A single predominant instrument recognition of polyphonic music using CNN-based timbre analysis

Abstract

Classifying musical instrument from polyphonic music is a challenging but important task in music information retrieval. This work enables to automatically tag music information, such as genre classification. In previous, almost every work of spectrogram analysis has been used Short Time Fourier Transform (STFT) and Mel Frequency Cepstral Coefficient (MFCC). Recently, sparkgram is researched and used in audio source analysis. Moreover, for deep learning approach, modified convolutional neural networks (CNN) widely have been researched, but many results have not been improved drastically. Instead of improving backbone networks, we have researched on preprocessing process. In this paper, we use CNN and Hilbert Spectral Analysis (HSA) to solve the polyphonic music problem. The HSA is performed at the fixed length of polyphonic music, and a predominant instrument is labeled at its result. As result, we have achieved the state-of-the-art result in IRMAS dataset and 3% performance improvement in individual instruments.