MIC_FuzzyNET: Fuzzy Integral Based Ensemble for Automatic Classification of Musical Instruments From Audio Signals

Abstract

Music has been an integral part of the history of humankind with theories suggesting it is more antediluvian than speech itself. Music is an ordered succession of tones and harmonies that produce sounds characterized by melody and rhythm. Our paper proposes an ensemble deep learning musical instrument classification (MIC) framework, named as MIC_FuzzyNET model which aims to classify the dominant instruments present in musical clips. Firstly, the musical data is converted to three different spectrograms: Constant Q-Transform, Semitone Spectrogram, and Mel Spectrogram, which are then stacked to form 3 channel 2D data. This stacked spectrogram is fed to transfer learning models namely, EfficientNetV2 and ResNet18 which output the preliminary classification scores. A fuzzy rank ensemble model is finally employed that assigns the classifier ranks, on the testing data to achieve final enhanced classification scores which reduces error and biases for the constituent CNN architectures. Our proposed framework has been evaluated on the Persian Classical Music Instrument Recognition (PCMIR) dataset and Instrument Recognition in Musical Audio Signals (IRMAS) dataset. It has achieved considerably high accuracy, making our proposed framework a robust MIC model.

Keywords

• Musical instrument classification
• MIC_FuzzyNET
• fuzzy integral
• spectrogram
• transfer learning
• PCMIR dataset