We study the problem of stereo singing voice cancellation, a subtask of music source separation, whose goal is to estimate an instrumental background from a stereo mix. We explore how to achieve performance similar to large state-of-the-art source separation networks starting from a small, efficient model for real-time speech separation. Such a model is useful when memory and compute are limited and singing voice processing has to run with limited look-ahead. In practice, this is realised by adapting an existing mono model to handle stereo input. Improvements in quality are obtained by tuning model parameters and expanding the training set. Moreover, we highlight the benefits a stereo model brings by introducing a new metric which detects attenuation inconsistencies between channels. Our approach is evaluated using objective offline metrics and a large-scale MUSHRA trial, confirming the effectiveness of our techniques in stringent listening tests.

WebRTC-based applications are assumed to be based mostly on peer-to-peer communication, where an instance of the application is talking to another instance. However, this is not always the case: a WebRTC-based application communicating with a legacy VoIP device or Media Server like a Video Conference Server is also very common. The ability to make both types of communications (WebRTC to WebRTC and WebRTC to Legacy VoIP) is a differentiating factor for a WebRTC product. This paper makes a proposal on how this could be achieved on the signaling layer by leveraging an industry standard method such as the RESTful Web Services.

We formulate the alignment problem of multiple and partially overlapping audio sequences in a probabilistic framework. We define and compare five generative models for several time varying features extracted from audio clips that are recorded independently and asynchronously. For each model, we derive the associated scoring function that evaluates the quality of an alignment. The matching is then achieved with a sequential algorithm. The derived score functions are also able to identify the cases where the sequences do not overlap and handle multiple sequences where no sequence is covering the entire timeline. The simulation results on real data suggest that the approach is able to handle difficult, ambiguous scenarios and partial matchings where simple baseline methods such as correlation fail.

In our previous work, we formulated multiple audio sequence alignment in a probabilistic framework [1]. Here, we extend the model for multi resolution alignment and focus on pairwise cases. We defined a similarity based approach for binary feature sequences and integrate it into a new generative model. We modify themodel formulti resolution case and the matching is achieved with a SequentialMonte Carlo Sampler (SMCS) which uses low resolution models as bridge distributions. The simulation results on real data sets suggest that our method is very robust and efficient under very noisy conditions with proper choices of model parameters.