Towards Music Instrument Classification Using Convolutional Neural Networks

Recognizing musical instruments from an audio signal is a challenging yet valuable endeavor within the realm of music study. The recognition and classification of musical instruments could prove beneficial in organizing various genres of music and automation in music transcription reading and producing. In this paper, we will investigate the use of a Deep Convolutional Neural Network for automatic instruments recognition of polyphonic music. We enhance the state-of-the-art model, which we establish to be responsive to the instrument's playing style rather than its timbre. Furthermore, we set up experimental validation on small networks to extract timbre features from a spectrogram. We demonstrate an ensemble model based on these experiments, which improves the model accuracy by 20% for both single and multiple instrument recognition. Additionally, we present several models capable of achieving competitive performance with a significantly smaller number of network parameters and neurons.