Stereo Singing Voice Cancellation: Small Model, Big Performance

The Problem of Stereo Singing Voice Cancellation in AI

Stereo singing voice cancellation is a subtask of music source separation, with the goal of estimating instrumental background from a stereo mix. Achieving performance similar to large state-of-the-art source separation networks starting from a small, efficient model for real-time speech separation is important when memory and compute resources are limited. In practice, existing mono models can be adapted to handle stereo input, with improvements in quality achieved through tuning model parameters and expanding the training set.

The Benefits of a Stereo Model in AI

Highlighting the benefits a stereo model brings by introducing a new metric which detects attenuation inconsistencies between channels is important. This approach is evaluated using objective offline metrics and a large-scale MUSHRA trial, confirming the effectiveness of the techniques in stringent listening tests. AI is advancing rapidly, and these insights into stereo singing voice cancellation are important for anyone working in the field.

Implementing Stereo Singing Voice Cancellation in AI

Implementing this technique involves adapting an existing mono model to handle stereo input, making improvements in quality through tuning model parameters and expanding the training set, and evaluating the effectiveness of the technique using offline metrics and large-scale MUSHRA trials.

Overall, stereo singing voice cancellation is an important subtask in music source separation, with implications for AI and advanced technology.

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