MusicNet: Compact Convolutional Neural Network for Real-time Background Music Detection
Chandan K. A. Reddy, Vishak Gopal, Harishchandra Dubey, Ross Cutler, Sergiy Matusevych, Robert Aichner · Interspeech 2022 · 2022
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 [1] 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.