously showed to give good results for speech quality estimation. We trained and tested the model on 16 different datasets, such as from the Blizzard Challenge and the Voice Conversion Challenge. Further, we show that the reliability of deep learning-based naturalness prediction can be improved by transfer learning from speech quality prediction models that are trained on objective POLQA scores. The proposed model is made publicly available and can, for example, be used to evaluate different TTS system configurations.

Gabriel Mittag, Saman Zadtootaghaj, Thilo Michael et al.

The ground truth used for training image, video, or speech quality prediction models is based on the Mean Opinion Scores (MOS) obtained from subjective experiments. Usually, it is necessary to conduct multiple experiments, mostly with different test participants, to obtain enough data to train quality models based on machine learning. Each of these experiments is subject to an experiment-specific bias, where the rating of the same file may be substantially different in two experiments (e.g. depending on the overall quality distribution). These different ratings for the same distortion levels confuse neural networks during training and lead to lower performance. To overcome this problem, we propose a bias-aware loss function that estimates each dataset's biases during training with a linear function and considers it while optimising the network weights. We prove the efficiency of the proposed method by training and validating quality prediction models on synthetic and subjective image and speech quality datasets.

Babak Naderi, Gabriel Mittag, Rafael Zequeira Jim\a'enez et al.

Speech communication systems based on Voice-over-IP technology are frequently used by native as well as non-native speakers of a target language, e.g. in international phone calls or telemeetings. Frequently, such calls also occur in a noisy environment, making noise suppression modules necessary to increase perceived quality of experience. Whereas standard tests for assessing perceived quality make use of native listeners, we assume that noise-reduced speech and residual noise may affect native and non-native listeners of a target language in different ways. To test this assumption, we report results of two subjective tests conducted with English and German native listeners who judge the quality of speech samples recorded by native English, German, and Mandarin speakers, which are degraded with different background noise levels and noise suppression effects. The experiments were conducted following the standardized ITU-T Rec. P.835 approach, however implemented in a crowdsourcing setting according to ITU-T Rec. P.808. Our results show a significant influence of language on speech signal ratings and, consequently, on the overall perceived quality in specific conditions.