  With the recent growth of remote work, online meetings often encounter
challenging audio contexts such as background noise, music, and echo. Accurate
real-time detection of music events can help to improve the user experience. In
this paper, we present MusicNet, a compact neural model for detecting
background music in the real-time communications pipeline. In video meetings,
music frequently co-occurs with speech and background noises, making the
accurate classification quite challenging. We propose a compact convolutional
neural network core preceded by an in-model featurization layer. MusicNet takes
9 seconds of raw audio as input and does not require any model-specific
featurization in the product stack. We train our model on the balanced subset
of the Audio Set~\cite{gemmeke2017audio} data and validate it on 1000
crowd-sourced real test clips. Finally, we compare MusicNet performance with 20
state-of-the-art models. MusicNet has a true positive rate (TPR) of 81.3% at a
0.1% false positive rate (FPR), which is significantly better than
state-of-the-art models included in our study. MusicNet is also 10x smaller and
has 4x faster inference than the best performing models we benchmarked.
