Abstract
This paper proposes a novel method for an automated classification of musical instruments based on the analysis of the audio signals generated by them. The paper studies the effectiveness and efficiency of a number of features like Mel frequency cepstral coefficients (MFCC), harmonic pitch class profile (HPCP), linear predictive coding (LPC) coefficients, spectral centroid and pitch salience peaks with cepstral coefficients (CC) with multiple machine learning algorithms like artificial neural network (ANN), K-nearest neighbors (K-NN), support vector machine (SVM), and random forest. The analysis finds that CC surpassed all other features to provide maximum accuracy and robustness along with neural networks. Multiple datasets have been used in the experimentations to remove the possibility of a bias. The overall accuracy obtained ranged between 90 and 93%.
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Chakraborty, S.S., Parekh, R. (2018). Improved Musical Instrument Classification Using Cepstral Coefficients and Neural Networks. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Methodologies and Application Issues of Contemporary Computing Framework. Springer, Singapore. https://doi.org/10.1007/978-981-13-2345-4_10
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DOI: https://doi.org/10.1007/978-981-13-2345-4_10
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